Autonomous processing reactor system and method

Abstract

A system and method for processing of organic waste, the system comprising: a converter reactor (such as having pyrolytic characteristics); an input waste feed material identification subsystem; one or more energy generators; one or more sensors for acquiring at least processing data; a communication bus for remote monitoring and control of the system; and artificial intelligence agents for making localised decisions (and optionally remote decisions) The system is configured to autonomously select or control the processing according to at least the identity of the waste feed material, and the system is configured to be controlled according to at least the processing data. The system may be equipped with or configured to be controlled according to smart contracts within blockchain digital ledgers that may be connected to a carbon trading exchange.

Description

[0001] Autonomous Processing Reactor System and Method

[0002] RELATED APPLICATION

[0003] This application claims the benefit of the filing and priority dates of Australian patent application no. 2023903363 filed 20 October 2023, the content of which is incorporated herein by reference in its entireties.

[0004] FIELD OFTHE INVENTION

[0005] The present invention relates to systems and methods for autonomously detecting input waste feedstock types, configuring internal process, processing organic waste, applying economic and environmental business rules, collecting and controlling process data, establishing blockchain based digital ledgers, creating smart digital contracts, generating carbon credits, connecting to carbon trading exchanges and automatically completing contractual transactions. The system may be, in particlaur, a modular autonomous processing reactor system.

[0006] BACKGROUND

[0007] Oil, char and gas can be produced using reactor based processing methods. Specific controlled processes, such as graphitization, are essential to tailoring the properties of carbon based material. Additionally, the Supervisory Control and Data Acquisition (SCADA) is a well-known system used in monitoring and control. Such methods are described in detail in various existing publications.

[0008] Governments around the world seek an economical balance between expenditure, public health benefit and waste handling when applied to Municipal Solid Waste (MSW), industrial waste, agricultural waste, sanitation and sewage sludge processing.

[0009] With global urbanisation growth and higher income levels MSW generation per capita has grown to alarming levels. MSW generation per capita is strongly driven by income. There are numerous expert reports and publications documenting the strong, linear correlation between population growth and MSW generation.

[0010] Combining the urban MSW generation per capita projections and the United Nations (UN) urban population projections produces estimates for the total quantities of MSW. Actual figures for each region is presented for 2013 and for 2050 projections.

[0011] Source: OECD and United Nations World Urbanisation Prospects (2014 revision)

[0012] The doubling in urban population correlates with more than double urban MSW generation. Thus for every 20-30-year period, the UNN and World Bank Group expect the global MSW generation to double (www.UN.org (UNEP) and Word Bank Group - Urban development series - knowledge pages - Annex J, page 83).

[0013] Living with large waste dumps is a global daily reality with no scientific or mathematical extrapolations required.

[0014] The Asian Development Bank (ADB) reported in its ‘Metro Manila Experience’ that the growing, uncontrolled problem with Municipal Solid Waste (MSW) dumps is that: “. ..it is a problem out of sight and out of mind. An estimated 150,000 residents of Metro Manila know the sight and smell of garbage as an integral part of their daily lives.”

[0015] As a consequence, waste disposal often disproportionately affects marginalised communities. Low- income areas and developing countries are more likely to bear the burden of imp roper waste disposal practices, leading to environmental injustice. Large waste dumps are also becoming a part of life, with some children referringto the dumpsite (or landfill) as ‘home’.

[0016] Even so-called ‘safe’ waste-disposal has problems:

[0017] • If land-filled, the waste contaminates the groundwater.

[0018] • If incinerated, the waste causes serious air pollution.

[0019] • If dumped in the ocean, the waste can harm the marine ecology.

[0020] • If used as land application is the most insidiously dangerous of all.

[0021] Generational neglect and lack of focused intent has created an approaching crisis.

[0022] The United Nation Environmental Programme Division (UNEP DTIE IETC) reported in March 2010 that Municipal Solid Waste (MSW) “...has created an increasingseverity of impacts:

[0023] • air pollution from emissions of spontaneous combustion in dumps.

[0024] • adverse impacts on fauna & flora

[0025] • severe health impacts on community in vicinity of dumpsites • pollution of surface and sub-surface water bodies dueto leachate contamination.”

[0026] SUMMARYOFTHE INVENTION

[0027] The present invention addresses the limitations, restrictive use and numerous problems of existing waste processing systems, reactor based processing plants, SCADA control systems and large data collection processes.

[0028] Accordingto a first aspect of the present invention, there is provided a system for processing (such as decentralised processing) of organic waste, the system comprising: a converter reactor; an input waste feed material identification subsystem; one or more energy generators; one or more sensors (whether internal and / or external) for acquiring at least processing data; a communication bus (such as communication bus of a communication system) for remote monitoring and control of the system; and artificial intelligence agents for making localised decisions (and optionally also remote decisions); wherein the system is configured to autonomously (such as decided by the artificial intelligence agents) select or control the processing according to at least the identity waste feed material, and the system is configured to be controlled accordingto at least the processing data.

[0029] In various embodiments, the system is any one or more of:

[0030] • Transportable to position the processing plant where the waste is situated

[0031] • Modular to provide unlimited scalability

[0032] • Flexible to processes a multitude ofvarious types of organic waste feedstock

[0033] • Efficient to produce significant resource recovery from organic waste feedstock

[0034] • Systematic for no environmental emission generation

[0035] • Adaptable to generate energy from organic waste feedstock

[0036] • Capable to reduce operational costs

[0037] • Scalable to maximise economic returns

[0038] • Natural extraction to retain the ‘goodness’ of recovered resources

[0039] Preferred and / or optional features of any aspect may be applied to any other aspect in any combination or sub-combi nation, unless the context demands otherwise. For example:

[0040] In an embodiment, the system comprises an interface (such as a smart city interface) for receiving commodity requirements and / or market demands, wherein the system is configured to autonomously select or control the processing according to commodity requirements and / or market demands. For example, the system may comprise or configured to be controlled according to smart contracts within blockchain digital ledgers; such smart contracts may be connected to or valid with a carbon trading exchange.

[0041] In an embodiment, the system is configured to function as an energy aggregator.

[0042] The system may be configured to perform energy conversion and / or include energy storage.

[0043] In an embodiment, the converter reactor is a pyrolytic reactor.

[0044] In an embodiment, the system is modularand / or portable.

[0045] In an embodiment, the system is housed or housable in one or more standard standard shipping containers, such as one or more standard 12.2 m (40 feet) shipping containers. In some examples, the system includes the one or more standard standard shipping containers.

[0046] In an embodiment, the system comprises one or more bio-gas driven turbines, wherein the system is at least partially self-powered thereby.

[0047] In an embodiment, the system comprises master management and control module for managing and controlling the system (either autonomously or under user control).

[0048] In an embodiment, the system comprises an in put waste feed material recognition module.

[0049] In an embodiment, the system comprises process matrix control and configuration management and setup.

[0050] In an embodiment, the system is configured to be connected to the Internet of Things (IOT), such that one or more processes implemented by the system are controllable and / or monitorable via the internet.

[0051] In an embodiment, the system comprises global remote monitoring and control functionality.

[0052] In an embodiment, the system comprises any one or more of a Localised Artificial Intelligence, a Decentralized Artificial Intelligence, and process-based Artificial Intelligence.

[0053] In an embodiment, the system is configured to generate electricity from one or more outputs of the processing.

[0054] In an embodiment, the system is configured to produce hydrogen with an external electrolyser and / or graphite from Bio-Char output and / or graphene from graphite output and / or Biodiesel or Green Diesel from Bio-Oil output.

[0055] In an embodiment, the system comprises a High Speed Quantum Nodes based network.

[0056] In an embodiment, the system comprises a random or pseudo-random number generator. In one example, the system is configured to create and employ highly secure Digital Cryptographic Keys using random or pseudo-random numbers generated by the random or pseudo-random number generator.

[0057] In an embodiment, the system is configured to generate carbon credits pursuant to sequestration of carbon products outputted by the system.

[0058] In an embodiment, the system is configured for integration into a realtime ‘spot’ or other carbon credits trading exchange.

[0059] In an embodiment, the system is configured to support a Carbon Credits Redemption Loyalty Program.

[0060] In an embodiment, the system comprises a plurality of converter reactors couples in series or in parallel.

[0061] In a second aspect, the invention provides a system for pyrolytic processing of organic waste feedstock, comprising the system of the first aspect.

[0062] In a third aspect, the invention provides a method for processing (such as pyrolytically) organic waste feedstock, comprising: providing organic waste feedstock to the system of the first aspect; and pyrolytically processing the organic waste feedstock with the system of the first aspect.

[0063] In each of these aspects, data may be collected from a plurality of loT sensors.

[0064] Each aspect may employ machine learning algorithms to adjust process parameters.

[0065] Each aspect may verify system output’s conformity to quality standards.

[0066] The present invention may thus, in various embodiments, employ or provide: i. a system approach to a standardised physical global plant configuration where the characteristics and functionality of the plant is established via data lookup tables in a remote central control centre, as an example. ii. rules, based on real time commercially driven and environmental factors dictate pyrolysis plant configuration, as an example. iii. remote unmanned pyrolysis plants, as an example, based on Artificial Intelligence and data lookup tables, in a central repository that is updated in real time from data collected from all other global plants. iv. a self-contained transportable pyrolysis plant taken to anywhere in the world to where the feedstock is available and then redirect to new locations as the need arises. v. a Pico-Pyrolysis localised domestic sewage and waste processing plant that not only locally processes waste and sewage but also creates renewable green energy and recyclable water for domestic use. vi. ad hoc indirect wireless neural communication paths (IWNCP) to enhance the direct communication paths of transportable pyrolysis plants for data integrity and disaster recovery conditions, and enable seamless communication between neural interfaces through a network infrastructure comprising of satellite links, wireless towers, and the internet, facilitated by an overlay network of wireless ready waste processing plants. vii. processing units that are easy to integrate and use a local host pyrolysis plant as a communication hub and as a main concentrator. viii. real time carbon credit exchange connectivity allowing spot carbon trading, which internally embeds real-time ‘spot’ carbon tradingfunctionality and incorporates carbon certificates, Blockchain and Smart Contract generation and interfacing directly to Carbon Trading Exchanges. ix. a physical pyrolysis processing plant configuration that is standardised for global installation where the local input feedstock variances are calculated within the central ‘Manufacturing Plant Matrix’ algorithm and downloaded as configuration specifications from the central control system as local feedstock is identified with local conditions. x. a common global build for transportable pyrolysis units to standardize the manufacturing process of the physical plants but also allow a standard methodology to be registered with Global Carbon Credit Certifying Authorities— so that pyrolysis plant output is standards based and hence can be reported in real time to carbon credit exchanges for sale. xi. a rural pyrolysis plant, using local industrial / farming waste and / or treated sewage (human and / or animal) as input feedstock and producing Bio-electricity to supplement power required (additional power provided by solar and / or wind process) by a remote / rural Data Centre and / ordirectlyfeedingthe BioElectricity into the electricity grid. The invention provides a unique energy distribution system for remote regions. With the availability of real time ongoing energy generation comes the ability to provide Remote Data Centres, Remote Laboratories and Remote Data Collection Systems. xii. a pyrolytic cleaning system that offers three stages (as an example) for convenient operator cleaning, noting that the dirtier the pyrolytic reactor, the higher the cleaning level is required; an auto detector and alarm function may also incorporated into the embodiments© that once the cleaning is detected. xiii. a heart beat encrypted control system; the central remote realtime monitoring system has location data of the processing unit. When the central monitoring system receives a signal from the remote plant that it needs to start operations a start sequence is commenced for authenticate (user, plant, location, access authority etc). A first heart beat is sent to the plant which activates a known part ofthe system. At the end of this sequence the plant sends a completion code. The central system uses this derived code with a new central code to initiate a second heart heat back to the remote plant. The process is implanted deep into an embedded subsystem where it is extremely difficult to break into without destroying the code itself. This provide system security at the physical level and at the code level. If the plant fails to reach the end of the authentication sequence the plant is shut down and investigated. This method allows the placement of unmanned units at remote locations and still allowing a central control centre to authenticate and activate. xiv. image detection and image recognition. xv. environmental data and samples collection by the PRP. xvi. digital signage. xvii. machine learning processes. xviii. supplementary energy to power additional peripheral pyrolysis equipment as solar and wind energy power generation. xix. a multi-functional input preparation system that is flexible to cater for pyrolysis process preparation fora multitude and varying input feedstock, with integrated process functionality that includes heaters, press dryers, crushers or shredders, which configures the equipment dependent on the type of input feedstock, from wet sewage sludge, biomass, tyres, blood and bone or municipal solid waste, and the preparative work that is required to make it suitable for input into the pyrolysis chamber. xx. collectively aggregates all data that is available from all the pyrolysis plants, globally, and apply data mining principles to establish new processing conditions based on, but not limited to, location, environment, input feedstock, output quality and deliverable quantity, and a methodology based thereon for numerically studying, amongst other things, the influence of input and Pyrolytic operating parameters on the stable process and the quality of the output product. xxi. Blockchain technology and smart contract transactions to interface directly to carbon trading exchanges.

[0067] The invention thus addresses mobility, usability and utility.

[0068] In one organic waste processing plant embodiment, the system is location aware with GPS coordinates, self-contained and inclusive of all essential operational requirements, self-powered by internally generated energy, autonomously operated with preprogramed logical process control, produces minimal emissions by mimicking natural processes, has a small footprint with encapsulating the equipment into a standard shipping container, is transportable and operates at a relatively low temperature to retain the natural goodness remaining within the organic waste. It provides a local community with the apparatus to process their generated organic waste as feed material and to further benefit from the processed output of commodity based products. The commodity products can subsequently be converted into electricity to energise the electricity grid, recharge batteries, used for additional conversion processes or directly power devices.

[0069] Compared to existing waste processing plants, embodiments of the invention can be more efficient, lower in cost, self-powered, environmentally friendly, blockchain capable, digital smart contract enabled, carbon trading connectivity, monitored from a remote control centre and has unlimited scalability potential.

[0070] Embodiments may, for example, use molecular re-arrangement by heating, achieved by a process of low temperature pyrolysis (to about 550 °C) plus intimate contacting of the product streams in the main conversion reactor to obtain the optimum quality of output by-products. For example, waste product molecules may be heated in an oxygen free environment (no combustion), and re-ordered into bio-gas and bio-char, with bio-gas condensed to produce a bio-oil by-product. All of these commodities can be used to generate electricity, and the system may be configured to aggregate plural energy outputs.

[0071] BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order that the invention may be more fully ascertained, embodiments invention will now be described, by way of example, with reference to the drawings, in which:

[0073] Figs. 1A& IB provide a schematic representation of a Multi Modal Autonomous Reactor with a Processing Reactor Plant (PRP) for processing input waste feed material according to an embodiment of the present invention.

[0074] Figs. 2A& 2B provide a schematic representation of a Processing Reactor Plant (PRP) according to an embodiment of the present invention.

[0075] Figs. 2C & 2D provide a schematic representation of a Process Matrix Control (PMC), according to an embodiment of the present invention, of the Processing Reactor Plant (PRP) of Figs. 2A& 2B.

[0076] Figs. 3A, 3B & 3C provide a schematic representation of the substructures, interrelationship and associations of the Process Matrix Control (PMC) of Figs. 2C& 2D, according to an embodiment of the present invention.

[0077] Fig. 4 is a schematic representation of the Master Multi-Path Matrix Manager (MMM) of the Processing Reactor Plant (PRP) of Figs. 2A& 2B, according to an embodiment of the present invention.

[0078] Figs. 5A&5B provide a schematic representation of the input feedstock image recognition system of the Processing Reactor Plant (PRP) of Figs. 2A& 2B, accordingto an embodiment of the present invention.

[0079] Figs. 6A& 6B provide a schematic representation of communication and control functionality between the Remote Monitoring and Control Centre (RMCC) (item 43) and the Processing Reactor Plant (PRP) of Figs. 2A& 2B, accordingto an embodiment of the present invention.

[0080] Figs. 7A& 7B provide a schematic representation of the interaction between sensors, control functions and the data within the Processing Reactor Plant (PRP) of Figs. 2A & 2B, accordingto an embodiment of the present invention. Figs. 8A& 8B provide a schematic representation of the security, authentication processing and heartbeat activation process within the Processing Reactor Plant (PRP) Plant (PRP) of Figs. 2A& 2B, according to an embodiment of the present invention.

[0081] Fig. 9 is a schematic flow diagram of the security, authentication code generation and the heartbeat activation processes of Figs. 8A& 8B, accordingto an embodiment of the present invention.

[0082] Figs. 10A& 10B provide a schematic representation of the Real Time Spot Carbon Trading Exchange (CTE), the Remote Monitoring and Control Centre (RMCC) and the Compliance and Certification Authority (CCA) of the Multi Modal Autonomous Reactor of Figs. 1A& IB, and their relationship with the Processing Reactor Plant (PRP) of Figs. 2A& 2B, accordingto an embodiment of the present invention.

[0083] Figs. 11A& 11B provide a schematic representation of the internal components of multiple standalone, continuous operation, transportable Process Reactor Plants (PRP) of Figs. 2A& 2B, accordin to an embodiment of the present invention.

[0084] Fig. 12 is a schematic representation of an integrated, continuous operation, autonomous transportable Multi Modal Processing Reactor Plant (MMPRP) that includes the Processing Reactor Plants (PRP) of Figs. 2A& 2B, accordingto an embodiment of the present invention.

[0085] Fig. 13 is a schematic representation of the multi-path communication process between continuous operation Processing Reactor Plants (PRP) of Figs. 2A& 2B, accordingto an embodiment of the present invention.

[0086] Fig. 14 is a schematic representation of the method and process of the integrated, continuous operation, condensed and very small size Micro Processing Reactor Plant (MPRP) accordingto an embodiment of the present invention.

[0087] Fig. 15 is a schematic configuration representation of the integrated, continuous operation, condensed and very small size Micro Processing Reactor Plant (MPRP) accordingto an embodiment of the present invention.

[0088] Fig. 16 is a schematic configuration representation of the integrated, continuous operation Converter Reactor Unit (CRU) as a single reactor configuration, accordingto an embodiment of the present invention.

[0089] Fig. 17 is a schematic representation of an integrated, continuous operation Converter Reactor Unit (CRU) in a dual reactor configuration with liquefied fertiliser injectors, accordingto an embodiment of the present invention.

[0090] Fig. 18 is a schematic representation of an integrated, continuous operation Converter Reactor Unit (CRU) in a dual reactor configuration with liquefied fertiliser injectors and sterilizer, accordingto an embodiment of the present invention.

[0091] Fig. 19 is a schematic representation of an integrated, continuous operation Converter Reactor Unit (CRU) in a dual reactor configuration with liquefied fertiliser injectors, sterilizer and organic rankine cycle (ORC) generator units, according to an embodiment of the present invention.

[0092] Fig. 20 is a schematic representation of an integrated, continuous operation Converter Reactor Unit (CRU) in a single reactor configuration with dual outputs, according to an embodiment of the present invention.

[0093] Fig. 21 is a schematic representation of a distributed knowledge base of the system of Figs. 1A& IB, according to an embodiment of the present invention.

[0094] Fig. 22 is schematic view of a core module for processing organic waste according to yet another embodiment of the present invention.

[0095] Fig. 23 is schematic view of core module for processing organic waste with a preparation module according to an embodiment of the present invention.

[0096] Fig. 24 is schematic view of a core module for processingorganic waste according to an embodiment of the present invention accommodated within a standard ‘10-foot’ (3.048 m) shipping container.

[0097] Fig. 25 is schematic view of first and second core modules for processing organic waste configured in series according to an embodiment of the present invention within a standard ‘20-foot’ (6.096 m) shipping container.

[0098] Fig. 26 is schematic view of first to fourth core modules for processing organic waste configured in series according to an embodiment of the present invention within a standard ‘40-foot’ (12.192 m) shipping container.

[0099] Fig. 27 is schematic view of first, second and third core modules for processing organic waste accordin to an embodiment of the present invention accommodated within a standard ‘20-foot’ (6.096 m) shipping container.

[0100] Fig. 28 is schematic view of first, second and third core modules for processing organic waste accordingto an embodiment of the present invention accommodated within a standard ‘20-foot’ (6.096 m) shipping container.

[0101] Fig. 29 is schematic view of eight core modules for processing organic waste arranged in two stacks accordingto an embodiment of the present invention accommodated within a standard ‘20-foot’ (6.096 m) shipping container. Fig. 30 is schematic view of 12 core modules for processing organic waste arranged in three stacks according to an embodiment of the present invention accommodated within a standard ‘40-foot’ (12.192 m) shipping container.

[0102] Fig. 31 is schematic view of a core module with central loading for processing organic waste with central loading according to another embodiment of the present invention.

[0103] Fig. 32 is a plan schematic view of vertical stack of core modules of the embodiment of Fig. 31 according to another embodiment of the present invention.

[0104] It should be noted that, in the following description and in the drawing, figures split over plural drawing sheets (e.g. Figs. 1A& 1B) may be identified by reference to their common numeral (e.g. Fig. 1).

[0105] DETAILED DESCRIPTION ntroduction & Overview

[0106] Fig. 1 is a schematic representation of a Multi Modal Autonomous Reactorwith a Processing Reactor Plant (PRP) accordingto an embodiment of the present invention, configured to process input waste feed material utilising Conversion Reactors within a Process Matrix Control method, Internet ofThings (loT) sensors monitored and controlled by local Artificial Intelligence, the production of commodities, energy conversion, product conversion from electricity, data storage and processing, Smart Contracts using Blockchain to interface to Certified Carbon Partners and Real-Time Carbon Credits T rading Exchange, Quantum Node Network connectivity, Smart City integration, remote monitoring and control as an example but not limited to, for use in performing the process accordingto the present invention. It will be noted that various components of Fig. 1 refer to the figure or figures that further elucidate the characteristics of the respective components.

[0107] Figs. 2A& 2B provide a schematic representation of a Processing Reactor Plant (PRP) 31 of the Multi Modal Autonomous Reactorwith a Processing Reactor Plant (PRP) of Fig. 1, accordin to an embodiment of the present invention. PRP 31 includes a Process Matrix Control (PMC) 21, Local Off-line Access Control (LOA) 22, Security Authentication and Heartbeat (SAH) 23, Master Multi-Path Matrix Manager (MMM) 24, Plant Communication and Control (PCC) 25, Multi-Path Communication and Control (MCC) 26, Remote Monitoring and Control Centre (RMCC) 43 utilising Administration Management and Control (AMC) 32 integrating a Master Artificial Intelligence Engine (MAIE) 37, Blockchain with Smart Contract process (item 69), Carbon Trading Exchange (CTE) 45 and connected by a Quantum Node Network 43Q.

[0108] Figs. 2C & 2D provide a schematic representation of the Process Matrix Control (PMC) 21, detai ling the various management processes within a typical transportable Processing Reactor Plant (PRP) 31. Also shown is the relationship of the PRP to the Remote Monitoring and Control Centre (RMCC) 43, the Carbon Trading Exchange 45, the relationship with the market (buyers) 44, the Master Multi-Path Matrix Manager (MMM) 24 that interfaces with the Security Authentication and Heartbeat (SAH) 23, SCADA Control 67, Plant Communication and Control (PCC) 25, Multi-Path Communications Control (MCC) 26.

[0109] Fig. 3 is a schematic representation of the substructures, interrelationship and association to the Process Matrix Control (PMC) 21 within each sector relating to Input 1, Arrangement 2, Conversion 3, Output 4 and Energy 5. Each ofthe identified controllers, the Input Matrix Control (IMC) 7, the Arrange Matrix Control (AMC) 10, the Convert Matrix Control (CMC) 13, the Output Matrix Control (OMC) 16 and the Energy Matrix Control (EMC) 19 is managed by the Master Multi-Path Matrix Manager (MMM) 24.

[0110] Fig. 4 is a schematic representation ofthe Master Multi-Path Matrix Manager (MMM) 24 and the association within each major function. Both the Energy Synchronisation and Control Manager (ESM) 108 and the Authentication and Security Manager (ASM) 117 collaborate with the Interface Tasks Manager (ITM) 109, Authentication Control Manager (ACM) 110, Process Tasks Manager (PTM) 111 and the Security Control Manager (SCM) 116 to ensure that the Input Feedstock Recognition Manager (IRM) 112 and the Process Data Manager (PDM) are in control and operate the Unit Cell Data 114.

[0111] Fig. 5 is a schematic representation ofthe input feedstock image recognition system internal to a Processing Reactor Plant (PRP) (item 31). Within the Arrange structure (item 2) is the Input Feedstock Imaging and Identification Manager (IFI) (item 50) that interacts with the Arrange Matrix Control (AMC) (item 10), new received samples (item 121), previously stored images data (item 118) and local Artificial Intelligence Engine (A) (item 11). Match and identification process (item 119) is carried out with subsequent internal communications to make ready the plant reaction chamber (item 126) is processed via the Plant Communication and Control (PCC) (item 25). All external communications are processed through the Remote Monitoring and Control (RMCC) (item 43).

[0112] Fig. 6 is a schematic representation of communication and control functions between the Remote Monitoring and Control Centre (RMCC) (item 43) and the Processing Reactor Plant (PRP) (item 31). The Master Multi-Path Matrix Manager (MMM) (item 24) interfaces with the Process Matrix Control (PMC), Plant Communication and Control (PCC), Security (SAH) (item 23), the Independent Wireless Network Node (IWNN) (item 176), Remote Networks (item 10) and the local operator (item 139) via face, iris, finger or other biometrics, smart cards or security tokens.

[0113] Fig. 7 is a schematic representation ofthe interaction between sensors, control functions and the data within the Processing Reactor Plant (PRP) 31. The Master Multi-Path Matrix Manager (MMM) 24 incorporates the Unit Cell Data (with Data Origination) 114 and the Process Data Manager (PDM) 115, and the Process Tasks Manager (PTM) 111 that features the Data Flow Process Manager 203 via the Plant Communication and Control (PCC) functionality. The generated origination data is managed by the Control Data Administration (CDA) 192 and each sensor data 184, 186 is locally controlled by individual sensor controllers 188, 190 to ensure process output is consistent and based on the required specifications. The Process Data Manager (PDM) 115 includes the Data Flow Engine (DFE) 199 that incorporates the Data Analysis Manager (DAM) 200 that evaluates all inputs such as Trading Data, Market Data, Historical Trends etc. 133 with relevant and targeted data 201 to create filtered local data 202.

[0114] Fig. 8 is a schematic representation of the security, authentication processing and heartbeat activation process within the Processing Reactor Plant (PRP) 31. The Security Access Request and Control (SARC) 208 manages all the functionality relatingto the Heartbeat Start (HBS) code 210 to the Heartbeat Sequence Control (HSC) 221 to the Unit / Sub-System Codes 225 into the Bucket Code 226 then into the Sequence Interchange Code 227 generating the codes 228 required forthe Heartbeat Response Code (HRC) 229. The generated Security Response Code (SRC) 216 coupled with the Manufactures Unique Code 217 produces the Unique Reactor Authentication Code (URA) 218 to create the Start Condition 224 or the Alarm Condition 223.

[0115] Fig. 9 is a schematic flow process representation of the security, authentication code generation process and the heartbeat activation process that is to be read in conjunction with Fig. 8. Security, Authentication Processing and Heartbeat Activation Process. The local operator 235 creates Task A 238 to the Administration Management and Control (AMC) 32 which then interacts with the Authentication & Authorisation Application Engine (AAE) 231 to the Master Artificial Intelligence Engine (MAIE) 37 that responds back to the AMC 32 which in turn responds to local operator 235, and thus communication is established. At Task G 244 the AMC 32 communicates with the Processing Reactor Plant (PRP) 31 via the Security Access Request and Control (SARC) 208 which in turn sends a code ready to Heartbeat Start (HBS) 210 and creates Sub-System Codes (SSC) 211 for Unit Sub-systems 225. The same sequence is repeated for Bucket-Brigade Coding (BBC) 212 for Buckets 226 and Sequence Interchange Control (SIC) 213 for interchanges 227. All correct codes are directed to the Unique Reactor Authentication Code (URA) 218 and following validation are forwarded back to the SARC 208 that responds back to AMC 32 at task L 249. The AMC 32 now has authenticated code to proceed to AAE 231 and then onto MAIE 37 to confirm from the Rules and Facts Database 41 and Security Code Database 205 back to AMC 32 at task M 250. The AMC 32 now has access to connect with Business Processing Algorithms (BPA) 233 and confirm all business transactions are in order at task N 251. Once all business transactions are in order the AMC (item 32) connects to the SARC 208 and to Heartbeat Sequence Control (HSC) 219 that reports a Pass condition 222 to Start 224 the Processing Reactor Plant (PRP) 31 ora Fail condition 220 to create an Alarm response 223 to the operator 225.

[0116] Fig. 10 is a schematic representation of the Real Time Spot Carbon Trading Exchange (CTE) 45, the Remote Monitoring and Control Centre (RMCC) 43, the Compliance and Certification Authority (CCA) 257 and the relationship with the Processing Reactor Plant (PRP) 31 as it relates to the Output Commodity Product 70 for the buyer marketplace 44. The Administration Management and Control (AMC) manages the Carbon TradingAlgorithms (CTA) 256 and via the Applications Management 35 controls the Carbon Audit and Trading Engine (CATE) 255. The Master Artificial Intelligence Engine (MAIE) 37 manages the actions, activities and undertakings forthe Blockchain and Smart Contracts 69. Fig. 11 is a schematic representation of the internals of a standalone, continuous operation, transportable Process Reactor Plant (PRP) 291 with the Converter Reactors and associated preparation process 2, external communications equipment 25, energy generation from internal capability, storage capability for external generation and control functionality 5. Fuel Cell capability 104 is presented in addition to the associated Energy Distribution Process 287. Product Conversion capabilities 287C shows the available production of hydrogen, graphite and graphene. The energy generated by the PRP 291 is available as Direct Energy Storage 294, distributed to eitherthe power grid 288, to external connections 273 orto other transportable units such as Transportable Data Hosting Centre (DHC) 275, Transportable Data Collector Centre (DCC) 278, Transportable Unified Communication Centre (UCC) 281, Transportable Scientific Laboratory Centre (SLC) 284 and a T ransportable Hydroponic Centre (THC) 275H.

[0117] Fig. 12 is a schematic representation of an integrated, continuous operation, autonomous transportable Multi Modal Processing Reactor Plant (MMPRP) 291 that incorporates a Process Reactor Plant (PRP) 31, additional functional units such as Data Collector Unit (DCU) 305, Scientific Laboratory Unit (SLU) 307, Data Hosting Unit (DHU) 309, Unified Communication Unit (UCU) 311 sensors and instrumentation 301, environmental data equipment300 and otherspecialised devices 295, 296, 297, 298, 299 as required. Connectivity via the communication transmitter and receiver 290 and the external energy generation devices via solar, wind, orany other energy generational means 76, supplement the internal energy generated by the Converter Reactor 3 from input waste feedstock 254.

[0118] Fig. 13 is a schematic representation of the multi-path communication process between continuous operation Processing Reactor Plants (PRP) 31 located locally via wireless communication towers321, stationary Micro Processing Reactor Plants 386, as transportable units between each other 338, globally via satellites 313 via online internet based services 319 and connection to the Remote Monitoring and Control Centre (RMCC) 43. The ‘ad hoc’ network 550 is self-organised rather than planned or pre-arranged in any configuration. The communication ad hoc network 550 is autonomously established with and between each of the Processing Reactor Plants (PRP) 31A, 31B, 31C, 31D and any existing wireless communication equipment 315, 313, 321, 319. Emergency vehicles 331, 332, 336 can connect to the ad hoc network 550; local devices 700, 703 can communicate and Digital Signage Systems (DSS) 317 can also connect and receive digital content to display on their video panels. The bidirectional ad hoc network 550 can receive and transmit data, voice and video content from any connected device.

[0119] Fig. 14 is a schematic representation of the method and process of the integrated, continuous operation, condensed and very small size Micro Processing Reactor Plant (MPRP) 386 and associated components for onsite domestic, commercial or industrial premises installations. The Premises Management and Control (PMC) (item 339) interconnects with online internet services 385 to the Remote Monitoring and Control Centre (RMCC) 43 which includes Blockchain and Smart Contract interface 69 and connects directly to the Carbon Audit and Trading Engine (MRE) 255 for real time carbon emissions trading. The organic input waste feedstock 347 is generated from various means such as any Bio-waste 355 or treated localised sewage sludge 351 and is monitored 349 by the Gateway Administration and Control (GAM) 360 for any medical 365 or other general services 366 as may be required by the resident or occupier within the premises.

[0120] Fig. 15 is a schematic configuration representation of the integrated, continuous operation, condensed and very small size Micro Processing Reactor Plant (MPRP) 386 incorporating the Micro Processing Reactor Plant (MPRP) 399 for onsite domestic, commercial or industrial premises installations. The Input Waste Feedstock347 is processed by the preparation assembly 420 through the processing pyrolytic chamber 427 that produces commodity outputs of bio-gas 402, bio-oil 403, bio-char 404 and water 401. Micro turbines convert each of the commodity outputs to usable renewable energy 405 for reuse in the premises via the Reactor Application and Energy Management Unit (E U) 408 or distributed to the central power grid 288. Locally generated external solar and wind energy is also harvested and becomes available energy for distribution 406. Clean air is produced by the Air Neutraliser 432.

[0121] Fig. 16 is a schematic configuration representation of the integrated, continuous operation of the Converter Reactor Unit (CRU) (item 88) as a single reactor configuration. The Conversion Reactor Units are fixed temperature (see Fig. 3 item 88) in either low, medium or high temperature variants or as variable temperature (see Fig. 3 item 88T) with monitor and control functionality by the attached multiple sensors 88S. Asthe Input Waste Feedstock 600 enters the converter reactor chamber 603 the process generates bio-gas 607 that is utilised to heat or dry the input waste feedstock 601 and, via the gas turbine 609, power drive motor 599 to turn an auger 476 within pyrolytic chamber 603. The produced bio-char 605 and bio-oil 612 are both stored in respective storages 616 and 615 for further processing as required.

[0122] Fig. 17 is a schematic configuration representation of the integrated, continuous operation of the Converter Reactor Unit (CRU) 88 as a dual reactor configuration. The initial process is as shown in Fig. 16, with the addition of a second Converter Reactor 604 that incorporates a Cooler and Motor assembly 611 to cool the bio-char 605. Liquefied fertiliser injectors 598 are incorporated to infuse the hot bio-char with liquid organic fertiliser. This process creates a new product for the agricultural sector as a single-shot sequestration of the bio-char to rejuvenate the soil, deliver organic fertiliser, retain moisture and promote microbial activity.

[0123] Fig. 18 is a schematic configuration representation of the integrated, continuous operation of the Converter Reactor Unit (CRU) 88 as a dual reactor configuration with Liquefied Fertiliser Injectors 598 with an incorporated steriliser 617. This process creates a new product for the health and medical industries to process waste from hazardous healthcare and infectious diseases.

[0124] Fig. 19 is a schematic configuration representation of the integrated, continuous operation of the Converter Reactor Unit (CRU) 88 as a dual reactor configuration with Liquefied Fertiliser Injectors 598 with an incorporated steriliser 617. In order to make use of the bio-gas that is produced 607, alternate energy generation 622 is incorporated in the form of multiple Organic Rankine Cycle units 620 (optionally further supplemented with panels of photovoltaic cells). The thermodynamic cycle generates electricity 622 for battery storage in addition to the electricity produced from the bio-gas conversion 613, bio-oil conversion 615 and bio-char converter 616. All generated electricity is subsequently stored in batteries or fuel cells such as hydrogen 614.

[0125] Fig. 20 is a schematic configuration representation of the integrated, continuous operation of the Converter Reactor Unit (CRU) 88 as a single reactor 462 and dual outputs 466 (on the left in this view) and 468 (on the right in this view). This configuration allows for independent dual processing of the organic material in pyrolytic chambers 477 and 482 provided with respective condensers 494 and 502. The pretreatment unit 503 is complete with shredders 447, 448, 449 and cutters 451 as an all-inclusive treatment process.

[0126] Fig. 21 is a schematic representation of a distributed knowledge base of the system of Fig. 1, according to an embodiment of the present invention.

[0127] Fig. 22 is schematic view of a core module 730 for processing organic waste according to yet another embodiment of the present invention. Core module 730 is configured to provide a process flow, for processing organic waste, contained in a single compact unit 732 that includes essentially of all the requisite data collection and monitoring sensors, control functions, waste input loading, processed commodity output, bio-gas output stage and heat extraction for redirection into a core processing chamber 734. Front and rear removable maintenance plates 736a, 736b (the latter provided with a shaft anchor plate 738) position a hollow auger-like helical screw shaft 740 at a central axis of processing chamber 734. Screw shaft 740 is driven by a drive motor 742 supported by rear maintenance plate 736b. Front and rear removable maintenance plates 736a, 736b facilitate easy access for maintenance. Helical screw shaft 740, with heat flowing through its hollow interior, provides indirect heat transfer for even and consistent heat distribution onto a waste stream in processing chamber 734.

[0128] Processing chamber 734 is provided with a bio-gas output 744 in the top of core module 730. Processing chamber 734 is surrounded by an outer chamber 746 that can circulate hot air or other gases around processing chamber 734 in order to facilitate the processing of waste within processing chamber 734. If desired, outputted bio-gas 748 can be fed into and burned in an external bio-gas to heat converter 750 (such as a furnace) and the resulting heated gas products, heated air or steam 751 admitted to outer chamber 746 via a first heat input 752a and to the hollow interiorof screw shaft 740 via a second heat input 752b.

[0129] Waste is admitted to processing chamber 734 through a demountable waste input hopper 754 removably attachable (remote from bio-gas output 744 ) to the top of core module 730 via a top coupling module 756 located above an upper hatch 758 of processing chamber 734 that is closable with a slidable cover 760.

[0130] Bio-char is outputted through a processed waste hatch 762 from closable with a slidable cover 764 in the bottom of core module 730, downstream from waste input hopper 754. A bottom coupling module 766 is located below processed waste hatch 762 to facilitate the coupling, if desired, of core module 730 to, for example, another comparable core module.

[0131] Core module 730 is approximately 2500 mm in length and 450 mm in diameter, so that it can be accommodated by a shipping container (though these dimensions can be varied according to requirements).

[0132] Process control is facilitated by an input waste feed material identification subsystem in the form of Artificial Intelligence (A.I.) system 780 that is configured to determine the structure, composition and condition of the incoming waste feedstock. A.I. system 780 receives input data from external sources (as to waste composition or origin) and from sensors (not shown) directed at or sampling the input waste, and is trained to determine from that input data that structure, composition and condition.

[0133] A module configuration processor 784, in wired or wirelessly data communication with A.I. system 780, receives the determination of the structure, composition and condition of the incoming waste feedstock from A.I. system 780 and, on that basis, determines the optimum number of standard core modules 730 required and activates those core modules. Module configuration processor 784 is in data communication 786 with core module 730 (and any other activated core modules), manages the process parameters and control functions of the process stream, and predicts the output quality of the outputs of the core modules.

[0134] Core module 730 includes on-board sensors CiSi, CiS2,. . . CiS„ 782 (which may be IOT enabled) that gather real-time processing data indicative of processing parameters, such as temperature, pressure, flow and chemical composition, which is communicated (wired or wirelessly) to an edge computer 790, which provides localised remote decision making and transmits important data to a remote, Global Monitoring and Control Centre 806.

[0135] The quality of data that is collected from core module 730 provides the learning iterations and methods for a reliable and directly applicable output quality outcome.

[0136] A data collection processor 792 is configured between edge computer 790 and module configuration processor 784, and enables blockchain digital ledgering 794 facilitating real-time data traceability 796.

[0137] A.I. system 780 facilitates the production of highly controlled industrial quality commodities such as biochar 605, bio-oil 612 and bio-gas607 that promotes a circular economy by reuse, recycle and repurpose. Thus, the organic waste material is kept in circulation following remoleculisation of its core constituent properties as oil, gas and char.

[0138] A.I. system 780 can interconnect the real-time generated and traceable blockchain data with a Carbon Trading Exchange 798 (via a carbon enabling portal 800) to secure real-time Carbon Credits 802. Carbon sequestration 804 of bio-char output captures and stores carbon into the soil and thus earns carbon credits. Sequestration of bio-char into the soil creates net emissions less than zero which has a net effect or removing and storing carbon dioxide from the atmosphere. A.I. system 780 interfaces with a central Global Monitoring and Control Centre 806 by transmitting the locally collected process data streams 808, such as via a data communications interface 810 and an on-board Very-Small-Aperture Terminal (VSAT) dish antenna 812 (in communication with an accessible communications satellite), and hence to Global Monitoring and Control Centre 806.

[0139] Fig. 23 is schematic view of core module 730 with a preparation module 850 according to an embodiment of the present invention. Preparation module 850 is fed with wet or sorted or shredded organic waste. Preparation module 850 includes a dryer 852 (for drying wet Municipal Solid Waste, and in this example with two chambers 852a, 852b) and a steriliser 854 (for sterilising medical waste). Preparation module 850 receives bio-gas output 751 from core module 730, which is used by dryer 852; steriliser 854 sterilises medical waste with, for example, ultraviolet radiation.

[0140] Other combinations of waste preparation processes can be implemented in preparation module 850 as required.

[0141] In order to produce additional electricity 878, preparation module 850 includes one or more Organic Rankine Cycle (ORC) energy generation units 856 (in this example, ORCi to ORCS), mounted, for example, on top of preparation module 850 and fed with the bio-gas output 751 from core module 730. Likewise, in this and other embodiments, preparation module 850 may include one or more panels 876 of photovoltaic cells (mounted, for example, above ORC energy generation units 856) for still further generation of electricity 878.

[0142] Waste is fed into a hopper 858 of preparation module 850, then carried by a conveyor belt 860 through dryer 852 and steriliser 854. After preparatory treatment by preparation module 850, the treated waste 862 is discharged by preparation module 850 through a lower hatch 864 thereof and, via a transfer chute 866, though upper hatch 758 and into core module 730. Consequently, waste input hopper 754 of core module 730 is not required so is demounted in this configuration.

[0143] Core module 730 can be arranged with a further such core module so that the output of core module 730 is fed into the input of the further core module. This arrangement facilitates greater processing of the waste.

[0144] Thus, Fig. 24 is schematic view of a core module 730 accommodated within a standard 10-foot shipping container 880 (of height 2376 mm, width 2340 mm and length 2831 mm).

[0145] Fig. 25 is schematic view of first and second core modules 730, 730' configured in series within a standard 20-foot shipping container 900, the output of first core module 730 being fed into second core module 730' for further processing.

[0146] Fig. 26 is schematic view of first to fourth core modules 730, 730', 730", 730"' configured in series within a standard 40-foot shipping container 920, the output of first core module 730 being fed into second core module 730' forfurther processing, the output of second core module 730' being fed into third core module 730" for still further processing, and the output of third core module 730" being fed into fourth core module 730'" for yet further processing— thereby providing flexibility for waste process tasks.

[0147] Fig. 27 is schematic view of first, second and third core modules 730, 730', 730" according to another embodiment of the present invention and accommodated within a standard 20-foot shipping container 930. First and second core modules 730, 730' are arranged in parallel, and third core module 730" receives input from the output of second core module 730'. This arrangement may be advantageous in certain implementations in which other equipment is also housed in container 930.

[0148] Fig. 28 is schematic view of first, second and third core modules 730, 730', 730" according to another embodiment of the present invention accommodated within a standard 20-foot shipping container 940. First and second core modules 730, 730' are arranged in parallel but such that waste received in second core module 730' is back and forth until expelled in at least partially processed form into third core module 730".

[0149] Fig. 29 is schematic view of eight core modules accommodated within a standard 20-foot shipping container 950 according to another embodiment of the present invention, configured as a first stack 952 of fourcore modules and a second stack 954 of four core modules. Waste is inputted via a waste input hopper 956 of the uppermost core module of first stack 952. The bio-char, or partially processed waste, output of lowermost core module of first stack 952 is conveyed (whether by pumping, bucket elevator or otherwise, as required and according to the consistency of the bio-char, or partially processed waste) inside a conduit 958 to the input hatch 960 of the uppermost core module of second stack 954. Bio-gas is outputted by each core module, such as to a converter in the form of condenser / blender 610 for producing bio-oil. Although not shown in this figure, this embodiment may optionally include one or more panels of photovoltaic cells for generating electricity, mounted — for example — on or above the top of shipping container 950.

[0150] Fig. 30 is schematic view of 12 core modules accommodated within a standard 40-foot shipping container 970 according to another embodiment of the present invention, configured as a first stack 952 of four core modules, a second stack 954 of four core modules and a third stack 972 of four core modules. Waste is inputted via a waste input hopper 956 of the uppermost core module of first stack 952. The bio-char, or partially processed wast, output of the lowermost core module of first stack 952 is conveyed (whether by pumping, bucket elevator or otherwise, as required and according to the consistency of the bio-char, or partially processed waste) inside a first conduit 958 to the input hatch 960 of the uppermost core module of second stack 954; likewise, the bio-char, or partially processed waste, output of the lowermost core module of second stack 954 is conveyed inside a second conduit 974 to the input hatch 976 of the uppermost core module of third stack 972. Bio-gas is again outputted by each core module, such as to a converter 962 for producing bio-oil. This embodiment may also optionally include one or more panels of photovoltaic cells for generating electricity, mounted — for example — on or above the top of shipping container 970.

[0151] Fig. 31 is a plan schematic view of a core module 980 according to another embodiment of the present invention. Core module 980 is comparable to core module 730 of Figs. 22 to 24 but is configured for central loading. That is, core module 980 has a waste input hatch 982 located centrally along the length of core module 980. This arrangement facilitates a more compact configuration.

[0152] Fig. 32 is a plan schematic view of core module 980 in a vertical stack 990 of plural core modules of the same design. Waste is inputted into the uppermost core module and bio-char is discharged from the lowermost core module. Bio-gas is outputted by each core module 980, such as to a converter for producing bio-oil. The respective core modules 980 are oriented rotated about their central vertical axis relative to one another to facilitate access to each core module, but such comparable access can instead be provided by providing vertical space between each pair of core modules 980.

[0153] In more detail, Fig. 1 is a schematic representation of a Multi Modal Autonomous Reactor (MMAR) which includes a Processing Reactor Plant (PRP), according to an embodiment of the present invention. The MMAR implements an energy distribution model for, in particular, remote or rural regions. The availability of real-time ongoing energy, generated from organic waste processing, provides the ability to electrify energy intensive industries such as Remote Internet Hosting Data Centres, Remote Scientific Laboratories and Remote Data Collection Systems as examples.

[0154] This embodiment— and the alternative embodiments described herein— comprises fifteen major interconnected systems (and optionally other systems), as follows.

[0155] 1. INPUT SYSTEM

[0156] The embodiments of the invention employ flexible input feedstock identification (Figs. 2A& 2B item 1), and preparation and arrangement of waste feedstock capacities (Figs. 2A& 2B item 2) that are integrated into the plant converter process (Figs. 2A& 2B item 3) to provide seamless process handling and control capabilities via an internal Master Multi-Path Matrix Manager (Figs. 2A& 2B item 24).

[0157] In-built, intuitive analysis, for the input (Figs. 2A& 2B item l), the arrangement (Figs. 2A& 2B item 2) and conversion (Figs. 2A& 2B item 3) is based on Artificial Intelligence agents (Fig. 1 items 8, 11, 14) utilising localised onboard data lookup tables (Figs. 2A& 2B items 27, 28, 29) and in a remote central repository (Figs. 2A& 2B item 40, 41), that is updated and referenced in real-time for comparative analytic data, as an example, collected from all otherglobal installations of waste processing plants.

[0158] The discrete, independent and localised module specific Artificial Intelligence, for the input (Figs. 2A& 2B item 1), the arrangement (Figs. 2A& 2B item 2) and conversion (Figs. 2A& 2B item 3), is provided within the Process Matrix Control (PMC) (Figs. 2A& 2B item 21) for each of the modules for assistance, guidance and recommendations to store (Figs. 2A& 2B item 28) for future use with reiterative updates using the onboard Machine Learning processes (Figs. 2A& 2B item 28M). Individual and localised Artificial Intelligence capability is established in the Input Matrix Control (IMC) (Figs. 2A& 2B item 7) as LocalA.I. (I) (Figs. 2A& 2B item 8), the Arrange Matrix Control (AMC) (Figs. 2A& 2B item 10) as LocalA.I. (A) (Figs. 2A& 2B item 11), the Convert Matrix Control (CMC) (Figs. 2A& 2B item 13) as Local A. I. (C) (Figs. 2A& 2B item 14).

[0159] The independent Master Multi-Path Matrix Manager (MMM) (Figs. 2A& 2B item 24) utilises distributed, decentralised management functionality to process, control, collect, administer, authenticate and validate all localised data underthe remote management of the Master Artificial Intelligence Engine (MAIE) (Figs. 2A& 2B item 37) via the Remote Monitoring and Control Centre (RMCC) (Figs. 2A& 2B item 43).

[0160] The characteristics and functionality of the plant is established via an Administration Management and Control (AMC) (Figs. 2A & 2B item 32, Figs. 2C & 2D item 32, Fig. 5 item 32, Fig. 6 item 32, Fig. 7 item 32, Fig. 10 item 32) integrating a Master Artificial Intelligence Engine (MAIE) (Figs. 2A& 2B item 37, Figs. 2C & 2D item 37, Fig. 5 item 37, Fig. 6 item 37, Fig. 7 item 37, Fig. 8 item 37, Fig. 9 item 37, Fig. 10 item 37) with data lookup tables with data records in Rules and Facts (Figs. 2A& 2B item 41), algorithms used to perform computations, instructions and actions (Figs. 2A& 2B item 36), self-contained applications (Figs. 2A & 2B item 35), access control authentication (Figs. 2A& 2B item 34) and simultaneoussychronisation (Figs. 2A& 2B item 33) within a Remote Monitoring and Control Centre (RMCC) (Figs. 2A& 2B item43), as an example.

[0161] The Master Artificial Intelligence Engine (MAIE) (Figs. 2A& 2B item 37) also manages the actions, activities and undertakings for the Blockchain and Smart Contracts (Figs. 2A& 2B item 69) by recording all the necessary information relating to the input stages, from all the relevant functions such as the input (Figs. 2A& 2B item 1), the arrangement (Fig. 1 item 2) and conversion (Figs. 2A& 2B item 3).

[0162] The waste feedstock arrangement and preparation sub-system (Figs. 2A& 2B item 2,) with unique capabilities for pulverising, dewatering, drying, shredding, crushing, sterilising etc. (Figs. 2A& 2B item 82) is configured by the Input Feedstock Imaging and Identification Manager (I Fl) (Figs. 2A& 2B item 50) and controlled, in real-time, by the Remote Monitoring and Control (RMCC) (Figs. 2A& 2B item 43) forthe predetermined output (Figs. 2A& 2B item 4) as required by the market or buyers (Figs. 2A& 2B item 44).

[0163] The processing waste feedstock converter (Figs. 2A & 2B item 3) is configured by the Converter Matrix Control (CMC) (Figs. 2A& 2B item 13), influenced by the required demand from the market, orthe buyers of the end product (Figs. 2A& 2B item 44) as forecast and supplied from the data and analytics provided by the remote Administration Management and Control (AMC) (Figs. 2A& 2B item 32).

[0164] The identified input waste feedstock (Figs. 2A& 2B item 1) is arranged (Figs. 2A& 2B item 2) and readied for conversion (Figs. 2A& 2B item 3) to create the required output product (Figs. 2A& 2B item 4) to supply into the market as the demand is identified (Figs. 2A& 2B item 44) by the remote Administration Management and Control (AMC) (Figs. 2A & 2B item 32).

[0165] This embodiment uses information relatingto the external market requirement (Figs. 2C & 2D item 44) and the actual Product Buyer demand (Figs. 2C& 2D item 72) for the output product type and specifications, from data and statistics gathered from local resources by the Master Artificial Intelligence Engine (MAIE) (Figs. 2A& 2B item 37). This process provides the condition for Market Push Forces (Figs. 2C & 2D item 71) from the Commodity Product Output (Figs. 2C& 2D item 70) and the Market Pull Forces (Figs. 2C& 2D item 73) from industry and market derived data in the Rules and Facts (R) database (Figs. 2C & 2D item 41), appropriate process Algorithms (Figs. 2C & 2D item 36) via the Master Artificial Intelligence Engine (MAIE) (Figs. 2C & 2D item 37) to effectively meet market demand (Figs. 2A& 2B item 44, Figs. 2C& 2D item 72).

[0166] The Master Multi-Path Matrix Manager ( MM) (Figs. 2A& 2B item 24), via the Convert Matrix Control (CMC) (Figs. 2A& 2B item 13) then configures the Converter Reactor Unit (CRU) (Figs. 2A & 2B item 15) to match the available input waste feedstock (Figs. 2A& 2B item 1) to the required output (Figs. 2A& 2B item 4) from the Processing Reactor Plant (PRP) (Figs. 2A& 2B item 31). This method maximises input waste processing efficiency via the Process Matrix Control (PMC) (Figs. 2A & 2B item 21) and also maximises the buyer uptake forthe output product. Production processes can now be planned, forecast and supplied from the data and analytics provided by the remote Administration Management and Control (AMC) (Figs. 2A& 2B item 32). This embodiment supports customisation and individualised processingforthe same type of input waste feedstock within various markets, geographic locations or environmental circumstances, to cater for localised market variants for the required output product. As an example biomass as in put waste feedstock (Fig. 3 item 77) in China may require different processingto the same type of biomass as input waste feedstock in France. The input waste feedstocks are the same types however the market demand forthe resultant outputs may be different in each of the locations. All such location processing data is captured and processed within the Administration Management and Control (AMC) (Figs. 2A& 2B item 32).

[0167] The input waste feedstock (Figs. 2A& 2B item 1, Fig. 3 item 77) may be separated, sorted or mixed together as municipal solid waste, biomass (wood, agricultural residues, processing waste, waste paper), asbestos, waste oils, oil refinery sludges, treated human sewage, animal manure, blood and bone and any other organic compounds for processing within the Process Matrix Control (PMC) (Figs. 2A& 2B item 21). This complex, intricate combinational process provides the Administration Management and Control (AMC) (Figs. 2A& 2B item 32) an ability to develop a strategic control process formulation of the Converter Reactor Unit (CRU) (Figs. 2A& 2B item 12) to match the required output product (Figs. 2A& 2B item 44).

[0168] 2. PROCESSING SYSTEM

[0169] This embodiment has highly configurable and controllable converter (Fig. 3 item 88, Fig. 3 item 88T) with blending and condenser stages (Fig. 3 item 89). From the information provided by the Arrange Matrix Control (AMC) (Fig. 3 itemlO) to the Convert Matrix Control (CMC) (Fig. 3 iteml3) the base Reactor Configuration (Fig. 3 item 87) requirements are established. Atthis stage the Conversion Reactors (Fig. 3 item 88) are selected for Low Temperature, Medium Temperature or High Temperature waste feedstock processing and the time duration required to process. The assessment is also made by the Process Requirement Detection (Fig. 3 item 86) in conjunction with the Energy Conversion and Distribution (ECD) (Fig. 3 item 85) if a Variable Temperature (Fig. 3 item 88T) process is required. This variable temperature processing chamber is equipped with a multitude of sensors (Fig. 3 item 88S) to accurately measure and control the temperatures, pressures, flow etc. at the predefined chamber points.

[0170] The Input Feedstock Imaging and identification Manager (I Fl) (Fig. 3 item 50) within the Arrangement Unit (Fig. 3 item 2) determines if the waste input feedstock has a pre-determined level above 10% moisture content. If this level is exceeded the Dewater and Dry functions (Fig. 3 item 82) are activated until feedstock moisture levels are restored. This clean condensed water can be sold to third parties as non- potable water, not for drinking but may still be used for many other purposes.

[0171] The sealed, oxygen deprived, chamber of the Conversion Reactor (Fig. 3 item 88) is heated on the outside to the desired temperature (Low at400°C, Medium at 600°C or High at 1100°C) (Fig. 3 item 88) ora range of Variable Temperatures (from 400°Cto 1100°C) (Fig. 3 item 88T) with sensor monitors and control (Fig. 3 item 88S). The chambers have very highly refined temperature controls that allow for low temperature (typically 400°C) to extremely high temperature (typically 1100°C) variations, to reorganises the molecular structure of the internal organic feedstock material. The remote Administration Management and Control (AMC) (Figs. 2A& 2B item 32) monitors and controls the Converter Reactor Unit (CRU) (Figs. 2A& 2B item 12) to ensure all time, temperature, flow etc. process variables are consistent with the anticipated output (Figs. 2A& 2B item 4).

[0172] With a thirty-minute processing cycle, the organic waste feedstock on the inside of the chamber reorganises its molecularstructure to commodities of Bio-Char (Fig. 3 item 101), Bio-Gas (Fig. 3 item 95) and Bio-Oil (Fig. 3 item 97) from the Condensing Unit (Fig. 3 item 89). A blended Bio-Oil (Fig. 3 item 99) is derived from the internal Blending Unit (Fig. 3 item 89). From the Dewater and Dry functions (Fig. 3 item 82) water is produced (Fig. 3 item 103) and stored in Storage (05) (Fig. 3 item 102) or used in a Steam Fired Turbine (Fig. 3 item 107) to produce electricity via the Energy Control Unit (Fig. 3 item 106) or to be used for Energy Storage (Fig. 3 item 105) within Batteries or Fuel Cells (Fig. 3 item 104) or for further processing within the Energy Distribution Process (Fig. 11 item 287), Energy to Power Grid (Fig. 11 item 288), Energy to External Connections (Fig. 11 item 273), Energy to Transportable Units (Fig. 11 item 286) and Product Conversions (Fig. 11 item 287C) to hydrogen, graphite or graphene.

[0173] Similarly, the generated Bio-Gas (Fig. 3 item 95) can be stored in Storage (01) (Fig. 3 item 94) or used in a Gas Fired Turbine (Fig. 3 item 107) to produce electricity via the Energy Control Unit (Fig. 3 item 106). The generated Bio-oil (Fig. 3 item 97) can be stored in Storage (02) (Fig. 3 item 96) or used in an Oil Fired Turbine (Fig. 3 item 107) to produce electricity via the Energy Control Unit (Fig. 3 item 106). The generated Blended Bio-Oil (Fig. 3 item 99) can be stored in Storage (03) (Fig. 3 item 98) or used in a Fuel Fired Turbine (Fig. 3 item 107) to produce electricity via the Energy Control Unit (Fig. 3 item 106). The generated BioChar (Fig. 3 item 101) can be stored in Storage (04) (Fig. 3 item 100) or used in a Coal Fired Turbine (Fig. 3 item 107) to produce electricity via the Energy Control Unit (Fig. 3 item 106). All of the above electricity produced from the turbines can also be used for Energy Storage (Fig. 3 item 105) within Batteries or Fuel Cells (Fig. 3 item 104) orforfurther processingwithin the Energy Distribution Process (Fig. 11 item 287), Energy to Power Grid (Fig. 11 item 288), Energy to External Connections (Fig. 11 item 273), Energy to Transportable Units (Fig. 11 item 286) and Product Conversions (Fig. 11 item 287C) to hydrogen, graphite or graphene.

[0174] 3. OUTPUT SYSTEM

[0175] Referrin to Fig. 3, the output sub-system 4 has highly configurable units that provide the raw material produced from the Conversion Reactors 88 into commodity storage such as Bio-Gas Storage (01) 94, BioOil Storage (02) 96, Blended Bio-Oil Storage (03) 98, Bio-Char Storage (04) 100 and Water Storage (05) 102.

[0176] The Energy Conversion and Distribution (ECD) 85 directs any Spare Energy 92 not utilised within the Converter process 3 to the Energy Control Unit 106 for further assessment a nd distribution as necessary. The Energy Control Unit 106 may also direct additional energy 93 as required by the Output Unit 1.

[0177] The output raw material produced from the Conversion Reactors 88 may also be used directly as commodities 90 (cf. Fig. 11 item 70) to produce other products as Pre-Process Commodity (cf. Fig. 1 item 15 & Fig. 11 item 292) such as nanotechnology, industrial process, construction products, fertiliser, synthetic fuels, steel production, fuel cells or electronic components.

[0178] 4. ENERGY SYSTEM

[0179] The invention provides an energy generation Processing Reactor Plant (see Figs. 2A& 2B item 31, Fig. 4 item 31) and an energy recovery system (see Fig. 3 item 5) that has varied applications in the industrial, commercial, agricultural and domestic sectors.

[0180] The system is flexible in the configuration and processing of organic waste feedstock via the Process Matrix Control (PMC) (Fig. 3 item 3) with many application areas not yet practiced. The Conversion Reactors (Fig. 3 item 88) produce renewable energy via the Energy Conversion Unit (Fig. 3 item 5) and the various internal turbines (Fig. 3 item 107) for Gas, Oil, Fuel, Coal and Steam. Other external renewable energy sources (such as solar, wind, Organic Rankine Cycle units etc.) (Fig. 3 item 76, Fig. 11 item 76, Fig. 11 item 76E, Fig. 14 item 384, Fig. 15 item 384, Fig. 19 item 620) independently producing electricity are consolidated as direct Energy Storage (Fig. 11 item 294). Further processing is possible within the Energy Distribution Process (Fig. 11 item 287), Energy to PowerGrid (Fig. 11 item 288), Energy to External Connections (Fig. 11 item 273), Energy to Transportable Units (Fig. 11 item 286) and Product Conversions (Fig. 11 item 287C) to hydrogen, graphite or graphene.

[0181] By consuming its own generated energy, the self-powered Processing Reactor Plant (PRP) (Figs. 2A& 2B item 31), is secured by the Security Authentication and Heartbeat Unit (SAH) (Figs. 2A& 2B item 23), monitored by Remote Monitoring and Control (AMC) (Figs. 2A& 2B item 43), configured by the Master Multi-Path Matrix Manager (MMM) (Figs. 2A& 2B item 24), controlled by the Administration Management and Control Unit (AMC) (Figs. 2A& 2B item 32), in real-time from a central remote site by the Remote Monitoring and Control (AMC) (Figs. 2A& 2B item 43).

[0182] 5. SELF POWERED SYSTEM

[0183] This embodiment is a self-generating energy system that stores that energy as Energy Storage (Fig. 3 item 105) and controlled by the Energy Control Unit (Fig. 3 item 106). The energy is generated by processing organic waste feedstock (Fig. 3 item 1) via the Arrangement Unit (Fig. 3 item 2) and then through the Convert Unit (Fig. 3 item 3) to produce the commodity output as Bio-Gas (Fig. 3 item 95), Bio-Oil (Fig. 3 item 97), Blended Bio-Oil (Fig. 3 item 99), Bio-char (Fig. 3 item 101) and Water (Fig. 3 item 103). These commodities are then used within the releva nt turbines such as Gas, Oil, Fuel, Coal and Steam (Fig. 3 items 107) to finally produce electricity that is controller and managed by the Energy Control Unit (Fig. 3 item 106). When the Master Multi-Path Matrix Manager (MMM) (Fig. 3 item 24) requires to energise the Processing Reactor Plant (PRP) (Figs. 2A & 2B item 31) the Energy Control Unit (Fig. 3 item 106) directs energy to the Add Energy Unit (Fig. 3 item 93) and then to the Energy Conversion and Distribution (ECD) (Fig. 3 item 85). The Convert Matrix Control Unit (CMC) (Fig. 3 item 13) manages the energy requirement of the Converter Unit (Fig. 3 item 3).

[0184] External renewable energy sources such as solar, wind, Organic Rankine Cycle units etc. (Fig. 3 item76, Fig. 11 item 76, Fig. 11 item 76E, Fig. 14 item 384, Fig. 15 item 384, Fig. 19 item 620) independently producing electricity are consolidated as direct Energy Storage (Fig. 11 item 294) and internally within the Energy Collection Unit (Fig. 3 item 81). This additional energy is also directed to the Energy Conversion and Distribution Unit (ECD) (Fig. 3 item 85).

[0185] The Energy Conversion and Distribution (ECD) (Fig. 3 item 85) is manages and directs any additional, unused and Spare Energy (Fig. 3 item 92) not utilised within the Converter process (Fig. 3 item 3) to the Energy Control Unit (Fig. 3 item 106) for further assessment and distribution as necessary. The Energy Control Unit (Fig. 3 item 106) may also direct additional energy back to the Add Energy Unit (Fig. 3 item 93) as required by the Converter Unit (Fig. 3 item 3) via the Convert Matrix Control (CMC) (Fig. 3 item 13).

[0186] 6. HEARTBEAT ACTIVATION, SECURITY AND AUTHENTICATION SYSTEM

[0187] Currently, most highly secure authentication codes rely on two-factor or at best three-factor authentication. The existing definition, as published openly, is that two-factor authentication is the method used to authenticate ‘someone’ with the presentation of two or three authentication factors such as:

[0188] Factor One - The knowledge factor (‘something the user knows’)

[0189] Factor Two - The possession factor (‘something the user has’)

[0190] Factor Three - The inherence factor (‘something the user is’)

[0191] This embodiment provides for a Factor Four authentication method that features a Dynamically Variable code, incorporates a Multi-Factor method, integrates a Sequential Bucket-Brigade procedure and is a Successively Adaptive authentication process (Fig. 8, item 23) to authenticate a local operator, an internal process, a multitude of sensorfunctions, locations or intellectual property coding.

[0192] The Factor Four authentication method has the following attributes:

[0193] 1. Dynamically Variable: The code generation, stored within the Security Code Database (R) (Fig. 8 item 205), is unique for each Processing Reactor Plant (PRP) (Fig. 8 item 31) and is dynamically uploaded to the Processing Reactor Plant (PRP) (Fig. 8 item 31) within the Security Code Database (R) (Fig. 8 item 205) on a daily basis, or any other frequency selected option.

[0194] 2. Multi-Factor: Uses all the sub-systems (Fig. 8 item 225) to formulate and prepare the total security code in association with the Bucket-Brigade elements (Fig. 8 item 226). Each sub-system (Fig. 8 item 225) provides its own factor to the authentication process via the interchange elements (Fig. 8 item 227). Thus, hundreds of codes for authentication are available in the system implementation.

[0195] 3. Sequential Bucket-Brigade: Each individual sub-system code (Fig. 8 item 225) is re-sequenced in the interchange unit (Fig. 8 item 227) as directed by the Sequence Interchange Control unit (SIC) (Fig. 8 item 213) in the order that is dictated by the Security Access Request and Control unit (SARC) (Fig. 8 item 208) and subsequently requested by the Remote Monitoring and Control (RMCC) (Fig. 8 item 43).

[0196] 4. Successive Adaptive: The first reference data foreach Processing Reactor Plant (PRP) (Fig. 8 item 31), as initially nominated by the Remote Monitoring and Control (RMCC) (Fig. 8 item 43), is stored within the Storage (R) unit (Fig. 8 item 40) and uploaded to the Security Code Database (R) (Fig. 8 item 205). New reference data is periodically requested by the Remote Monitoring and Control (RMCC) (Fig. 8 item 43) from the Processing Reactor Plant (PRP) (Fig. 8 item 3). The received data is verified and confirmed by the Master Artificial Intelligence Engine (MAIE) (Fig. 8 item 37). This method is used to validate the data from the last known reference, stored in the Security Code Database (R) (Fig. 8 item 205), and compared with the most current received reference. One such example is used to compare the previous GPS location coordinates to new GPS location coordinates of each Processing Reactor Plant (PRP) (Fig. 8 item 3). Any variance in location coordinates means the Processing Reactor Plant (PRP) (Fig. 8 item 3) has now moved to a new location. The last known GPS coordinates of each Processing Reactor Plant (PRP) (Fig. 8 item 3) becomes an integral part of the Security Code Database (R) (Fig. 8 item 205). Thus the Processing Reactor Plant (PRP) (Fig. 8 item 3) cannot be moved without the Remote Monitoring and Control (RMCC) (Fig. 8 item 43) logging the coordinates into the Security Code Database (R) (Fig. 8 item 205).

[0197] All of the above authentication methods can be composed together, singularly or in variable arrangement, to produce a verifiable authentication outcome stored in the Security Code Database (R) (Fig. 8 item 205).

[0198] The mathematical formula for a Factor Four authentication process involves combining multiple factors in a way that ensures security and reliability. Below is a simplified formula that is applied to the present embodiment and is the basis of designingthe strong, robust and secure authentication system as applied to the Local Operator (Fig. 9 item 253), the Processing Reactor Plant (PRP) (Fig. 8 item 3) and the Remote Monitoring and Control (RMCC) (Fig. 8 item 43).

[0199] Fi, Fi, F3, and F4represent the four authentication factors. These factors respectively include and relate to: Fi = something the user knows (e.g. a password) Fi = something the user has (e.g. a physical token)

[0200] F3= something the user is (e.g. biometric data such as face, finger, iris)

[0201] F4= somewhere the user is (e.g. geolocation)

[0202] Each factor is assigned different weights based on their importance and reliability the four-factor authentication process is represented as follows:

[0203] Authentication Score (AS) = Wi x Factor Score (Fj

[0204] + W x Factor Score (F2)

[0205] + W3x Factor Score (F3)

[0206] + W4x Factor Score (F4) where:

[0207] 1. Authentication Score (AS) is the overall score indicating the confidence level of the authentication process.

[0208] 2. Wi, Wi, W3and W4are the weights assigned to each factor.

[0209] 3. Factor Score ( F,) is the score obtained from the individual authentication factor ( Fi), calculated using various methods such as cryptographic algorithms for passwords, proximity checks for physical tokens, biometric data or geolocation coordinates.

[0210] The factors and their respective scores are carefully designed and validated to ensure the security of the authentication process. Additionally, incorporating dynamic elements such as time-based authentication codes or behavioral biometrics will further enhance the security of the system.

[0211] As an example the Master Artificial Intelligence Engine (MAIE) authenticates the local operator (Fig. 9 item 235) with multiple means of security identification such as biometrics, physical smart card identification etc. (as shown in Fig. 6 item 139) and using various databases such as the Rules and Facts Database (R) (Fig. 8 item 41) and Security Code Database (R) (Fig. 8 item 205) as well as the various Applications such as the Authentication (R) (Fig. 8 item 131), Authorisation (R) (Fig. 8 item 131), Security (R) Applications (Fig. 8 item 131) and other Applications such as Biometrics (R) (Fig. 8 item 132) and Rules (R) (Fig. 8 item 132).

[0212] The implementation of the embodiment involves, as examples, complex algorithms (Figs. 2A& 2B item 8, Figs. 2A& 2B item 11, Figs. 2A& 2B item 14, Figs. 2A& 2B item 20, Figs. 2A& 2B item 36, Fig. 5 item 134, Fig. 6 item 139, Fig. 8 item 233, Fig. 10 item 256, Fig. 14 item 233), methodologies (Figs. 2A & 2B item 27, Fig. 3 item 79, Fig. 3 item 83, Fig. 5 item 41, Fig. 7 item 133, Fig. 7 item 133, Fig. 8 item 132, Fig. 8 item 41, Fig. 14 item 372, Fig. 14 item 398) and data processing (Fig. 3 item 80, Fig. 3 item 84, Fig. 5 item 133, Fig. 7 item 206, Fig. 14 item 348, Fig. 14 item 350) specific to each factor, and comply with the latest security standards and best practices.

[0213] This embodiment of the methodology protects the intellectual property as the security coding method (Fig. 8 item 23) is inherently embedded into each original system component and is used in a sequential manner to formulate the final code check digit (Fig. 8 item 228C). The check digit (Fig. 8 item 228C) generation requires all original components within the Processing Reactor Plant (PRP) (Fig. 8 item 31) to be present and to provide the original manufacturer’s designated code embedded within the Unique Reactor Authentication Code (URA) (Fig. 8 item 218) in a pre-arranged sequence and as requested by the code validation unit, the System Data Validation Codes (SDV) (Fig. 8 item 214). If a replacement component is inserted or replaced the original within the Processing Reactor Plant (PRP) (Fig. 8 item 31), then it must be reported to the Remote Monitoring and Control (RMCC) (Fig. 8 item 43) for validation or updating of the expected pre-assigned check digit within the Security Code Database (R) (Fig. 8 item 205). In this way fraudulent replacements are detected and not permitted for inclusion into the original system unless authorised by the Remote Monitoring and Control (RMCC) (Fig. 8 item 43).

[0214] This embodiment’s new and unique encrypted handshake authentication method uses the Sequential Bucket-Brigade feed (Fig. 8 item 226) from all the main components of the processing plant, to produce a ‘check digit’ (Fig. 8 item 228C).

[0215] The Sequential Bucket-Brigade check digit (Fig. 8 item 228C) is a consecutive derivation of the system ‘check digit’ using all the sub-system components that form the entire Processing Reactor Plant (PRP) (Fig. 8 item 31). This unique methodology provides a quick system check to ensure all the available system resources are responding and operational. It also functions as an interrogator of the unique code that was assigned by the system owner to all sub-systems within the Processing Reactor Plant (PRP) (Fig. 8 item 31). If any of the sub-systems were compromised, interfered or damaged by any means, security or fraudulent reproduction, the derived check digit (Fig. 8 item 228C) would not provide the correct authentication code and hence the Remote Monitoring and Control (RMCC) (Fig. 8 item 43) would not send a start heartbeat to the Processing Reactor Plant (PRP) (Fig. 8 item 31). On the third iteration of a ‘FAIL’ authentication the process would be terminated and an alarm is raised. The Remote Monitoring and Control Centre (RMCC) (Fig. 8 item 43) has exact location data of each Processing Reactor Plant (PRP) (Fig. 8 item 31) from each time the processing unit was logged on to the last time a maintenance / service routine was carried out. As each Processing Reactor Plant (PRP) (Fig. 8 item 31) is ready to begin processing, an encrypted data message is sent to the Remote Monitoring and Control (RMCC) (Fig. 8 item 43). This message is encrypted to prevent any interception and deciphering by any opponent within the information transmission in the network.

[0216] This authentication methodology improves security and control over the Processing Reactor Plant (PRP) (Fig. 8 item 31) as it introduces additional unique factors into the security code preventing unauthorised starting of the Processing Reactor Plant (PRP) (Fig. 8 item 31) without access to any secret sequencing codes stored within the Remote Monitoring and Control (RMCC) (Fig. 8 item 43).

[0217] The encrypted data message includes, but not limited to:

[0218] Source Data such as: GSP location ID, Operator / User ID, Manufacturers Code ID, Processing Unit ID, Synch ID, Time base ID.

[0219] Environmental Data such as: Temperature, Pressure, Flow, Air quality.

[0220] Other Local Influencing Matters such as: Water quality, Gas type, Oil viscosity, Char density.

[0221] The Remote Monitoring and Control Centre (RMCC) (Fig. 8 item 43) has exact GPS location data of each Processing Reactor Plants (PRM) (Fig. 8 item 31) collected by the Master Artificial Intelligence Engine (MAIE) (Fig. 8 item 37) and stored within the Security Code Database (R) (Fig. 8 item 205).

[0222] When the Remote Monitoring and Control Centre (RMCC) (Fig. 8 item 43) receives a signal from a Processing Reactor Plant (PRM) (Fig. 8 item 31) via the plant’s Security Access Request and Control Unit (SARC) (Fig. 8 item 208) a start sequence is commenced for authentication of the user, the transportable processing plant, the location of the processing plant and access authority amongst many other data, access control and security information. A first heartbeat data signal is sent to the transportable Processing Reactor Plant (PRM) (Fig. 8 item 31), which activates a known core part of the system. At the end of this sequence the transportable Processing Reactor Plant (PRM) (Fig. 8 item 31) sends a local derived completion security code. The Remote Monitoring and Control Centre (RMCC) (Fig. 8 item 43) uses this derived code with a new central code to initiate a second heart heat back to the remote transportable Processing Reactor Plants (PRM) (Fig. 8 item 31).

[0223] The security, authentication processingand heartbeat activation process within the Processing Reactor Plant (PRP) (item 31) is detailed as follows:

[0224] The Secu rity Access Requestand Control (SARC) (Fig. 8 item 208) manages all the functionality relatingto the Heartbeat Start (HBS) code (Fig. 8 item 210) to the Heartbeat Sequence Control (HSC) (Fig. 8 item 221) to the Unit / Sub-System Codes (Fig. 8 item 225) into the Bucket Code (Fig. 8 item 226) then into the Sequence Interchange Code (Fig. 8 item 227) generatingthe codes (Fig. 8 item 228) required forthe Heartbeat Response Code (HSC) (Fig. 8 item 229). The generated Security Response Code (SRC) (Fig. 8 item 216) coupled with the Manufactures Unique Code (Fig. 8 item 217) produces the Unique Reactor Authentication Code (URA) (Fig. 8 item 218) to create the Start Condition (Fig. 8 item 224) or the Alarm Condition (Fig. 8 item 223).

[0225] A schematic flow process, Fig. 9 - Security, Authentication and Heartbeat Timeline and Flow Process, represents an example of the security, authentication code generation process and the heartbeat activation process as represented and is to be read in conjunction with fig 8. - Security, Authentication Processing and Heartbeat Activation Process in orderforan operator to activate the Processing Reactor Plant (PRP) (Fig. 8 item 31) within a location.

[0226] Task A (Fig. 9 item 238) - The local operator (Fig. 9 item 235), via the Local Off-Line Access Control interface unit (LOA) (Fig. 8 item 22) creates a request to the Administration Management and Control (AMC) (Fig. 8 item 32) to activate the transportable Processing Reactor Plant (PRP) (Fig. 8 item 31) within his location.

[0227] Task B (Fig. 9 item 239) - The authentication and activation request received by the Remote Monitoring and Control Centre (RMCC) (Fig. 8 item 43) is processed by the Administration Management and Control (AMC) (Fig. 8 item 32) and via the Applications Management Unit (R) (Fig. 8 item 35) interacts with the Authentication & Authorisation Application Engine (AAE) (Fig. 8 item 231) to the Master Artificial Intelligence Engine (MAIE) (Fig. 8 item 37) that responds back to the Administration Management and Control (AMC) (Fig. 8 item 32) with validation or confirmation codes. The Master Artificial Intelligence Engine (MAIE) authenticates the local operator (Fig. 9 item 235) with multiple means of security identification such as biometrics, smart card etc. (as shown in Fig. 6 item 139) and using various databases such as the Rules and Facts Database (R) (Fig. 8 item 41) and Security Code Database (R) (Fig. 8 item 205) as well as the various Applications such as the Authentication (R) (Fig. 8 item 131), Authorisation (R) (Fig. 8 item 131), Security (R) Applications (Fig. 8 item 131) and other Applications such as Biometrics (R) (Fig. 8 item 132) and Rules (R) (Fig. 8 item 132).

[0228] Task C (Fig. 9 item 240) - The Administration Management and Control (AMC) (Fig. 8 item 32), with all the information provided to it via Task B (Fig. 9 item 239) above, positively authenticates or rejects the Local Operator (Fig. 9 item 235). The Administration Management and Control (AMC) (Fig. 8 item 32), with positive confirmation of successful security communication with the remote transportable Processing Reactor Plant (PRP) (Fig. 8 item 31) and from a known pre- registered location, responds back to the local operator (Fig. 9 item 235) that the communication link is successfully established.

[0229] Task D (Fig. 9 item 241) - Once the Administration Management and Control (AMC) (Fig. 8 item 32) has first level local operator positive authentication then access is granted to the Processing Reactor Plant (PRP) (Fig. 8 item 31). The local operator (Fig. 9 item 235), via the Local Off-Line Access Control interface unit (LOA) (Fig. 8 item 22) then creates a new request for validation to the Administration Management and Control (AMC) (Fig. 8 item 32) to commence the next stage of activation of the transportable Processing Reactor Plant (PRP) (Fig. 8 item 31) within his location.

[0230] Task E (Fig. 9 item 242) - The new validation request received by the Remote Monitoring and Control Centre (RMCC) (Fig. 8 item 43) is processed by the Administration Management and Control (AMC) (Fig. 8 item 32) and via the Applications Management Unit (R) interacts with the Authentication & Authorisation Application Engine (AAE) (Fig. 8 item 231) to commence the process and the initiation of authorisation codes for a Start Condition (Fig. 8 item 224).

[0231] Task F (Fig. 9 item 243) - On confirmation of the new validation request the Administration Management and Control (AMC) (Fig. 8 item 32) utilises the Authentication and Authorisation Application Engine (AAE), which in turn utilises the Master Artificial Intelligence Engine (MAIE) to access the various databases such as the Rules and Facts Database (R) (Fig. 8 item 41) and Secure Code Database (R) (Fig. 8 item 205) as well as the various Applications such as the Authorisation (R), Encryption (R) and Security (R) Applications (Fig. 8 item 131) and other Applications such as Encryption and Security Application Engine (ESE) (Fig. 8 item 230). The Master Artificial Intelligence Engine (MAIE) (item 37) responds back to the Administration Management and Control (AMC) (Fig. 8 item 32) with validation codes.

[0232] TaskG (Fig. 9 item 244) - The Administration Management and Control (AMC) (Fig. 8 item 32) communicates with the Processing Reactor Plant (PRP) (Fig. 8 item 31) via the Security Access Request and Control (SARC) (Fig. 8 item 208) which in turn sends a code ready to the Heartbeat Start (HBS) (Fig. 8 item 210) and also creates Sub-System Codes (SSC) (Fig. 8 item 211). The Security Access Requestand Control (SARC) (Fig. 8 item 208) activates the Heartbeat Start (HBS) (Fig. 8 item 210) sequence that commences the HB1 (Fig. 8 item 221) process that provides the first of three attempts, as an example and can be more if required, to start the Processing Reactor Plant (PRP) (Fig. 8 item 31).

[0233] Task H (Fig. 9 item 245) - The Heartbeat Sequence Control (HSC) (Fig. 8 item 219) provides the HB1 code (Fig. 8 item 221) combined with the Sub-System Codes (SSC) (Fig. 8 item 211) establishes the relevant Unit Sub-systems (Fig. 8 item 225). The HB1 code (Fig. 8 item 221) that is combined with the Sub-System Codes (SSC) (Fig. 8 item 211) into the Unit / Sub-Systemi (Fig. 8 item 225) produces Codei and the first Bucketi code (Fig. 8 item 226). Authenticated codes are then directed to the Unique Reactor Authentication Code (URA) (Fig. 8 item 218).

[0234] Task I (Fig. 9 item 246) - The Secu rity Access Request and Control (SARC) (Fig. 8 item 208) sends a code ready to the Bucket-Brigade Coding (BBC) (Fig. 8 item 212) to utilise the derived Bucket code (Fig. 8 item 226) and pass that code onto the next Unit / Sub-System2(Fig. 8 item 225) which then creates into the interchange (Fig. 8 item 227) and Bucket2code. This sequence is continued serially until the last Unit / Subsysterrin (Fig. 8 item 225) creates Codeninto the interchange (Fig. 8 item 227). All codes are directed to the Heart Response Code (HRC) (Fig. 8 item 229) including the generated Check Digit (Fig. 8 item 228C). Authenticated codes are then directed to the Unique Reactor Authentication Code (URA) (Fig. 8 item 218). Task J (Fig. 9 item 247) - The Security Access Requestand Control (SARC) (Fig. 8 item 208) sends an activation code ready directive to the Sequence Interchange Control (SIC) (Fig. 8 item 213) to rearrange and prepare Codei to Coden(Fig. 8 item 225) into a predetermined sequence order as determined by the Master Artificial Intelligence Engine (MAIE) (Fig. 8 item 37). This process establishes the interchange codes (Fig. 8 item 227) received from the Sub-systems codes (Fig. 8 item 225), as Codei,Code2,Code3,Code4and Coden(Fig. 8 item 225), in a predetermined arrangement, as Codeis,Code2S,Code3s,Code4Sand Codens plus the Check Digit (Fig. 8 item228C) as previously generated by Unit / Sub-Systemn(Fig. 8 item 228). The derived codes are then collated and become the Heartbeat Response Code (HRC) (Fig. 8 item 229). Authenticated codes as are then directed to the Unique Reactor Authentication Code (URA) (Fig. 8 item 218).

[0235] Task K (Fig. 9 item 248) - The Secu rity Access Request and Control (SARC) (Fig. 8 item 208) combines and collates the Processing Reactor Plant (PRP) (Fig. 8 item31) unique identifiers (Fig. 8 item 215), such as Plant IP Address, Plant Unique Numbers, Plant GPS Co-ordinates, the Number of Sub-Systems and any other available identifiers, and sends a code ready to the System Data Validation Codes (SDV) (Fig. 8 item 214). A Security Response Code (SRC) (Fig. 8 item 216) is then generated by combining the SDV with the Manufacturers Unique Code (Fig. 8 item 217) which then produces one part of the activation ready codes forthe Unique Reactor Authentication Code (URA) (Fig. 8 item 218).

[0236] Task L (Fig. 9 item 249) - Combining the Heartbeat Response Code (HRC) (Fig. 8 item 229) with the Security Response Code (SRC) (Fig. 8 item 216) forms the Unique Reactor Authentication Code (URA) (Fig. 8 item 218) which provides the validation codes back to the Secu rity Access Request and Control (SARC) (Fig. 8 item 208) that reports back to the Administration Management and Control (AMC) (Fig. 8 item 32) that all secu rity authentications are now secu rely activated and access to heartbeat activation can now proceed.

[0237] Task M (Fig. 9 item 250) - The Administration Management and Control (AMC) (Fig. 8 item 32) now has authenticated code to proceed to the Authentication & Authorisation Application Engine (AAE) (Fig. 8 item 231) via the Applications Manager (R) (Fig. 8 item 35) and then to the Master Artificial Intelligence Engine (MAIE) (Fig. 8 item 37) to confirm from the Rules and Facts Database (Fig. 8 item 41) and Security Code Database (Fig. 8 item 205) that all codes and responses are validated and correctly presented forthe appropriate local operator (Fig. 9 item 235) and the transportable Processing Reactor Plant (PRP) (Fig. 8 item 31) within that location. The response is provided back to the Administration Management and Control (AMC) (Fig. 8 item 32). If authorisation is a ‘FAIL’ the Administration Management and Control (AMC) (Fig. 8 item 32) sends request to the Security Access Request and Control (SARC) (Fig. 8 item 208) for the Heartbeat Start (HBS) (Fig. 8 item 210) to commence the second of three attempts to start the Processing Reactor Plant (PRP) (Fig. 8 item 31) by activating HB2 sequence (Fig. 8 item 221) within the Heartbeat Sequence Control (HSC) (Fig. 8 item 219) and repeats Task G (Fig. 9 item 244) to Task M (Fig. 9 item 250) again. Once a second ‘FAIL’ is achieved from Task G (Fig. 9 item 244) to Task M (Fig. 9 item 250) again then the final HB3 sequence is activated. On a third ‘FAIL’ the system shuts down and an alarm (Fig. 8 item 223) is raised.

[0238] Task N (Fig. 9 item 251) - From a positive code match the Administration Management and Control (AMC) (Fig. 8 item 32) now has access to connect with Business Process! ng Algorithms (BPA) (Fig. 8 item 233) and confirm the status of all business transactions, such as ‘Are all invoices by the Local Operator paid?’ and, if all such matters are in order, completes authentication requirements.

[0239] TaskO (Fig. 9 item 252) - Once all business transactions are in order and validated by the Business Processing Algorithms (BPA) (Fig. 8 item 233) the Administration Management and Control (AMC) (Fig. 8 item 32) connects to the Security Access Request and Control (SARC) (Fig. 8 item 208) and combines with the Heartbeat Sequence Control (HSC) (Fig. 8 item 219) to report a Pass System state (Fig. 8 item 222) as a START Condition (Fig. 8 item 224) for the Processing Reactor Plant (PRP) (Fig. 8 item 31) ora Fail System Alarm state (Fig. 8 item 220) as a STOP Condition (Fig. 8 item 223) for the Processing Reactor Plant (PRP) (Fig. 8 item 31).

[0240] Task P (Fig. 9 item 253) - The Administration Management and Control (AMC) (Fig. 8 item 32) creates a Start condition notification (Fig. 8 item 224) or a Fail Alarm notification (Fig. 8 item 223) to the operator (Fig. 9 item 235).

[0241] If the transportable Processing Reactor Plant (PRM) (Fig. 8 item 31) fails to reach the end of the authentication sequence at Task P (Fig. 9 item 253) the Processing Reactor Plant (PRM) (Fig. 8 item 31) is shut down and alarm procedures are investigated. This method allows the placement of unmanned Processing Reactor Plants (PRM) (Fig. 8 item 31) at remote locations and still allowing a Remote Monitoring and Control Centre (RMCC) (Fig. 8 item 43) to authenticate, activate or cease operations.

[0242] The process is implanted in an embedded subsystem where it is difficult to break into without destroying the code itself. This provides system security at the physical level and at the code level.

[0243] The ‘Heartbeat’ is a unique data stream generated by the Remote Monitoring and Control Centre (RMCC) (Fig. 8 item 43) and is based on an encrypted algorithm exclusive for each of the receiving units. The encrypted algorithm is then decrypted by the specific receiving Processing Reactor Plants (PRM) (Fig. 8 item 31).

[0244] The exclusive encrypted algorithm protects the Intellectual Property established within the design of the Processing Reactor Plants (PRM) (Fig. 8 item 31) as the ‘Heartbeat’ is used to synchronise all control functionality within each Processing Reactor Plants (PRM) (Fig. 8 item 31) on a daily, or any variable time based criteria.

[0245] The encryption method also provides confidentiality for the inner workings of the control system and inner functional workings of the Processing Reactor Plants (PRM) (Fig. 8 item 31). This security layer prevents any deliberate unauthorised duplication or imitation of the components within the Processing Reactor Plants (PRM) (Fig. 8 item 31).

[0246] The ‘Heartbeat’ security process will also ensure all Business Rules between the Local Operator and the Remote Monitoring and Control Centre (RMCC) (Fig. 8 item 43) are adhered to. As an example if the Local Operator invoices are not paid on time the RMCC has the ability to stop generating the ‘Heartbeat’ data stream required to activate the Processing Reactor Plants (PRM) (Fig. 8 item 31).

[0247] The process flow is divided between multiple responsible entities such as the Remote Monitoring and Control Centre (RMCC) (Fig. 8 item 43), the Processing Reactor Plants (PRM) (Fig. 8 item 31) and the Local Operator. Each one of the responsible entities has various authentication protocol layers associated with it. The overall security service is applied as a non-specific protocol layer that is not attached to any responsible entity.

[0248] The ‘Heartbeat’ becomes an encryption method that allows for the data to be encrypted and then to be made available to other parts of the system. In this example it is not intended for the encrypted data to be decrypted in other parts of the system. In this way the multiple data forms that are combined to form the ‘Heartbeat’ create an intelligible form that is used in subsequent process modules within the system. The use of algorithms within the central Administration Management and Control (AMC) (Fig. 8 item 32) provides the recovery of authentication data to protect the intellectual property and to ensure authorised use of process equipment.

[0249] The present embodiment’s unique ‘Heartbeat’ security mechanism is used to authenticate physical and human resources, to ensure data integrity, to enforce access rights and to maintain secrecy within routing channels. The Administration Management and Control (AMC) (Fig. 8 item 32) establishes and enforces all security policies within the Rules and Facts Databases (Fig. 8 item 41).

[0250] 7. ALGORITHMS

[0251] This embodiment relates to an innovative algorithm-based transportable autonomous reactor. The system utilises advanced algorithms to optimise waste processing operations, improve resource recovery, enhance efficiency, minimise environmental impact, interface to carbon credit exchanges, utilise blockchains and create digital smart contracts. Current waste processing methods often lack efficiency and environmental friendliness. The system integrates algorithmic processes, their implementation and examples of their application as outlined below:

[0252] 7.1. Input Feedstock Identification Algorithm:

[0253] The Input Matrix Control (IMC) (Figs. 2A& 2B item 7) and the Arrange Matrix Control (AMC) (Fig. 3 item 10) utilise the Input Feedstock Imaging and Identification Manager (IFI) (Fig. 5 item 50) with Local Artificial Intelligence Engine (A) (Fig. 5 item 11) and machine learningtechniques (Fig. 3 item 28M) to automatically compare, identify and sort different types of waste materials (Fig. 5) and (Figs. 2A & 2B item 1, Figs. 2A& 2B item 77). Examples include sensors and computer vision systems that classify recyclables, organic waste, and hazardous materials.

[0254] 7.2. Arrange Control Preparation Algorithm:

[0255] The Arrange Matrix Control (AMC) (Fig. 3 item 10) utilise the Input Feedstock Imaging and Identification Manager (IFI) (Fig. 5 item 50) with Local Artificial Intelligence Engine (A) (Fig. 5 item 11) and machine learning techniques (Fig. 3 item 28M) to automatically compare, identify and prepare the waste (Fig. 5 item 127) for conversion. Integrates various resource recovery Algorithms to identify the most efficient methods for recovering resources from specific waste streams. For instance, it determines moisture content, optimal temperatures, flow durations and particle sizes for the specific organic waste feedstock.

[0256] 7.3. Process Convert and Energy Conversion Algorithm:

[0257] The Conversion Reactors (Fig. 3 item 88) are controlled by the Convert Matrix Control (CMC) (Fig. 3 item 13) and configured via the Reactor Configuration Unit (Fig. 3 item 87) to provide low, medium, high or variable temperature conversion chambers (Fig. 3 item 88), (Fig. 3 item 88T) and activating a multitude of sensors (Fig. 3 item 88S)

[0258] 7.4. Process Control Algorithm:

[0259] The Process Matrix Control (PMC) (Fig. 5 item 21) manages the entire waste feedstock process from identification of input (Fig. 5 item 1), to arrangement of preparation (Fig. 5 item 2) and then the conversion (Fig. 5 item 3) in the most complete, efficient and effective manner.

[0260] 7.5. Output Control Algorithm:

[0261] To extract and store all available resources from the in put waste feedstock the Output Matrix Control (OMC) (Fig. 3 item 16) within the Output stage (Fig. 3 item 4) of the Process Matrix Control (PMC) (Fig. 3 item 21) relies on delivery of Bio-Gas (Fig. 3 item 95), Bio-Oil (Fig. 3 item 97), Blended Bio-Oil (Fig. 3 item 99), Infused Bio-Char (Fig. 3 item 99A), Bio-Char (Fig. 3 item 101) and Water (Fig. 3 item 103) from the Converter Unit (Fig. 3 item 3).

[0262] 7.6. Market Forces Algorithm:

[0263] To match and balance market demand (Figs. 2C& 2D item 72) to the optimal, configurable waste input feedstock (Figs. 2C & 2D item 3) and maximum available resource recovery (Figs. 2C & 2D item 4) the Market and Buyers unit (Figs. 2C& 2D item 44) utilises the Commodity Product (Output) unit (Figs. 2C & 2D item 70) and the Market Push Forces unit (Figs. 2C & 2D item 71) to balance the Market Pull Forces (Figs. 2C & 2D item 73). Accordingly, the Algorithms (R) (Figs. 2C & 2D item 36) and Storage (R) (Figs. 2C & 2D item 41) utilised by the Master Artificial Intelligence Engine (MAIE) (Figs. 2C& 2D item 37) can satisfy the buyers, maximise recovery and process the waste.

[0264] 7.7. Energy Control and Distribution Algorithm:

[0265] To monitor and control energy requirements and flow within the Processing Reactor Plant (PRP) (Figs. 2C & 2D item 31) the Energy Unit (Fig. 3 item 5) utilises the Energy Matrix Control unit (EMC) (Fig. 3 item 19) to activate the various turbines (Fig. 3 item 107) such as Gas, Oil, Fuel, Coal or Steam, to produces the required energy (Fig. 3 item 106) and then maintain the necessary flow as add to the requirement (Fig. 3 item 93), spare to the requirement (Fig. 3 item 92) or as storage (Fig. 3 item 105). Additionally, the Energy Generation Storage and Control unit (Fig. 11 item 5) provides energy output (Fig. 11 item 293) to the external Direct Energy Storage (Fig. 11 item 294) that combines the energy generated by Solar and Wind sources (Fig. 11 item 76E) to then process to the Energy Distribution Process unit (Fig. 11 item 287) to either connect to the Energy Power Grid (Fig. 11 item 288), to other external connections (Fig. 11 item 273) or used as External Energy to Transportable Units (Fig. 11 item 286).

[0266] 7.8. Sensors and Instrumentation Algorithms:

[0267] Referringto Fig. 12, to control data flows, from the on-board sensors 295, 296, 297, 298, 299, the external sensors 301X, the external instrumentation 301E or any combination of input data sources, the transportable Multi Modal Processing Reactor Plant (MMPRP) 31 utilises the Sensor, Instrumentation Interface and Control unit (SI IC) 303 and the Energy Distribution and Sensor Control unit (EDSC) 304 as either an external Output process 294 or is handled as an internal process via the Data Collector Unit (DCU) 305, the Scientific Laboratory Unit (SLU) 307, the Data Hosting Unit (DHU) 309 orthe Unified Comms Unit (UCU) 311.

[0268] 7.9. Laboratory Process Algorithm:

[0269] To collect, monitor and process samples and create data flows the T ransportable Scientific Laboratory Centre (SLC) (Fig. 11 item 284) is internally equipped as the Multi Modal Processing Reactor Plant (MMPRP) (Fig. 12 item 291, in simplified form for clarity) with integrated External Sensor, Instrumentation Interface and Control unit (Fig. 12 item 303) and the Energy Distribution Sensor Control module (Fig. 12 item 304) as either an external Output process (Fig. 12 item 294) or is handled as an internal process via a larger Scientific Laboratory unit (Fig. 12 item 307) only.

[0270] 7.10. Data Logs Algorithm:

[0271] Referringto Figs. 2A& 2B, the Processing Reactor Plant (PRP) 31 creates internal local Sensor Data Flow and Application Control functionality with each individual operating unit such as the Local A. I. (I) 8 for the Input unit 1, the Local A. I. (A) 11 forthe Arrange unit 2, the Local A. I. (C) 11 for the Convert unit 3, the Local A.I. (O) 17 forthe Output unit 4 and the Local A. I. (E) 20 forthe Energy unit 5. The Remote Monitoring and Control Centre (RMCC) 43 via the Administration Management and Control (AMC) 32 utilises Algorithms (R) 36, Applications (R) 35 and Rules and Facts (R) 41 to support the processes and functionality of the Master Artificial Intelligence Engine (MAIE) 37.

[0272] Referring to Fig. 7, the Master Multi-Path Matrix Manager (MMM) 24 also incorporates the Unit Cell Data unit (UCD) 179 which interrogates, via the Control Data Administration unit (CDA) 192, all sensor output data by the Unit Data and Log Collection 193. The Data Analysis Manager (DAM) item 200, within the Data Flow Engine (DFE) 199, provides analysis tools, historical trends and correction tools as used by the Data Flow Process Manager 203.

[0273] 7.11. Management and Administration Algorithm:

[0274] The control of security access and service routines is provided by the Authentication Control Manager (AMC) (Fig. 7 item 110) in association with the Security Manager (SCM) (Figs. 2A& 2B item 116). Each of these units reports to the Data Flow Process Manager (Fig. 7 item 203) and then via the Plant Communication and Control unit (PCC) (Fig. 7 item 25) are connected to the Remote Monitoring and Control Centre (RMCC) (Fig. 7 item 43). The Administration Management and Control (AMC) (Fig. 7 item 32) utilises Algorithms (R) (Figs. 2A& 2B item 36), Applications (R) (Figs. 2A & 2B item 35) and Rules and Facts (R) (Fig. 7 item 41) to support the processes and functionality of the Master Artificial Intelligence Engine (MAIE) (Fig. 7 item 37). The Data Analysis (R) unit (Fig. 7 item 133) also provides management and administration support with analytic tools, correlation tools, historical trends and strategic projections.

[0275] 7.12. Predictive Maintenance Algorithm:

[0276] The Local Support Services unit (Fig. 6 item 137) contains algorithms to support the Processing Reactor Plant (PR) (Fig. 6 item 31) with functionality for calibration, maintenance, access, updates, backups, installations and authentication. The Unit Cell Data unit (UCD) (Fig. 7 item 179) interrogates, via the Control Data Administration unit (CDA) (Fig. 7 item 192), all sensor output data by the Unit Data and Log Collection (Fig. 7 item 193). The Data Analysis Manager (DAM) (Fig. 7 item 200), within the Data Flow Engine (DFE) (Fig. 7 item 199), provides analysis tools, historical trends, correction tools and relevant targeted data to establish and predict equipment failures. Within the Remote Monitoring and Control Centre (RMCC) (Figs. 2A& 2B item 43) the Administration Management and Control (AMC) (Figs. 2A& 2B item 32) utilises Algorithms (R) (Figs. 2A& 2B item 36), Applications (R) (Figs. 2A& 2B item 35) and Rules and Facts (R) (Figs. 2A& 2B item 41) to provide automated maintenance routines. Proactive maintenance, reducing downtime and improving operational efficiency is achieved by analysing historical data.

[0277] 7.13. Communications Algorithm:

[0278] Management of internal communications within the Processing Reactor Plant (PRP) (Fig. 6 item 31) is controlled by the Master Multi-Path Matrix Manager (MMM) (Fig. 6 item 24) and managed by the Plant Communication and Control unit (PCC) (Fig. 6 item 25). The Multi-Path Comms Control unit (MCC) (Fig. 6 item 26) controls all communication activities such as Local Processing Plant Comms (LPPC) (Fig. 6 item 143) for local network selection (Fig. 6 item 146), Remote Processing Plant Comms (RPPC) (Fig. 6 item 142) for remote network selection (Fig. 6 item 145) and Independent Wireless Network Comms (IWNC) (Fig. 6 item 144) for various wireless services (Fig. 6 item 155) or Priority User Channel Selector (PUS) (Fig. 6 item 161) such as first responder users (Fig. 6 item 162). The Remote Monitoring and Control Centre (RMCC) (Fig. 6 item 43) via the Administration Management and Control unit (AMC) (Fig. 6 item 32) incorporates the Comms Processing Engine (CPE) (Fig. 6 item 177), the Comms Channel Allocation Manager (CAM) (Fig. 6 item 178) and the Master Artificial Intelligence Engine (MAIE) (Fig. 6 item 37) which interfaces with the Wireless Communication Access Point / Concentrator (Fig. 6 item 176). Ad hock indirect wire less neural communication paths (Fig. 13 items 333-335, 337, 327, 328, 322, 330, 338, 323, 324, 316, 314, 318, 320, 326) enhance the direct communication paths of the transportable Processing Reactor Plants (PRP) (Fig. 6 item 31) for data integrity, disaster recovery and first responder conditions (Fig. 13 items 331, 332336). The multipath communications (local short range wireless, long range wireless and satellite) ensures the built- in redundancy will deliver the data to its destination at all times.

[0279] 7.14. Security and Authentication Algorithm:

[0280] The authentication processing and heartbeat activation process within the Processing Reactor Plant (PRP) (Fig. 8 item 31) is within the Security Access Request and Control unit (SARC) (Fig. 8 item 208) that manages all the functionality relatingto the Heartbeat Start (HBS) code (Fig. 8 item 210) to the Heartbeat Sequence Control (HSC) (Fig. 8 item 219) to the Unit / Sub-System Codes (Fig. 8 item 225) into the Bucket Code (Fig. 8 item 226) then into the Sequence Interchange Code (Fig. 8 item 227) generating the codes (Fig. 8 item 228) required forthe Heartbeat Response Code (HRC) (Fig. 8 item 229). The generated Security Response Code (SRC) (Fig. 8 item 216) coupled with the Manufactures Unique Code (Fig. 8 item 217) produces the Unique Reactor Authentication Code (URA) (Fig. 8 item 218) to create the Start Condition (Fig. 8 item 224) or the Alarm Condition (Fig. 8 item 223).

[0281] 7.15. Carbon TradingAlgorithm:

[0282] This algorithm administers business rules and artificial intelligence agents in the control of carbon trading transactions. It is also equipped with smart contracts within blockchain digital ledgers that are directly connected to carbon trading exchanges. The Real Time Spot Carbon Trading Exchange (CTE) (Fig. 10 item 45), the Remote Monitoring and Control Centre (RMCC) (Fig. 10 item 43), the Compliance and Certification Authority (CCA) (Fig. 10 item 257) and the relationship with the Processing Reactor Plant (PRP) (Fig. 10 item 31) as it relates to the Output Commodity Product (Fig. 10 item 70) forthe buyer marketplace (Fig. 10 item 44). The Administration Management and Control (AMC) (Fig. 10 item 32) manages the Carbon Trading Algorithms (CTA) (Fig. 10 item 256) and via the Applications Management (Fig. 10 item 35) controls the Carbon Audit and T rading Engine (CATE) (Fig. 10 item 255). The Master Artificial Intelligence Engine (MAIE) (Fig. 10 item 37) manages the actions, activities and undertakings forthe Blockchain and Smart Contracts (Fig. 10 item 69). 8. DATA COLLECTION AND DATA PROCESSING SYSTEM

[0283] The present embodiment has application as an unmanned waste processing plant (Fig. 11 item 291), an environmental data collection unit (Fig. 12 item 291) an energy generation system (Fig. 11 item 291), a wireless communication hub, node or concentrator (Fig. 13 item 650, 651, 655) a digital signage unit (Fig. 13 item 653) a scientific laboratory (Fig. 11 item 284, fig 12 item 291). As a data collector (Fig. 12 item 291) the unmanned waste processing plant is able to capture the data (Fig. 12 item 303), process the data (Fig. 12 item 304) and deliver the data (Fig. 12 item 294, item 25, item 290), in real-time, from remote locations to a central Remote Monitoring and Control Centre (RMCC) (Fig. 7 item 43). The unmanned waste processing plant also has the ability to generate energy via the unit’s input feedstock processing plant (Fig. 12 item 31), the ability to store the generated energy (Fig. 12 item 5) and then to supplement the energy storage with additional renewable energy (solar and / or wind energy) (Fig. 12 item 76) provides self-powering and sustainable operation. The unmanned waste processing plant is activated from a central Remote Monitoring and Control Centre (RMCC) (Fig. 7 item 43), powered-up, create enough energy to sustain itself, collect data of environmental conditions, process localised data and communicate results back to the remote base.

[0284] To store sensor data (Fig. 7 item 192, item 196, item 40, item 207), process parameters (Fig. 7 item 115, item 206), A.I. models (Fig. 5 item 11, item 134) and quality metrics (Fig. 5 item 133), this embodiment is designed with robust and scalable databases (Fig. 5 item 135, item 41) with storage (Fig. 7 item 40, item 51) and retrieval functionality (Fig. 5 item 52, item 118). Data backup and recovery procedures (Fig. 5 item 132) are also implemented to prevent data loss. Localised sensitive information of input (Figs. 2A & 2B item 6), processes (Figs. 2A & 2B item 9), flow (Figs. 2A & 2B item 12) and output (Figs. 2A& 2B item 15) are protected by utilising data security (Figs. 2A& 2B item 38) and access control mechanisms (Figs. 2A& 2B item 32).

[0285] 9. SYSTEM ARCHITECTURE

[0286] The system architecture for one embodiment is designed to be robust, flexible and optimised for the Processing Reactor Plant (PRP) (Figs. 2A & 2B item 31), the Remote Monitoring and Control (RMCC) (Figs. 2A& 2B item 43), thermal converters (Figs. 2A & 2B item 3), energy generation and storage (Figs. 2A& 2B item 5), Internet of Things (loT) sensors (Fig. 7 item 179), Master Artificial Intelligence (MAI) (Figs. 2A & 2B item 37), localised Artificial Intelligence (Local A.I.) (Figs. 2A& 2B item 8, item 11, item 14, item 17, item 20), process control (Fig. 3 item 7, item 10, item 13, item 16, item 19), blockchain and smart contracts (Figs. 2A& 2B item 69) and machine learning (Figs. 2A & 2B item 28M). The design parameters are carefully selected and utilised in a specific way so that all the various integrated components are consideration for their interactions and functionality. The system requirements, as designed, provides efficient and reliable optimisation of pyrolytic waste feedstock processing (Figs. 2A & 2B item 3) while ensuring data accuracy (Figs. 2A& 2B item 199), environmental compliance (Fig. 10 item 257), security (Fig. 8 item 23) and operator safety (Fig. 6 item 139). Regular testing, maintenance, and updates (Fig. 6 item 137) are also part of the system's lifecycle management.

[0287] Below are system architecture considerations for this embodiment:

[0288] 9.1. loT Sensor Network:

[0289] Appropriate loT sensors are selected for placement within the Processing Reactor Plant (PRP) (Figs. 2A& 2B item 31), to generate data (Fig. 7 item 193) for measuring critical parameters such as temperature, pressure, flow rate, emissions, and material consistency (Figs. 2A& 2B item 6, item 9, item 12, item 15, item 18). Each of these sensors is either attached or embedded into optimal locations within the pyrolytic chamber (Fig. 3 item 88S) and along the feedstock input (Fig. 3 item 75) and output paths (Fig. 7 item 91). As data is generated (Fig. 7 item 114) the frequency at which the sensor data is collected is specified (e.g., seconds or milliseconds) (Fig. 7 item 192) for real-time monitoring (Fig. 7 item 194) based on the specific type of waste feedstock entering the process chamber (Fig. 7 item 115) and the type of output that is required (Fig. 10 item 70). Reliable real-time data transmission from sensors (Fig. 7 item 290) is provided to the Data Flow Process Manager (Fig. 7 item 203) by wired or wireless communication protocols (e.g., Wi-Fi, Ethernet, Bluetooth) from the Plant Communication and Control (PCC) (Fig. 7 item 25) on demand or as needed.

[0290] 9.2. Central Processing Manager:

[0291] The high-performance Master Multi-Path Matrix Manager (MMM) (Fig. 4 item 24), utilised within each Processing Reactor Plant (PRP) (Figs. 2A & 2B item 31), is capable of managing (Fig. 4 item 108), processing (Fig. 4 item 111), securing (Fig. 4 item 117), authenticating (Fig. 4 item 117), interfacing (Fig. 4 item 109) and synchronising (Fig. 4 item 108) large volumes of data in real-time and storing that data in a centralised local database repository (Fig. 4 item 28). Local algorithms (Figs. 2A& 2B item 8, item 11, iteml4, item 17, item 20) are designed to fuse the relevant data from multiple sensors and provide the Master Multi-Path Matrix Manager (MMM) (Fig. 4 item 24) a comprehensive view of the total activities and processes within the transportable waste processing plant. Data pre-processing techniques such as filtering, noise reduction, and outlier detection are utilised to ensure data accuracy (Fig. 7 item 200). Plant-wide A.I. generated algorithms (Fig. 4 item 24A) are integrated into the total system for real-time decision-makingand control. As new data variances and differences occurthe A.I. uses Machine Learning techniques (Figs. 2A& 2B item 28M), to either confirm, update orenhance existing process information previously stored in the transportable waste processing plant data repository (Fig. 4 item 28). Any such changes to the transportable waste processing plant’s data repository (Fig. 4 item 28) are uploaded by the Plant Communication and Control (PCC) (Fig. 6 item 25) via available communication networks (Fig. 6 item 138) to the Remote Monitoring and Control (RMCC) (Figs. 2A& 2B item 43), for acceptance, rejection or storage. 9.3. Pyrolytic Process Chamber:

[0292] Organic waste processing conversion reactor 88 of Fig. 3 is configured to accommodate various waste feedstock (Fig. 3 item 74), types (Fig. 3 item 77), sizes (Fig. 3 item 50) and moisture content (Fig. 3 item 82). A precise temperature monitoring and control system is implemented (Fig. 3 item 88S), which includes heating elements and cooling mechanisms to regulate the required temperature values at specific preidentified positions alongthe processing chamber. Pressure regulation components are also integrated into the processing chamber to maintain desired pressure levels. The processing chamber incorporates a Flow Control System (FC) (Fig. 3 item 13F), via the Convert Matrix Control (CMC) (Fig. 3 item 13), that normalises, controls and regulates the flow rate of the converter process (Fig. 3 item 3) and then the output process (Fig. 3 item 4). This functionality is monitored by the loT sensors (Fig. 3 item 88S) and the movement of the Conversion Reactor’s (Fig. 3 item 88) physical internal auger mechanism (Fig. 16 item 476).

[0293] 9.4. Artificial Intelligence Control:

[0294] Various embodiments have an architecture that incorporates localised A.I. (Figs. 2A& 2B item 8, item 11, iteml4, item 17, item 20) in conjunction with centralised plant-wide A.I. (Fig. 4 item 24A) forthe optimisation of processingof organic waste feedstock and the integration of artificial intelligence algorithms with loT sensors (Fig. 3 item 88S), processes (Fig. 3 item 21) and administration (Figs. 2A& 2B item 24). The incorporation of these elements into the architecture, creates a robust and intelligent waste processing system that optimises organic waste feedstock conversion (Figs. 2A& 2B item 3), while adapting to varying conditions and future advancements. Regular monitoring, feedback analysis, and iterative improvements to the A.I. models facilitate continual optimisation of the waste process (Figs. 2A& 2B item 3) and maximises resource recovery (Figs. 2A& 2B item 4). The Machine Learning algorithms (Figs. 2A& 2B item 28M) are trained on historical data (Fig. 7 item 133, item 133A, item 207) to predict optimal waste processing parameters (Fig. 7 item 206). The localised A.I. generated algorithms (Figs. 2A& 2B item 8, item 11, iteml4, item 17, item 20) are designed to fuse the relevant data from multiple sensors and provide the Master Multi-Path Matrix Manager (MMM) (Fig. 4 item 24) with a comprehensive view of the total activities and processes within the transportable waste processing plant. Plant-wide A.I. generated algorithms (Fig. 4 item 24A) are integrated into the total system for real-time decision-making and control.

[0295] Thus, in various the organic waste processing system produces bio-char, bio-oil and bio-gas under the control of an A.I. system, owingto the complex and dynamic nature of the processing. The control parameters for the Processing Reactor Plant (Fig. 2A item 31) relate to data derived from, amongst others, temperature, pressure and flow parameters.

[0296] As an example, the design of one such system using an A.I. methodology, including the process and algorithms used, is as shown in Fig. 21. This A.I. based system optimises the organic waste process for producing high-quality bio-char, bio-oil, and bio-oil by controlling temperature, pressure, and flow rates. Of paramount importance is the quality of the output (by way of the bio-char, bio-oil and biogas), yield efficiency, energy consumption, process stability and other identifiable variables.

[0297] Real-time data is collected (Fig. 7A item 114) from the available temperature sensors, pressure sensors, flow meters and any other sensors and possibly chemical composition analysers within the converter reactor units (Fig. 3B item 88). Historical data of process parameters and output quality is controlled by the Process Data Manager (PDM) (Fig. 7A item 115) the Data Flow Engine (DFE) (Fig. 7A item 199) and processed by the Data Analysis Manager (DAM) (Fig. 7A item 200). The data is screened and cleaned, within the Relevant and Targeted Data unit (Fig. 7A item 201) to correct for any missing values, duplicates noise, and outliers.

[0298] The Local Filtered Data unit (Fig. 7A item 202) ensures data accuracy, completeness and normalises or standardises the data to make it suitable for analysis. It also extracts features relevant to any process optimisation such as trends, patterns or other requirements. The data is then divided into training, validation and test sets for modelling purposes as it interfaces with the Data Flow Process Manager (Fig. 7A item 203) within the Process Task Manager (PTM) (Fig. 7A item 111).

[0299] Algorithm Selection and Model Building

[0300] 9.4.1. Control System Configuration:

[0301] 9.4.1.1 Dynamic Modelling Control DMC) - A mathematical model of the pyrolytic process is developed by the Process Data Manager (PDM) (Fig. 7A item 115) by using system identification techniques within the Master Multi-Path Matrix Manager (MMM) (Fig. 7A item 24). Models could be linear or nonlinear, depending on the complexity of the desired process as derived by the Process Matrix Control (PMC) (Fig. 7A item 21).

[0302] 9.4.1.2 Control Algorithms (CA) - The system of various embodiments implements Proportional- Integral-Derivative (PID) controllers for basic control of temperature, pressure, and flow parameters from the Unit Cell Data origination unit (fig.7A item 114). The PID parameters are tuned, to achieve the desired stability and responsiveness, controlled by the Control Data Admin unit (CDA) (Fig. 7A item 192), managed by the A.I. Edge Manager unit (AIEM) (Fig. 7A item 111E) and to iteratively learn the process by the Machine Learning unit (ML) (Fig. 7A item HIM).

[0303] 9.4.1.3 Model Predictive Control (MPC) - The system of various embodiments is capable of more advanced process control such as the Model Predictive Control (MPC), as an example, available within the Data Flow Engine (DFE) (Fig. 7A item 199) by using the Data Analysis Manager (DAM) (Fig. 7A item 200) with tools for analysis, trends, correlation etc. (Fig. 7A item 133). MPC predicts future states based on a model of the process and optimises control actions via the Control Data Admin (CDA) (Fig. 7A item 192) over a prediction horizon to achieve the desired outcomes for the Processing Reactor Plant (PRP) (Fig. 7A item 31). The system can train a regression model to predict the desired bio-char quality from the process parameters of the origination Unit Cell Data (UDC) (Fig. 7A item 114).

[0304] 9.4.2. Al and Machine Learning Models:

[0305] 9.4.2.1 Supervised and Unsupervised Machine Learning - The system of various embodiments uses Regression Techniques such as Linear Regression, Polynomial Regression or Support Vector Regression, to predict bio-char, bio-oil and bio-gas output quality based on data from the origination Unit Cell Data unit (UDC) (Fig. 7A item 114). Also Classification Models are used to classify the quality of outputs (e.g., high / medium / low quality bio-char), by means of classification algorithms like Random Forests, Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Gradient Boosting Machines, or Neural Networks such as Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Transformers. Unsupervised Machine Learning, within the Processing Reactor Plant (PRP) (Fig. 7A item 31), utilises clustering methods such as K-Means, Hierarchical Clustering or DBSCAN via the A.I. Edge Manager (AIEM) (Fig. 7A item 111E). Dimensionality Reduction methods such as Principal Component Analysis (PCA) or t-Distributed Stochastic Neighbour Embedding (t-SNE) are incorporated via the Process Data Manager (PDM) (Fig. 7A item 115).

[0306] 9.4.2.2 Reinforcement Learning - The system of various embodiments uses Policy Optimisation techniques to implement reinforcement learning algorithms such as Q-Learning, Deep Q-Networks (DQN), Policy Gradients, Actor-Critic Methods or Proximal Policy Optimisation (PPO) to learn optimal control policies. The localised A.I. Edge Manager (AIEM) (Fig. 7A item 111E) facilitates local A.I. agents to learn to optimise process parameters by receiving feed back on performance (rewards) from the local Data Flow Process Manager (Fig. 7A item 111). The Remote Monitoring and Control Centre (RMCC) (Fig. 7B item 43) collects data from all local Processing Reactor Plants (PRP) (Fig. 7A item 31) and utilises the Master Artificial Intelligence Engine (MAIE) (Fig. 7B item 37) to apply reinforcement learningto optimise control strategies at localised PRPs.

[0307] 9.4.2.3 Anomaly Detection - The system of various embodiments uses the central Data Processing Algorithm unit (DPA) (Fig. 7B item 206), the Historical Trend Database (R) unit (Fig. 7B item 207) and the Rules & Facts Database (R) (Fig. 7B item 41) for anomaly detection technique analysis (e.g., Isolation Forests, One-Class SVM) to identify and handle deviations from normal or unsupervised process behaviour.

[0308] 9.4.3. Model Evaluation:

[0309] The system of various embodiments tests the created models in a simulated environment, pre- production run basis, to evaluate their performance before deployment into actual waste processing activities. Performance Metrics for accuracy and precision such as Mean Squared Error (MSE) for regression, Fl Score for classification or Confusion Matrix are deployed within the Data Processing Algorithms (DPA) (Fig. 7B item 206), or classification accuracy, precision, and recall for classification tasks. Evaluation of the effectiveness of the control algorithms using stability and tracking performance is performed by the Data Analysis (R) unit (Fig. 7B item 133A) and the Correlation Tools (Fig. 7B item 133A). Cross-validation is performed by the Rules & Facts Database (R) (Fig. 7B item 41) by using techniques such as k-Fold Cross-Validation to ensure that models generalise well to unseen data and is not overfitting. Continual refinement of the models is implemented via the Machine Learning unit (ML) (Fig. 7A item HIM).

[0310] 9.4.4. HyperparameterTuning:

[0311] The system of various embodiments tunes hyperparameters such as learning rate, number of layers, regularisation strength etc., for machine learning models via the Process Data Manager (PDM) (Fig. 7A item 115) and controls algorithms via the A.I. Edge Manager (AIEM) (Fig. 7A item 111E) using techniques like Grid Search or Random Search. For reinforcement learning, the Machine Learning unit (ML) (Fig. 7A item 111M) adjusts learning rates, reward functions, and exploration strategies. The system also uses Bayesian Optimisation or Hyperband for more efficient automated hyperparameter tuning.

[0312] 9.4.5. System Integration and Deployment:

[0313] The A.I. models are incorporated into the remote Processing Reactor Plants (PRP) (Fig. 7A item 31) via the A.I. Edge Manager (AIEM) (Fig. 7A item 111E) as well as into the Remote Monitoring and Control Centres (RMCC) (Fig. 7B item 43) via the Master Artificial Intelligence Engine (MAIE) (Fig. 7B item 37) and control algorithms into the organic waste processing control system.

[0314] Within the Remote Monitoring and Control Centre (RMCC) (Fig. 7B item 43) communications between the A.I. systems and the process control systems is controlled by the Applications Management (R) unit (Fig. 7B item 35) within the Administration Management and Control unit (AMC) (Fig. 7B item 32). In each Processing Reactor Plant (PRP) (Fig. 7A item 31) communications between the A.I. systems and the process control systems is controlled via the Plant Communication and Control (PCC) (Fig. 7A item 25) and the Data Flow Process Manager (DFPM) (Fig. 7A item 31). Each of these sub-systems provides realtime data, analytics, exchange information and control functionality.

[0315] 9.4.6. Monitoring and Maintenance:

[0316] The system of various embodiments continuously monitors process parameters, performance monitoring and processed output quality in real-time via the Master Multi-Path Matrix Manager (MMM) (Fig. 7A item 24). Process and system performance metrics, such as process stability, efficiency, and quality consistency are collected, tracked, analysed, actioned and stored via the Data Analysis Manager (DAM) (Fig. 7A item 200). The model is retrained or updated via the Master Artificial Intelligence Engine (MAIE) (Fig. 7B item 37), with new data to adapt to changes in the process or input organic waste material. The system also incorporates a feedback loop, via the Data Analysis (R) unit (Fig. 7B item 35), to refine control strategies and models based on ongoing performance and new data. 9.4.7. Ethical and Safety Considerations:

[0317] The system of various embodiments includes safeguards to ensure that the A.I. systems do not compromise the safety of the process via the Interface Tasks Manager (ITM) (Fig. 4 item 109) and the Process Tasks Manager (PTM) (Fig. 4 item 111). Safety protocols and fail-safes to handle unexpected scenarios are an integral part of the system via the Energy, Synchronisation and Control Manager (ESM) (Fig. 4 item 108). Transparency in the decision-making process of the A.I. systems is maintained via the Rules & Facts Database (R) (Fig. 7B item 41). Model choices, control strategies, and performance evaluations are documented, updated and controlled via the Administration Management and Control (AMC) (Fig. 7B item 32). Regulatory compliance, relevant industrial regulations and standards for process control and safety, bias and fairness are all managed by the Master Artificial Intelligence Engine (MAIE) (Fig. 7B item 37) and maintained within the Rules & Facts Database (R) (Fig. 7B item 41).

[0318] 9.5. Operator Control Information (User Interface):

[0319] Within the Processing Reactor Plant (PRP) (Figs. 2A& 2B item 31), the display information presented to a local operator (Fig. 6 item 139) plays a critical role in facilitating efficient monitoring and control of the entire process (Fig. 6 item 137). The operator's display serves as a comprehensive interface that empowers the operatorto monitorthe process (Fig. 3 item 21), make real-time adjustments (Fig. 6 item 137), and ensure that the output meets quality standards (Figs. 2A& 2B item 35) while maintain! ng safety and environmental compliance (Fig. 10 item 257). The display is user-friendly, intuitive, and provides clear visualisations and alerts for efficient operation and troubleshooting. The types of information that is presented on the operator's display includes, but not limited to:

[0320] 9.5.1. The User Interface Dashboard:

[0321] The User Interface Dashboard provides the operator with a real-time dashboard displaying sensor data ((Fig. 3 item 88S), Ai-d riven adjustments (Fig. 5 item 11) and the general status of the waste conversion process (Figs. 2A& 2B item 3). System status indicates whether the system is operational, in standby, or in maintenance mode (Fig. 6 item 137). The Real-time Data provides key real-time parameters such as temperature, pressure, and flow rate inside the waste conversion chamber (Fig. 7 item 179). The Input Feedstock Information displays details about the organic waste feedstock, including its type, quantity, and origin (Fig. 3 item 1). The Data Analysis Manager (DAM) (Fig. 7 item 200) provides information on realtime quality indicators such as thermal properties, material consistency and other relevant metrics as collected from the Unit Cell (Data Origination) unit (UCD) (Fig. 7 item 179).

[0322] 9.5.2. Sensor Data Visualisation:

[0323] The Temperature Profiles are shown as graphs or charts depicting temperature distributions within the conversion reactor chamber (Fig. 37 item 88T), whereas the Pressure Trends are graphs illustrating pressure changes overtime (Fig. 7 item 196). The Flow Characteristics show flow rate data displayed in graphical or tabular format (Fig. 3 item 13F). The Sensor Status is shown as icons or indicators showing the operational status of individual loT sensors (Fig. 3 item 88S), including any faults or anomalies.

[0324] 9.5.3. A.I. Control Panel:

[0325] The Local Support Services (off-line) display unit (Fig. 6 item 137) provides control functions for setting desired parameters, such as target temperatures and pressure levels expressed and displayed as Set Point adjustments. The A.I. Control Mode allows the local operator to set constraints, view A. I. recommendations, and manually override A.I. decisions if necessary, to switch between manual and automated pre-set A.I. controlled modes.

[0326] 9.5.4. Process Timeline:

[0327] The Processing Duration, derived from the Process Data Manager (PDM) (Fig. 7 item 115), displays the time elapsed since the start of the waste conversion process (Figs. 2C& 2D item 3) and the Time Remaining for the current processing cycle based on historical data and A.I. predictions (Fig. 7 item 133, item 207, item 133A).

[0328] 9.5.5. Quality Assurance:

[0329] Quality thresholds are provided as visual indicators or numeric values showingwhetherthe output meets predefined quality standards (Fig. 3 item 4). The Quality Trends are historical charts tracking quality metrics over multiple processing cycles (Fig. 7 item 133, item 207, item 133A).

[0330] 9.5.6. Alerts and Notifications:

[0331] Alarms are incorporated within the design for immediate visual and auditory alerts for critical issues like sensor failures, safety breaches, unauthorised access, system anomalies or deviations from pre-set quality standards (Fig. 6 item 137) and (Fig. 8 item 223). Warnings are provided for non-critical notifications for conditions that may require operator attention. Alerts are also incorporated to suggest when the retraining for the A.I. model is required and when there are process deviations.

[0332] 9.5.7. Environmental Metrics:

[0333] The Emissions Monitoring feature displays output data on emissions, including pollutants and greenhouse gases, to ensure compliance with environmental regulations (Fig. 10 item 265). The Resource Efficiency metrics relate to internal energy consumption, water usage, or other resources, as applicable (Fig. 10 item 264). The Input Feedstock feature displays the condition of input material and related data (Fig. 10 item 263).

[0334] 9.5.8. Maintenance and Troubleshooting:

[0335] The Maintenance Schedule incorporates reminders for routine maintenance tasks and inspections while the T roubleshooting Guides provide access to troubleshooting guides or manuals for diagnosing and resolving any common process or product issues (Fig. 6 item 137).

[0336] 9.5.9. User Controls:

[0337] The Start / Stop buttons initiate and terminate the waste conversion process. The Emergency Shutdown button provides immediate shutdown in case of emergencies (Fig. 6 item 137).

[0338] 9.5.10. Historical Data and Reports:

[0339] The Data Logging function (Fig. 7 item 193) collects, processes, and stores data for analysis and compliance reporting (Fig. 7 item 133). Access to detailed reports summarising the performance and quality metrics of past processing cycles and reports on historical data is provided by the Data Analysis (R) functionality (Fig. 7 item 133A). Integrated data analytics are used to gain insights from historical data, optimising any A. I. models and improving the waste conversion process further (Fig. 7 item 206).

[0340] 9.5.11. User Authentication and Permissions:

[0341] The Processing Reactor Plant (PRP) (Fig. 8 item 31) provides user Login Credentials for secure user authentication to ensure only authorised personnel can access and control the plant. The Security Authentication and Heartbeat unit (SAH) (Fig. 8 item 23) provides highly secure access control (Fig. 8 item 208) with Unique Reactor Authentication Code generation (URA) (Fig. 8 item 218). Different levels of access and permissions for operators, supervisors, and administrators are controlled by the User Roles functionality (Fig. 6 item 139).

[0342] 9.5.12. Help and Support:

[0343] Access to user manuals, guides, and documentation is provided within the system as well as contact information for technical support, customer support or assistance (Fig. 6 item 137).

[0344] 9.6. Data Storage and Management:

[0345] To store sensor data (Fig. 7 item 192, item 196, item 40, item 207), process parameters (Fig. 7 item 115, item 206), A.I. models (Fig. 5 item 11, item 134) and quality metrics (Fig. 5 item 133), robust and scalable databases (Fig. 5 item 135, item 41) are desirably employed, with storage (Fig. 7 item 40, item 51) and retrieval functionality (Fig. 5 item 52, item 118). Data backup and recovery procedures (Fig. 5 item 132) are also implemented to prevent data loss. Localised sensitive information of input (Figs. 2A& 2B item 6), processes (Figs. 2A & 2B item 9), flow (Figs. 2A & 2B item 12) and output (Figs. 2A & 2B item 15) are protected by utilising data security (Figs. 2A& 2B item 38) and access control mechanisms (Figs. 2A& 2B item 32).

[0346] 9.7. Communication and Integration: Various embodiments incorporate a Multi-Path Comms Control unit (MCC) (Fig. 6 item 26) as an external systems integration functionality that allows seamless data exchange with external systems or databases for data sharing and reporting. The MCC unit also controls the Remote Processing Plant Communications (RPPC) (Fig. 6 item 142) for remote networks (Fig. 6 item 145), the Local Processing Plant Communications (LPPC) (Fig. 6 item 143) for local networks (Fig. 6 item 146) and the Independent Wireless Network Communications (IWNC) (Fig. 6 item 144) for diversified and extended communication pathways. The MCC unit also establishes connectivity to remote networks (Fig. 6 item 138) and other Processing Reactor Plants (PRP) (Fig. 6 item 31). In this way an ad hoc wireless communication network is established (Fig. 13) which includes connectivity to satellites (Fig. 13 item 313), satellite dishes (Fig. 13 item 315), wireless towers (Fig. 13 item 321), internet gateways (Fig. 13 item 319) and emergency services vehicles (Fig. 13 item 331, item 332, item 336). A remote monitoring functionality within the Multi-Path Comms Control unit (MCC) (Fig. 6 item 26) enables remote monitoring and control capabilities with the Remote Monitoring and Control (RMCC) (Fig. 13 item 43) and off-site and remote based personnel (Fig. 6 item 137).

[0347] 9.8. Environmental Compliance:

[0348] Emissions monitoring sensors may be closely integrated (Fig. 10 item 257) to ensure the Processing Reactor Plant (PRP) (Fig. 10 item 31) is compliant with environmental regulations and any Certification Authority (Fig. 10 item 261). The system structure and functionality incorporates compliance reports and the sharing of data with regulatory bodies as required (Fig. 10 item 255).

[0349] 9.9. Redundancy and Failover:

[0350] To minimise downtime in case of failures the Processing Reactor Plant (PRP) (Fig. 6 item 31) incorporates redundancy of critical components with access from the Local Support Services unit (Fig. 6 item 137). In addition, failover mechanisms are an integral part of the system to ensure continuous operation and data integrity.

[0351] 9.10. Scalability:

[0352] The architecture of various embodiments is scalable to accommodate varying feedstock volumes and to accommodate future expansion. This incorporated design feature is essential for the long-term viability and efficiency of the pyrolytic processing system. Scalability considerations within the system offers varying feedstock volumes, supports future expansion and provides technological advancements. Regular monitoring and optimisation is an inherent part of the ongoing maintenance to ensure the scalability goals are met as the system evolves.

[0353] The scalability is achieve by reference to at least these considerations and parameters:

[0354] 9.10.1. Modular Design:

[0355] One embodiment incorporates the entire Processing Reactor Plant (PRP) (Fig. 6 item 31) into a standard shipping container (Fig. 11 item 291), of various sizes, so that it can be transported to any waste generating site to process the waste locally. The system is divided into modular components that relate to sensors (Fig. 7 item 179), data processing units (Fig. 7 item 115), and the conversion reactor chamber (Fig. 3 item 88). Each module within the system has well-defined interfaces (Fig. 4 item 109) and communication protocols (Fig. 6 item 26) that allows for easy replacement or addition of components.

[0356] 9.10.2. Parallel Scaling:

[0357] To accommodate available increase of total volume of waste feedstock the Processing Reactor Plant (PRP) (Fig. 3 item 31) can be combined so that they operate in parallel (Fig. 6 item 136). This processing arrangement has infinitely scalable capability. Each of the PRP’s additionally incorporates an automated input sensing mechanism to handle increased input feedstock volumes (Fig. 3 item 1) via the input Matrix Control unit (IMC) (Fig. 3 item 7). The scaling is achieved by adding more instances of components to handle the increased feedstock volumes as it enters each processing unit. The processing load is measured and automatically distributed across multiple functional components such as CPUs, sensors, and chambers to ensure the waste processing is consistent and distributed overall the available resou rces.

[0358] 9.10.3. Load Balancing:

[0359] Load balancing mechanisms are implemented into the Processing Reactor Plant (PRP) (Fig. 3 item 31), via the Control Data Admin unit (CDA) (Fig. 7 item 192), to evenly distribute incoming data, collected from IOT sensors (Fig. 7 item 180, item 182, item 184, item 186, item 188, item 190) and processing tasks (Fig. 7 item 115) across all the available resources. This load balancing process dynamically allocates the required resources based on real-time processing demands.

[0360] 9.10.4. Elasticity:

[0361] Various embodiments use a localised edge based configuration A.I. Edge Manager (AIEM) (Fig. 7 item 111E), within the Unit Cell Data (UCD (Fig. 7 item 114) and managed by the Master Multi-Path Matrix Manager (MMM) (Fig. 7 item 24) and (Fig. 4 item 24) to interface (Fig. 4 item 109), process data (Fig. 4 item 115), and localised routine system tasks (Fig. 4 item 111), rather than a centralised remote cloud-based system. This configuration structure can rapidly determine and resolve localised issues and to automatically synchronise (Fig. 4 item 108) orscale up ordown based on demand. This solution can dynamically allocate localised resources as needed.

[0362] 9.10.5. Database Scalability:

[0363] To increase throughput, the database system (Fig. 7 item 115) may support horizontal scaling with distributed databases (Fig. 5 item 40, item 41, item 135, item 41, item 51, item 128), and replication to distribute data storage (Fig. 7 item 40). This approach allows for the addition of more sensors (Fig. 7 item 179), process managers (Fig. 7 item 115), or A.I. modules (Fig. 4 item 24A) based on demand.

[0364] 9.10.6. Message Queue:

[0365] To decouple components and handle data flows efficiently, consistently and cost effectively, the Data Flow Engine (DFE) (Fig. 7 item 192), incorporates a message queuingsystem with queues buffering data during high loads and allow asynchronous processing.

[0366] 9.10.7. Monitoring and Auto-Scaling:

[0367] Monitoring tools are incorporated into the Processing Reactor Plant (PRP) (Fig. 7 item 31) configuration to track system performance (Fig. 7 item 133A), resource utilisation (Fig. 7 item 41), A.I. model accuracy (Fig.

[0368] 4 item 24A), response times (Fig. 7 item 199), and any other process bottlenecks (Fig. 7 item 115). Autoscaling policies are implemented within the Rules functionality (Fig. 7 item 41) that trigger additional resource allocation based on predefined thresholds (Fig. 3 item 31).

[0369] 9.10.8. APIs and Micro services:

[0370] Various embodiments have micro services architecture, where each service handles specific functionalities. APIs are utilised to facilitate communication between micro services, allowing for easier replacement or expansion of individual services.

[0371] 9.10.9. Caching:

[0372] Caching mechanisms may be implemented within the embodiments, via the Process Data Manager (PDM) (Fig. 7 item 115) to reduce the need for repeated data processing and in-memory cachingforfrequently accessed data.

[0373] 9.10.10. Documentation and Knowledge Transfer:

[0374] Detailed and methodical documentation of the system’s architecture, components, and scalability strategies is compiled and maintained within the Local Support Services unit (Fig. 6 item 137). This feature ensures knowledge transferto new team members, personnel responsible forfuture expansion and onsite operators.

[0375] 9.10.11. Testingand Simulation:

[0376] Testing and simulation environments are developed and stored within the Local Support Services unit (Fig. 6 item 137) to evaluate the system's scalability under various waste feedstock inputs, normal loads, excessive loads or environmental stress conditions. Performance testing tools are used to identify bottlenecks and optimise resource allocation.

[0377] 9.10.12. Compliance and Environmental Considerations: Any expansion or modifications requirements are processed through the Modification Request Process Application (Figs. 2A& 2B item 35). to ensure that all such updates or modifications comply with environmental regulations and safety standards (Fig. 10 item 257).

[0378] 9.11. Maintenance and Support:

[0379] Various embodiments include provision for remote diagnostics, security and maintenance within the Local Support Services unit (Fig. 6 item 137) to minimise downtime and maintain secure operations. The Remote Monitoring and Control Centre (RMCC) (Fig. 6 item 43) has direct access, via the inbuilt satellite communications dish (Fig. 11 item 290) on top of each Processing Reactor Plant (PRP) (Fig. 3 item 31), to monitor and control any system function. Documentation and support resources for operators and maintenance personnel is also available via the Local Support Services unit (Fig. 6 item 137) or the nominated online website.

[0380] 9.12. Security and Access Control:

[0381] Strong and robust security measures may be maintained to protect the embodiments of the invention against potential vulnerabilities. Security checks and controls are automatically enabled and implemented (Fig. 6 item 139) based on local operator type, time, location, process and waste feedstock types, as examples. Automated access controls, via the Security Authentication and Heartbeat (SAH) (Fig. 8 item 23) are implemented to ensure that only authorised personnel can make changes to the system and the system is protected from unauthorised access and cyber threats.

[0382] 10. NETWORKAND COMMUNICATION SYSTEM

[0383] Various embodiments incorporate a Plant Communication and Control (PCC) (Fig. 6 item 25) for seamless data interchange, remote network connectivity (Fig. 6 item 138), via satellite, wireless or fixed networks, to local networks (Fig. 6 item 140), to additional Processing Reactor Plants (PRP) (Fig. 6 item 136), for local operator access (Fig. 6 item 139), for security and authentication processing (Fig. 6 item 23) and forVirtual Private Network (VPN) Gateway and IP Secure Tunnelling (Fig. 6 item 141).

[0384] The Multi-Path Communications Control (MCC) (Fig. 6 item 26) controls the Remote Processing Plant Communications (RPPC) (Fig. 6 item 142) for remote networks (Fig. 6 item 145), the Local Processing Plant Communications (LPPC) (Fig. 6 item 143) for local networks (Fig. 6 item 146) and the Independent Wireless Network Communications (IWNC) (Fig. 6 item 144) for diversified and extended communication pathways. The MCC unit also establishes connectivity to remote networks (Fig. 6 item 138) and other Processing Reactor Plants (PRP) (Fig. 6 item 31). In this way an ad hoc wireless communication network is established (Fig. 13) which includes connectivity to satellites (Fig. 13 item 313), satellite dishes (Fig. 13 item 315), wireless towers (Fig. 13 item 321), internet gateways (Fig. 13 item 319) and emergency services vehicles (Fig. 13 item 331, item 332, item 336). A remote monitoring functionality within the Multi-Path Comms Control unit (MCC) (Fig. 6 item 26) enables remote monitoring and control capabilities with the Remote Monitoring and Control (RMCC) (Fig. 13 item 43) and off-site and remote based personnel (Fig. 6 item 137).

[0385] Management of internal communications within the Processing Reactor Plant (PRP) (Fig. 6 item 31) is controlled by the Master Multi-Path Matrix Manager (MMM) (Fig. 6 item 24) and managed by the Plant Communication and Control unit (PCC) (Fig. 6 item 25). The Multi-Path Comms Control unit (MCC) (Fig. 6 item 26) controls all communication activities such as Local Processing Plant Comms (LPPC) (Fig. 6 item 143) for local network selection (Fig. 6 item 146), Remote Processing Plant Comms (RPPC) (Fig. 6 item 142) for remote network selection (Fig. 6 item 145) and Independent Wireless Network Comms (IWNC) (Fig. 6 item 144) for various wireless services (Fig. 6 item 155) or Priority User Channel Selector (PUS) (Fig. 6 item 161) such as first responder users (Fig. 6 item 162). The Remote Monitoring and Control Centre (RMCC) (Fig. 6 item 43) via the Administration Management and Control unit (AMC) (Fig. 6 item 32) incorporates the Comms Processing Engine (CPE) (Fig. 6 item 177), the Comms Channel Allocation Manager (CAM) (Fig. 6 item 178) and the Master Artificial Intelligence Engine (MAIE) (Fig. 6 item 37) which interfaces with the Wireless Communication Access Point / Concentrator (Fig. 6 item 176). Ad hock indirect wire less neural communication paths (Fig. 13 items 333, 334, 335, 337, 327, 328, 322, 330, 338, 323, 324, 316, 314, 318, 320, 325, 326, 701, 702, 704) enhance the direct communication paths of the transportable Processing Reactor Plants (PRP) (Fig. 6 item 31) for data integrity, disaster recovery and first responder conditions (Fig. 13 items 331, 332336). The multipath communications (local short range wireless, long range wireless and satellite) ensures the built-in redundancy will deliver the data to its destination at all times.

[0386] 11. CARBON TRADING, BLOCKCHAIN AND SMART CONTRACTS SYSTEM

[0387] Bio-Char (Fig. 3 item 101), as produced from the Conversion Reactors (Fig. 3 item 88), generally is accepted as a potential tool for carbon sequestration and climate change mitigation. Bio-Char can attract Certified Emission Reduction (CER) credits (Fig. 10 item 271) under the Clean Development Mechanism (CDM) of the Kyoto Protocol. This embodimenttotally integrates the processing of waste input feedstock (Fig. 3 item 1), the production of Bio-Char (Fig. 3 item 101), the creation of a Blockchain based digital ledger (Fig. 10 item 69), the creation of Smart Contracts (Fig. 10 item 69), the certifying of CER credits (Fig. 10 item 257) and facilitating the buy / sell transaction of the credits by registering those credits on a Carbon Trading Exchange (CTE) (Fig. 10 item 45).

[0388] One ofthe Output Commodity Products (Fig. 10 item 70), for the buyer marketplace (Fig. 10 item 44), is Bio-Char (Fig. 3 item 101). When the Bio-Char is produced from the Processing Reactor Plant (PRP) (Fig. 10 item 31) the Master Multi-Path Matrix Manager (MMM) (Fig. 10 item 24) informs the Administration Management and Control (AMC) (Fig. 10 item 32), within the Remote Monitoring and Control Centre (RMCC) (Fig. 10 item 43) that carbon certificates are required (Fig. 10 item 257), Blockchain and Smart Contracts need to be generated (Fig. 10 item 69) and the Carbon Trading Exchange (Fig. 10 item 45) interfacing is to be established. This unique automated process provides the real-time ‘spot’ Carbon Trading functionality and incorporates internal tasks for the Compliance and Certification Authority (CCA) (Fig. 10 item 257).

[0389] The Administration Management and Control (AMC) (Fig. 10 item 32) manages the Carbon Trading Algorithms (CTA) (Fig. 10 item 256) and via the Applications Management (Fig. 10 item 35) controls the Carbon Audit and Trading Engine (CATE) (Fig. 10 item 255). The Master Artificial Intelligence Engine (MAIE) (Fig. 10 item 37) manages the actions, activities and undertakings for the Blockchain and Smart Contracts (Fig. 10 item 69).

[0390] To qualify for CER credits, Bio-Char projects need to follow rigorous verification and monitoring processes to demonstrate the actual emissions reduced or sequestered. These processes ensure transparency and accountability, making Bio-Char projects eligible for CER credits. This embodiment provides all the necessary data integrity to verify and comply with all requirements and guidelines (Fig. 10 item 257).

[0391] 12. SMART CITY INTEGRATION SYSTEM

[0392] Smart city development refers to the integration of various technologies and solutions to enhance the quality of life for citizens, improve sustainability, and efficiently manage resources.

[0393] Various embodiments integrate into a Smart City development (Figs. 2A& 2B item 43C). The elements of waste feedstock processing (Fig. 3 items 1, 2, 3, 4), energy generation and storage (Fig. 3 item 5), environment sustainability, advance sensors in the conversion reactors (Fig. 3 item 88S), integrated automation to optimise the entire process (Fig. 5 item 21) and the data analytics process (Fig. 7 item 200) provides the Processing Reactor Plant (PRP) (Figs. 2A& 2B item 31) with many benefits to integrate directly into a Smart City development. Integrating a pyrolysis process into a smart city development (Figs. 2A& 2B item 43C) involves the use of advanced technologies to optimise waste processing (Fig. 3 item 3), renewable energy generation (Fig. 3 item 5), and environmentally sustainable energy distribution and control methods (Fig. 3 item 106). This embodiment incorporates a Processing Reactor Plant (PRP) (Figs. 2A& 2B item 31), equipped with a pyrolytic converter (Fig. 3 item 3), advanced Internet of Things (loT) sensors (Fig. 15 item 88S) and artificial intelligence controlled systems (Figs. 2A& 2B item 37). The highly optimised pyrolysis process is monitored continuously, to ensure the Processing Reactor Plant (PRP) (Figs. 2A& 2B item 31) operates efficiently and safely, converts the organic input waste feedstock (Fig. 3 item 1) into Bio-Char (Fig. 3 item 101), Bio-Gas (Fig. 3 item 95) and Bio-Oil (Fig. 3 item 97). Each of the reactor output commodities (Fig. 3 item 4) are utilised within the city framework. Infused Bio-Char (Fig. 3 item 99A) is used as a soil amendment and fertiliser in urban agriculture projects, Bio-Gas (Fig. 3 item 95) and Bio-Oil (Fig. 3 item 97) used as renewable energy to power streetlights, public buildings and the generated energy (Fig. 3 item 105) used for electric vehicle charging stations. Within the system of this mebodiment, the Direct Energy and Hydrogen Storage module (Fig. 11 item 293) provides energy to an Energy Distribution Process unit (Fig. 11 item 287) for distribution to the main smart power grid (Fig. 11 item 288), communal micro-grids (Fig. 11 item 288C), other external connections (Fig. 11 item 273) or external energy into other transportable units (Fig. 11 item 286) such as Transportable Hydroponic Centre (THC) (Fig. 11 item 275H), Transportable Data Hosting Centre (DHC) (Fig. 11 item 275), Transportable Data Collector Centre (DCC) (Fig. 11 item 278), Transportable Unified Communication Centre (UCC) (Fig. 11 item 281) and T ransportable Scientific Laboratory Centre (SLC) (Fig. 11 item 284).

[0394] Demand-response mechanisms, to balance energy supply and demand (Fig. 3 item 104), is controlled in real-time by the Energy Control module (Fig. 3 item 106). By integrating the Processing Reactor Plant (RRP) of Fig. 3 into a smart city development, cities can effectively manage organic waste, reduce greenhouse gas emissions, generate renewable energy and promote a more sustainable urban environment.

[0395] 13. SUSTAINABLE DEVELOPMENT GOALS (SDG)

[0396] The United Nations Sustainable Development Goals (SDG) provide a comprehensive framework for addressing global challenges, including poverty, inequality, climate change, environmental degradation, peace, and justice. Integrating elements of the SDG program into a smart city development ensures that cities contribute significantly to achieving these goals by aligning the city's initiatives, policies, and projects with the 17 SDGsto create a sustainable, inclusive, and resilient urban environment. For a smart city, goals such as Clean Water and Sanitation (SDG 6), Affordable and Clean Energy (SDG 7), Sustainable Cities and Communities (SDG 11), Responsible Consumption and Production (SDG 12), and Climate Action (SDG 13) are highly relevant. By incorporating these framework data elements as embedded features within embodiments of the pyrolytic waste processing plant of the invention (see Fig. 1), the technical feasibility, environmental benefits, social advantages and economic viability significantly aligns with the overall SDG program’s goals for sustainable development.

[0397] Various embodiments incorporate smart technologies in waste processing (Fig. 3 item 21), resource recovery (Fig. 3 item 3), renewable energy generation and management (Fig. 3 item 5) and waste disposal (Fig. 3 item 4) to optimise resource usage and reduce environmental impact. Data processing (Fig. 7 item 31), data analytics (Fig. 7 item 200), artificial intelligent modules (Fig. 7 item 37), data processing algorithms (Fig. 7 item 206), historical trend databases (Fig. 7 item 207) and loT devices (Fig. 7 item 179) to collect and analyse data for informed decision-making (Fig. 7 item 32), enabling efficient resource allocation (Fig. 7 item 35), effective renewable energy distribution (Fig. 11 item 287), appropriate resource recovery (Fig. 11 item 293) and environmental conservation.

[0398] 14. ENVIRONMENTAL SOCIAL AND GOVERNANCE (ESG) CRITERIA

[0399] Environmental, Social, and Governance (ESG) is a globally recognised corporate based criteria that is increasingly becoming integral to smart city initiatives, ensuring that these developments are environmentally sustainable, socially inclusive, and governed responsibly. Various embodiments of the invention are eco-friendly in managing organic waste, and their design is intrinsically ESG compliant by following and embedding the following ESG framework factors. The reduction in greenhouse gas emissions achieved by diverting organic waste from landfills and incineration is a critical aspect of sustainable waste management. When organic waste, such as food waste, green waste, or paper, is diverted from landfills and incineration and instead is processed through methods such as according to this embodiment, it undergoes a natural, low temperature (500°C) pyrolytic processes that produce significantly fewer greenhouse gas emissions. When organic waste decomposes in landfills, it undergoes anaerobic (without oxygen) decomposition, producing methane (CH4), a potent greenhouse gas that is significantly more effective at trapping heat in the atmosphere than carbon dioxide (CO2). By diverting organic waste from landfills, the production of methane is minimised. Burning organic waste in incinerators releases carbon dioxide directly into the atmosphere. While CO2is a greenhouse gas, it is less potent than methane. However, diverting organic waste from incineration prevents the immediate release of C02, contributing to emission reduction. Landfills not only produce methane but also emit carbon dioxide as organic waste slowly decomposes aerobically (with oxygen). Diverting organic waste reduces these CO2emissions as well, although at a slower rate than methane reduction. Incinerating organic waste releases C02directly into the atmosphere, contributing to climate change. By diverting organic waste, these CO2emissions are avoided, further reducing the carbon footprint. When organic waste is processed according to an embodiment of this invention, such as depicted in Fig. 1, it is converted (Fig. 3 item 3) into commodities such as Bio-Char (Fig. 3 item 101), which can be used to enrich soil, Bio-Gas (Fig. 3 item 95) and Bio-Oil (Fig. 3 item 97), which can be used to generate energy (Fig. 3 item 105). Healthy soil acts as a carbon sink, sequestering carbon dioxide from the atmosphere and offsetting emissions. The transportation of organic waste to landfills or incinerators also contributes to greenhouse gas emissions. By promotingsmall, localised waste processing plants, such as the embodiment of Fig. 11 (see item 291), transportation emissions associated with waste disposal are reduced.

[0400] Emission reductions are typically measured in terms of carbon dioxide equivalent (CO2e), a standard unit for comparing the potency of various greenhouse gases relative to carbon dioxide over a specified timeframe (commonly 100 years). Reductions achieved through waste diversion programs are quantified by calculating the difference in emissions between the baseline scenario (organic waste going to landfill or incineration) and the diversion scenario using a pyrolytic process. This embodiment incorporates smart technologies in waste processing (Fig. 3 item 21), resource recovery (Fig. 3 item 3), renewable energy generation and management (Fig. 3 item 5) and waste disposal (Fig. 3 item 4) to provide quantifiable calculations. Data processing (Fig. 7 item 31), data analytics (Fig. 7 item 200), artificial intelligent modules (Fig. 7 item 37), data processing algorithms (Fig. 7 item 206), historical trend databases (Fig. 7 item 207) and loT devices (Fig. 7 item 179) are used to collect and analyse data for informed decision-making (Fig. 7 item 32).

[0401] Blockchain based digital ledgers (Fig. 10 item 69), provide a decentralised and transparent platform for transactions and Smart Contracts (Fig. 7 item 69). The Remote Monitoring and Control Centre (RMCC) (Fig. 19 item 43) includes Blockchain and Smart Contract interface (Fig. 10 item 69) and connects directly to a Carbon Audit and T rading Engine (MRE) (Fig. 10 item 255) for real time carbon emissions trading. The data collection, processing and analysis are valuable for assessing the effectiveness of waste management policies, reduction in greenhouse gas emissions and mitigating climate change.

[0402] By generating and storing energy (Fig. 3 item 5), producing valuable commodities (Fig. 3 item 4), creating subsequent by-products (Fig. 110 item 287C) and interfacing to carbon trading exchanges (Figs. 2A& 2B item 45), resource recovery from organic waste is maximised through the natural, low temperature (500°C) pyrolytic process (Fig. 3 item 88), (conversion by the absence of oxygen) integrated within the Processing Reactor Plant (PRP) (Figs. 2A & 2B item 31) to produce significantly fewer greenhouse gas emissions. Conversion and reaction times, within the Conversion Reactor (Fig. 3 item 88), are dependent on sensor output data and control (Fig. 3 item 88S) the required output configuration (Fig. 3 item 4) and the characteristic of the input waste feedstock (Fig. 3 item 1). As an example, based on a 24-hour operational day, with 100 Metric Tonnes of biomass input waste feedstock (Fig. 3 item 77), with 10% moisture content, at a pyrolysis processing temperature of500°C will require a processing time of 30 minutes (Fig. 3 item 3) to produce Bio-Char (Fig. 3 item 101). The Processing Reactor Plant (PRP) will require 9 staff per day (3 staff on an 8-hour shift basis for the 24-hour operation) to operate efficiently and cost effectively. All documentation and data of compliance with environmental regulations is maintained and controlled by the Compliance and Certification Authority module (CCA) (Fig. 10 item 257) which includes Audit of Waste Input (Fig. 10 item 263), Audit of the Reactor Plant (Fig. 10 item 264) and the Audit of Product Output (Fig. 10 item 265).

[0403] 15. CIRCULAR ECONOMY MODEL (CEM)

[0404] The Circular Economy Model (CEM) is a globally recognised economic model, based on production and consumption, that aims to eliminate waste and promote the continuous use of resources. When incorporated into a smart city development, a circular economy program can transform the way cities manage resources, products, and waste. By incorporatingthe economic framework data elements as embedded features within the pyrolytic waste processing plant of various embodiments of the invention, such as detailed in Fig. 1, the technical feasibility, environmental advantages, social benefits and economic viability significantly aligns with the overall Circular Economy process of sustainable resource management within a smart city development. By incorporatingthe economic framework data elements as embedded features within the pyrolytic waste processing plant of Fig. 1, the technical feasibility, environmental benefits, social advantages and economic viability significantly aligns with the overall CEM program's aims for production and consumption.

[0405] Various embodiments incorporate smart technologies in waste processing (Fig. 3 item 21), resource recovery (Fig. 3 item 3), renewable energy generation and management (Fig. 3 item 5) and waste disposal (Fig. 3 item 4) to optimise resource usage and reduce environmental impact. Data processing (Fig. 7 item 31), data analytics (Fig. 7 item 200), artificial intelligent modules (Fig. 7 item 37), data processing algorithms (Fig. 7 item 206), historical trend databases (Fig. 7 item 207) and loT devices (Fig. 7 item 179) to collect and analyse data for informed decision-making (Fig. 7 item 32), enabling efficient resource allocation (Fig. 7 item 35), effective renewable energy distribution (Fig. 11 item 287), appropriate resource recovery (Fig. 11 item 293) and environmental conservation.

[0406] Various embodiments of the invention integrate into a Smart City development (Figs. 2A & 2B item 43C). The elements of waste feedstock processing (Fig. 3 items 1, 2, 3, 4), energy generation and storage (Fig. 3 item 5), environment sustainability, advance sensors in the conversion reactors (Fig. 3 item 88S), integrated automation to optimise the entire process (Fig. 5 item 21) and the data analytics process (Fig. 7 item 200) provides the Processing Reactor Plant (PRP) (Figs. 2A& 2B item 31) with many benefits to integrate directly into a Smart City development. Integrating a pyrolysis process into a smart city development (Figs. 2A& 2B item 43C) involves the use of advanced technologies to optimise waste processing (Fig. 3 item 3), renewable energy generation (Fig. 3 item 5), and environmentally sustainable energy distribution and control methods (Fig. 3 item 106). This embodiment incorporates a Processing Reactor Plant (PRP) (Figs. 2A& 2B item 31), equipped with a pyrolytic converter (Fig. 3 item 3), advanced Internet of Things (loT) sensors (Fig. 15 item 88S) and artificial intelligence controlled systems (Figs. 2A& 2B item 37).

[0407] By introducing certain embodiments of the invention into the marketplace, significant environmental benefits can be gained, such as the reduction in greenhouse gas emissions achieved by diverting organic waste from landfills and incineration, by using the pyrolytic conversion reactor (Fig. 3 item 3), the conservation of natural resources by resource recovery methods (Fig. 3 item 4) and the generation of renewable energy for distribution (Fig. 3 item 5). Additionally, substantial environmental benefits are achieved by the restoration of natural systems with the applications of biochar (Fig. 3 item 101) in agriculture, water filtration, and carbon sequestration.

[0408] Various important features of these embodiments are discussed below in more detail as ‘Key Items’ 1 to 21. Itshould be understood that each embodiment may have any one or more of the features.

[0409] KEY ITEM 1.

[0410] Figs. 2A& 2B present a schematic diagram of a decentralised Processing Reactor Plant 31 according to an embodiment of the present invention. The plant’s characteristics, control functionality, logic, business rules and process methods 27 are established by a Master Multi-Path Matrix Manager (MMM) 24 for a desired output 4 within the Process Matrix Control (PMC) 21 and communication established with a centralised Remote Monitoring and Control Centre (RMCC) 43 via a Multi-Path Communication Control (MCC) 26. The remote Administration Management and Control (AMC) 32 within the Remote Monitoring and Control Centre (RMCC) 43, integrates a Master Artificial Intelligence Engine (MAIE) 37 with data lookup tables with data records in Rules and Facts 41, algorithms used to perform computations 36, self- contained applications, instructions and actions 35, access control authentication 34 and simultaneous sychronisation 33 of multiple Processing Reactor Plants (PRP) (see Fig. 6 item 136), as an example which addresses the shortcomings of current and existing waste processing plants.

[0411] Based on market requirements 44, the Administration Management and Control (AMC) 32, whether automatically, manually or autonomously, identifies all input waste material 1, arranges the pretreatment requirements 2, establishes reactor chamber configurations 3, creates flow methods as necessary for the desired output 4 and the energy conversion requirements 5 via the Multi-Path Communication Control (MCC) 26, the Plant Communication and Control (PCC) 25 and the Master MultiPath Matrix Manager (MMM) 24.

[0412] The Processing Reactor Plant (PRP) 31 is continuously monitored and controlled via the Remote Monitoring and Control Centre (RMCC) 43, which also interfaces directly to the commodity market 44 and the buyers for indicative real-time requirements for the output commodities 4 or the generated energy 5. The commercially valuable output commodities requirement 15, for conformation to specifications, quantity, timing etc., is dynamically configured within the Converter Reactor Unit (CRU) 12 as requested by the Administration Management and Control (AMC) 32. This is only one embodiment of the output requirement 15 configuringthe Processing Reactor Plant (PRP) 31 process by external market influences with historical data analytics 28 and associated Machine Learning processes 28M.

[0413] This embodiment incorporates the decentralised Processing Reactor Plant (PRP) 31 including the Process Matrix Control (PMC) 21, Local Off-line Access Control (LOA) 22, Security Authentication and Heartbeat (SAH) 23, Master Multi-Path Matrix Manager (MMM) 24, Plant Communication and Control (PCC) 25, and the Multi-Path Communication and Control (MCC) 26. The centralised Remote Monitoring and Control Centre (RMCC) 43 with Administration Management and Control (AMC) 32 integrates a Master Artificial Intelligence Engine (MAIE) 37, Blockchain with Smart Contract process 69, Carbon Trading Exchange (CTE) 45 and connected by a Quantum Node Network 43Q. Multiple Processing Reactor Plants (PRP) 31 simultaneously communicate and are wirelessly interconnected with a centralised global Remote Monitoring and Control Centre (RMCC) 43.

[0414] All input waste feedstock (cf. Fig. 3 item 1) entering the Processing Reactor Plant (PRP) 31, via the Process Matrix Control (PMC) (cf. Fig. 3 item 21), is identified by type of material (see Fig. 3 item 74), the available data on the waste material 75, the energy values of the material (see Fig. 3 item 76) and the type of waste classification (see Fig. 3 item 78). The pre-treatment process (see Fig. 3 item 2) analyses the input waste for moisture, particle size, etc. (see Fig. 3 item 79, item 80). Upon analysing all the derived input waste information, the Master Multi-Path Matrix Manager (MMM) (see Fig. 3 item 24) engages the Converter Matrix Control (CMC) 13 to prepare the most suitable Converter Reactor Unit (CRU) (see Fig. 3 item 12) for the desired output (see Fig. 3 item 4) taking into consideration any Flow Control (FC) (see Fig. 3 item 13F) requirements. Separate, independentand module specific Artificial Intelligence is provided within the Process Matrix Control (PMC) 21 for each of the modules for assistance, guidance and recommendations to store 28 for future use with reiterative updates usingthe onboard Machine Learning processes 28M. Individual Artificial Intelligence capability is established in the Input Matrix Control (IMC) 7 as Local A.I. (I) 8, the Arrange Matrix Control (AMC) 10 as Local A.I. (A) 11, the Convert Matrix Control (CMC) 13 as Local A. I. (C) 14, the Output Matrix Control (OMC) 16 as Local A.I. (O) 17 and the Energy Matrix Control (EMC) 19 as Local A. I. (E) 20.

[0415] As a process flow and configuration example, but not limited to, the Administration Management and Control (AMC) 32, within the Remote Monitoring and Control Centre (RMCC) 43, receives information from the Master Multi-Path Matrix Manager (M M) 24 that the Processing Reactor Plant (PRP) 31 has received sewage sludge as the input waste feedstock for processing. The Administration Management and Control (AMC) 32 usingthe Master Artificial Intelligence Engine (MAIE) 37 with algorithms36, applications 35, rules and facts 41 or other stored information 40, analyzes and predicts the strong local market and buyer 44 demand for Bio-Char, a commodity and by-product of sewage sludge, where the Processing Reactor Plant (PRP) is physically located, and will also receive Certified Emission Reduction (CER) credits on the Carbon Trading Exchange (CTE) 45. Upon receiving the process activation requestfrom the Administration Management and Control (AMC) 32, the Process Matrix Control (PMC) (see Fig. 3 item 21), establishes the plant matrix configuration to produce Bio-Char as the required output based on sewage sludge as the available input waste feedstock. Each individual controller, Input Matrix Control (IMC) (Fig. 3 item 7), Arrange Matrix Control (AMC) (Fig. 3 item 10), Convert Matrix Control (CMC) (Fig. 3 item 13), Output Matrix Control (OMC) (Fig. 3 item 16) and Energy Matrix Control (EMC) (Fig. 3 item 19) is provided with requirement and establishment instructions. The Input Matrix Control (IMC) (Fig. 3 item 7) reviews the derived analysed data (Fig. 3 item 6) and identifies the input array as sewage sludge waste (Fig. 3 item 77) with 30% moisture content (Fig. 3 item 74). The Arrange Matrix Control (AMC) (Fig. 3 item 10) identifies that the preparation arrangement as derived from the Input Feedstock Imaging and Identification Manager (IFI) (Fig. 3 item 50) is requiring dewatering, followed by dryingto 10% moisture content (Fig. 3 item 82). The Convert Matrix Control (CMC) (Fig. 3 item 13) activates the Energy Conversion and Distribution (ECD) (Fig. 3 item 85) and the Process Requirement Detection (Fig. 3 item 86) to create the reactor configuration (Fig. 3 item 87) which was determined to require a variable conversion temperature reactor (Fig. 3 item 88T) at 450°C to 600°C (840°Fto 1100°F), with all the converter sensor activated (Fig. 3 item 88S) for maximum conversion control, to produce the required specification of output Bio-char. As there is no blending or condensing requirement (Fig. 3 item 89) additional processing equipment is not utilised. The Output Matrix Control (OMC) (Fig. 3 item 16) selects the Bio-char output requirement (Fig. 3 item 101) and the Master Multi-Path Matrix Manager (MMM) (Fig. 3 item 24) informs the Energy Matrix Control (EMC) (Fig. 3 item 19) that no Coal Fired Turbine (Fig. 3 item 107) will be required as this is an output for raw Bio-Char only. The Master Multi-Path Matrix Manager (MMM) (Figs. 2A& 2B item 24) interacts with the master Local Rules and Facts (P) (Figs. 2A& 2B item 27) to orchestrate all activities and to ensure all processing data is stored within the Store (P) (Figs. 2A& 2B item 28) to enable Machine Learning (Figs. 2A & 2B item 28M) forfuture requirements. As the Bio-Char is produced from the Processing Reactor Plant (PRP) (Figs. 2A & 2B item 31), in real-time, the Master Multi-Path Matrix Manager (MMM) (Figs. 2A& 2B item 24) informs the Administration Management and Control (AMC) (Figs. 2A& 2B item 32) that carbon certificates are required (Fig. 10 item 261), Blockchain and Smart Contracts need to be generated (Fig. 10 item 69) and Carbon Trading Exchange (Fig. 10 item 45) interfacing to be established. This unique automated process provides the real-time ‘spot’ Carbon Trading functionality and incorporates internal tasks for the Compliance and Certification Authority (CCA) (Fig. 10 item 257).

[0416] Similarly, another example within the Process Matrix Control (PMC) (Fig. 3 item 21), but not limited to, details the Master Multi-Path Matrix Manager (MMM) (Fig. 3 item 24) forthe configuration for Bio-Oil output (fig 3 item 97) based on used tyres as the input waste feedstock (Fig. 3 item 77). The conversion process commences as the waste input is identified as tyres (Fig. 3 item 77). the preparation is established as shred with no drying (Fig. 3 item 82) and then the processing conversion stage is configured for fixed high temperature reactor (Fig. 3 item 88) at 500°C (900°F) followed by a condensing process (Fig. 3 item 89) to finally produce the Bio-Oil (Fig. 3 item 97). The Arrange Matrix Control (AMC) (Figs. 2A& 2B item 10) also identifies that an additional process of removing the tyre steel is required by the Otheri process (Figs. 2A& 2B item 82).

[0417] Referring to Figs. 2A& 2B, automated processes within the Processing Reactor Plant (PRP) 31 can be switched to off-line use, as a manual mode operation, via the Local Off-Line Access Control (LOA) 22 once the local operator is authenticated via the Security Authentication and Heartbeat (SAH)(P) 23.

[0418] The localised and independent Master Multi-Path Matrix Manager (MMM) (see Fig. 1 item 24) utilises distributed, decentralised management functionality to process, control, collect, administer, authenticate and validate all localised data underthe centralised management of the Master Artificial Intelligence Engine (MAIE) 37 within the Remote Monitoring and Control Centre (RMCC) 43.

[0419] This unique process allows for massively large data to be generated, collected, analysed and applied at the localised level, within each Processing Reactor Plant (PRP) 31 and then to be forwarded to the central Remote Monitoring and Control Centre (RMCC) 43 for aggregated consolidation of global data for further historical data analytics 28 and associated Machine Learning processes 28M.

[0420] The embodiment provides the basis for the utilisation and processin of organic waste as input feedstock 1, within the Converter Reactor Unit (CRU) 12, and producing renewable green energy 5, within Energy Matrix Control (EMC) 19 and the Turbine Energy Array 18 resulting in:

[0421] - New energy supplies to reduce fossil fuel dependency.

[0422] - Reduction of Greenhouse Gas Emissions by decreasing landfill methane emissions.

[0423] - Environmental benefits derived from reduced waste sent to landfills.

[0424] - Environmental quality improvements by reducing chemical leaching. - Reduced operating costs offset by new revenue streams.

[0425] - Economic growth by employment opportunities for more people.

[0426] - New revenue streams created by new business generation.

[0427] - Circular econo my reduces strain on natural resources.

[0428] There are many applications of Processing Reactor Plant (PRP) 31. One embodiment of the system is that it can be used for an Industrial Waste Processing plant (IWP), Municipal Solid Waste (MSW) processing plant or treated Sewage Processing Plant (SPP) or a used Tyre Processing Plant (TPP), Tanneries Sludge Waste (TSW), Meat and Bone Meal (MBM) industry, Oil Refinery Sludge (ORS), Plastic Waste Disposal (PWD). In each of the embodiments, the system forms the fundamental basis of the Process Matrix Control (PMC) 21 as the input stage 1, preparation or arrangement stage 2, processing or conversion stage 3, output commodities stage 4 or energy generation stage 5 to manage all aspects of the Processing Reactor Plant (PRP) 31.

[0429] As an example, Processing Reactor Plant (PRP) 31 is transported into waste feedstock processing requirements that may be located in remote locations and is required to function independently for prolonged periods of time. In addition, Processing Reactor Plant (PRP) 31 may be manned or unmanned determined by the environmental surroundings and location. These locations may be hazardous, have extreme environmental issues or where human life forms cannot be sustained. The autonomous capability of the Processing Reactor Plant (PRP) 31 is well suited for such hazardous and extreme environmental conditions without manned operations.

[0430] KEY ITEM 2.

[0431] Again referringto Figs. 2A& 2B, this embodiment provides a rules-based 27 artificial intelligence (Al) structure that utilises localised A.I. in each module, such as Input Local A.I. (I) 8, Arrange Local A. I. (A) 11, Convert Local A.I. (C) 14, Output Local A.I. (0) 17, Energy Local A.I. (E) 20 and a master control A.I. , Master Artificial Intelligence Engine (MAIE) 32, within the centralised Remote Monitoring and Control Centre (RMCC) 43. This system self-configures and adapts in real-time to factors driven by economics 44, the environment 1, energy 5, output 4 and geographic location 650, as examples which addresses the shortcomings of current and existing waste processing plants.

[0432] The embodiment is flexible in structure, highly adaptive, efficient and able to provide real-time data for analytics that are utilised by all the process control managers within the Processing Reactor Plant (PRP) (see Fig. 7 item 31). By considering real-time data, commercial demands, and environmental factors, the plant can operate optimally, maximising both economic returns and environmental benefits.

[0433] Referringto Fig. 3, the Input Matrix Control (IMC) 7, within the Process Matrix Control (PMC) 21, analyses the incoming waste feedstock and measures key parameters 75, type of material 74, identifies the input array 77, the energy values of the material 76 and any other input data such as waste composition, moisture content, waste classification, temperature, pressure, etc. 78. Each individual controller, within the Process Matrix Control (PMC) 21, is provided with requirements and control instructions to independently administertheir own process requirements without referringto a central supervisory authority. These controllers are the Input Matrix Control (IMC) 7, the Arrange Matrix Control (AMC) 10, the Convert Matrix Control (CMC) 13, the Output Matrix Control (OMC) 16 and the Energy Matrix Control (EMC) 19. As an example, the Arrange Matrix Control (AMC) 10 may instruct the Arrange Array unit 82 to adjust the temperature and dryingtime accordingly if the waste moisture content is above the acceptable 10% level. As another example, the Convert Matrix Control (CMC) 13 may modify the Variable Temperature Conversion Reactor’s 88T temperature (e.g. lower the temperature by 5 °C) of the last 10 control sensors 88S fora period of 5 minutes. The reduction of internal auger speed will also be included within the processing parameters. A further exam pie, the Energy Collection unit 81 in conjunction with the Energy Conversion and Distribution unit (ECD) 85 may adjust the gas flow rates to the Conversion Reactors 88 to optimise the conversion process for increased output by-product yields.

[0434] Within the data processing system there is flexibility with highly controlled waste processing configurations by utilising the Variable Temperature Conversion Reactor 88T, integration of the multitude of configurable sensors 88S, the Control Data Administration unit (CDA) (see Fig. 7 item 192), the precise management of origination data by the Unit Cell Data unit (UCD) (see Fig. 7 item 179) and the analytical computations of the Data Analysis Manager (DAM) (Fig. 7 item 200) all managed by the Data Flow Process Manager (Fig. 7 item 203). Thresholds and trigger parameters are established forspecific actions and or adjustments are activated with the Unit Cell Data (UCD) (Fig. 7 item 179).

[0435] Data collected from all the localised Processing Reactor Plants (PRP) (Figs. 2A& 2B item 31) are collated and aggregated by the Administration Management and Control (AMC) (Fig. 7 item 32), within the Remote Monitoring and Control Centre (RMCC) (Fig. 7 item 43), to create a comprehensive overview of the waste characteristics and the processing condition of the Conversion Reactors (Fig. 3 item 88).

[0436] Additional rules-based (Figs. 2A& 2B item 27) artificial intelligence (Al) structures are created with commercially driven factors and implemented within the Market Demand and Buyers module (Figs. 2A& 2B item 44) and (Fig. 10 item 44) to analyse real-time market demand for commodities (Fig. 3 item 15), byproducts (Fig. 3 item 99) and energy (Fig. 3 item 106) as produced by the Output stages (Fig. 3 item 15) of the Processing Reactor Plant (PRP) (Fig. 10 item 31). The commercial factors, within the Rules and Facts Database (R) (Fig. 10 item 41), implemented by the Master Artificial Intelligence Engine (MAIE) (Fig. 10 item 37), incorporate cost factors such as labour, energy, consumables and maintenance as an integral part of the decision making process to maximise output quality and quantity while minimising costs. Further decisions may be required by the Master Artificial Intelligence Engine (MAIE) (Fig. 10 item 37), based on the Market Demand (Fig. 10 item 41) in each location where the Processing Reactor Plants (PRP) (Figs. 2A& 2B item 31) are based, whether to sell the output as raw Bio-Char directly to byers (Fig. 10 item 44), and claim Emission Reduction Credits (ERC) (Fig. 10 item 271), or convert the commodity to energy (Fig. 3 item 106), and then sell to byers (Fig. 10 item 70).

[0437] This embodimentspports an economic model that is market based ‘pull’ forces (Figs. 2C & 2D item 73), to drive the type of input stage (Figs. 2C & 2D item 1) that is required to be processed at the output stage (Figs. 2C& 2D item 4). This pull force means the market, or the output (Figs. 2C & 2D item 4), is driving the configuration and requirement of the Processing Reactor Plant (PRP) (Figs. 2C & 2D item 31) ratherthan the current method of processing any input waste that is received.

[0438] The Master Artificial Intelligence Engine (MAIE) (Figs. 2C & 2D item 37) uses two-way deductions. The first type of deduction is from the Rules and Facts (Figs. 2C & 2D item 41) and the Market Demand (Figs. 2C & 2D item 44) where the Master Artificial Intelligence Engine (MAIE) (Figs. 2C & 2D item 37) uses the rules and data of what product demand there is in the marketplace to feed into the control function and configure the process of the Processing Reactor Plant (PRP) (Figs. 2C & 2D item 31) (pull system). The other type of deduction is from the Processing Reactor Plant (PRP) (Figs. 2C & 2D item 31) control function where the Master Artificial Intelligence Engine (MAIE) (Figs. 2C & 2D item 37) uses the rules and data from the Processing Reactor Plant (PRP) (Figs. 2C & 2D item 31) to compare what in put feedstock is available to process in order to deduce whether there is a market demand for the anticipated product that will be produced (push system). The Master Artificial Intelligence Engine (MAIE) (Figs. 2C & 2D item 37) pull and push systems start with data and facts stored in the Rules and Facts database (Figs. 2C & 2D item 41) and then uses the data, facts and the rules to deduce a conclusion that is then translated into a set of instructions to control sub-systems within the Processing Reactor Plant (PRP) (Figs. 2C& 2D item 31) or interface control into Carbon T rading Exchanges (Figs. 2C & 2D item 45), as an example.

[0439] Once the Master Artificial Intelligence Engine (MAIE) (Figs. 2C & 2D item 37) conclusion is executed by the Processing Reactor Plant (PRP) (Figs. 2C & 2D item 31) control function it is added or appended to the Master Artificial Intelligence Engine (MAIE) (Figs. 2C & 2D item 37) data and facts database the Rules and Facts database (Figs. 2C & 2D item 41) to be reused as a refinement or additional fact in the next iteration of deductions. This appended iterative process allows the Master Artificial Intelligence Engine (MAIE) (Figs. 2C & 2D item 37) to derive its conclusions faster as it starts to learn the environment it is operating in. Conflict can arise when the Master Artificial Intelligence Engine (MAIE) (Figs. 2C & 2D item 37) can deduce more than one conclusion from the stored data and fact database. Conflict resolution of Master Artificial Intelligence Engine (MAIE) (Figs. 2C & 2D item 37) derived conclusions are minimised via the ranking process of the facts database (as detailed in item 3) and the number of facts that were used to derive at each conclusion. In this case the larger the number of facts that are used to derive at the conclusion would mean that that it would have a higher ‘detail score’ and thus more reliable in its outcome rather than a small number offactsthat are used to derive at another conclusion.

[0440] This new economic model not only provides maximum shareholder value, by facilitating and producing saleable products, but also benefits the environment. The new system assists in our ability to sustain a positive presence on earth by protecting our environment, defending our food sources, harnessing energy sources and producing additional clean sustainable energy.

[0441] The Master Artificial Intelligence Engine (MAIE) (Fig. 7 item 37) is also actively involved in process optimisation by analysing the HistoryTrend Database (R) (Fig. 7 item 207) to analyse processing patterns within the waste input (Fig. 3 item 1), arrange methods (Fig. 3 item 2), conversion process (Fig. 3 item 3), output production (Fig. 3 item 4) and energy generation (Fig. 3 item 5). Within this process, internal emission controls (Fig. 3 item 88S) are implemented to ensure all Processing Reactor Plants (PRP) (Fig. 10 item 31) comply with local environmental regulations.

[0442] Each Processing Reactor Plant (PRP) (Figs. 2A& 2B item 31) is equipped with predictive analysis capability that use the Machine Learning module (ML) (Figs. 2A& 2B item 28M), the Storage (P) database (Figs. 2A & 2B item 28) and the Local Rules and Facts (P) (Figs. 2A& 2B item 27) to predict Converter Reactor Unit (CRU) (Fig. 3 item 88) performance. The Historical T rend Database (R) (Fig. 7 item 207) from the Remote Monitoring and Control Centre (RMCC) (Figs. 2A& 2B item 43) is also dynamically used in proactive decision making and continuous improvement. This system-wide process provides quantifiable and measurable historical performance data for the algorithm based modules (Figs. 2A& 2B item 36) to feed into the Rules and Facts database (R) (Figs. 2A& 2B item 41).

[0443] The user interface, within the Local Support Services unit (Fig. 6 item 137), provides the local operator with direct access into the Processing Reactor Plant (PRP) (Fig. 6 item 31) to review system performance and automated A.I. decisions. The operator has the ability to manually input constrains or processing preferences to suit local parameters. All such adjustments are recorded in the local Storage (P) (Figs. 2A & 2B item 28) and also monitored and recorded in the Remote Monitoring and Control Centre (RMCC) (Figs. 2A& 2B item 43) as the remote Storage (R) (Figs. 2A& 2B item 40) to guide the Master Artificial Intelligence Engine (MAIE) (Figs. 2A& 2B item 37).

[0444] The Master Artificial Intelligence Engine (MAIE) (Figs. 2A& 2B item 37) uses the Rules and Facts (R) database (Figs. 2A& 2B item 41) to provide recommendations and / or to determine the extent of control over the product outcome and / or configuration of the local Processing Reactor Plant (PRP) (Figs. 2A& 2B item 31). This interpretive system provides economic judgment and knowledge base management to the waste processing capability of this embodiment. Thus the created, and continually updated, database of rules and other economic facts and factors are used as input facts by the Master Artificial Intelligence Engine (MAIE) (Figs. 2A& 2B item 37) to combine, evaluate the scenario and to then derive conclusions for feeding into the control function of the local Processing Reactor Plant (PRP) (Figs. 2A& 2B item 31) for action.

[0445] In order to keep the process of relevance and to control order the Master Artificial Intelligence Engine (MAIE) (Fig. 7 item 37) adds a time and date stamp (Fig. 7 item 196) stored in the Rules and Facts Database (R) (Fig. 7 item 41). Thus the latest fact or rule that is added or appended into the Rules and Facts Database (Fig. 7 item 41) has the highest time and date ranking order and so is used first before any similar fact or rule to deduce a conclusion. So as new rules and facts are added to the database without deleting any old or superseded rules or facts the Master Artificial Intelligence Engine (MAIE) (Fig. 7 item 37) automatically uses the most current of the facts or rules. As the Master Artificial Intelligence Engine (MAIE) (Fig. 7 item 37) appends new facts and rules and does not delete any of the old or existing facts and rules it can always resolve any conflicting conclusions by checking the older facts and rules. In most instances the newest facts and rules have highest priority in the formation of conclusions.

[0446] The entity that creates and appends the facts and rules in the Rules and Facts Database (R) (Fig. 7 item 41) is also monitored and recorded. This ‘entry entity’ becomes relevant and taken into consideration when the Master Artificial Intelligence Engine (MAIE) (Fig. 7 item 37) formulates the conclusion. As an example the entry created and appended by an identified expert or manager would carry a higher ranking than if the entry was entered by an operator or a service technician.

[0447] The Rules and Facts Database (R) (Fig. 7 item 41) also has a ‘facts relevance link’ or context specific ordering sequence. In this way the facts database in linked to subject matter to create relevant conclusions. As an example, to produce water as an output there needs to be wet input feedstock. Thus any conclusions relatingto water, heaters and dryers should also be related to wet input feedstock. Combining and utilising the ‘facts ranking process’, the ‘facts time stamp’, the ‘fact relevance ordering sequence’, the data and rule ‘entry entity’ and the ‘number of facts used’ to produce conclusions ensures the Master Artificial Intelligence Engine (MAIE) (Fig. 7 item 37) derives to conclusions that are accurate, timely and relevant.

[0448] The Master Artificial Intelligence Engine (MAIE) (Fig. 7 item 37) also uses the quickest path to a conclusion by using all or some of the facts stored in the Rules and Facts Database (R) (Fig. 7 item 41). Sometimes the quickest path to a conclusion, with no changes to any of the received information, may be the last conclusion that was stored is again repeated. These ‘stored conclusions’ form the historical operation of the local Rules and Facts Database (P) (Figs. 2A& 2B item 27).

[0449] In order to maintain coherence and system integrity on a global basis the Remote Monitoring and Control Centre (RMCC) (fig7. Item 43) uses the Master Artificial Intelligence Engine (MAIE) (fig7. Item 37) that aggregates all the individual Local Artificial Intelligence (LMAI) (Figs. 2A& 2B item 28A) from all the remote Processing Reactor Plants (PRP) (Figs. 2A & 2B item 31).

[0450] The Master Artificial Intelligence Engine (MAIE) (fig7. Item 37) sources all the conclusions generated by the Local Artificial Intelligence (LMAI) (Figs. 2A& 2B item 28A) and formulates common linkages as a total system resource. If a Local Artificial Intelligence (LMAI) (Figs. 2A& 2B item 28A) cannot deduce a conclusion with all of the local facts and rules (it may be a new installation and thus not enough facts are available) it would request assistance from the Master Artificial Intelligence Engine (MAIE) (fig7. Item 37). Thus, the Master Artificial Intelligence Engine (MAIE) (fig7. Item 37) conclusion database in the Rules and Facts (R) (Figs. 2A& 2B item 41) acts as an extension to the Local Artificial Intelligence (LMAI) (Figs. 2A& 2B item 28A) conclusion database.

[0451] The Master Artificial Intelligence Engine (MAIE) (fig7. Item 37) uses all of the derived conclusion inputs from each of the Local Artificial Intelligence (LMAI) (Figs. 2A& 2B item 28A) and starts to formulate new total system conclusions or models and plans. These models and plans will then act as templated database additions to Local Artificial Intelligence (LMAI) (Figs. 2A & 2B item 28A). These templates will not only refine data structures, rules and algorithms for continual system refinement but will also allowfor the prediction of economic resource in areas or the combination of some Local Artificial Intelligence (LMAI) (Figs. 2A& 2B item 28A) conclusions to formulate the best outcomes for other Local Artificial Intelligence (LMAI) (Figs. 2A & 2B item 28A) on the other side of the planet.

[0452] KEY ITEM 3.

[0453] Referring to Fig. 3, to minimise the requirement for highly skilled process manpower to operate the localised Processing Reactor Plants (PRP) 31 an assessment of the performance outcomes of the control processes and functions are required in real-time which addresses the shortcomings of current and existing waste processing plants.

[0454] Various embodiments provide a localised, unmanned Processing Reactor Plant (PRP) 31, as an example, wirelessly connected to a centralised Remote Monitoring and Control Centre (RMCC) 43 situated in a different location, with an embedded Master Artificial Intelligence Engine (MAIE) 37 containing Data Processing Algorithms (DPA) 206, Rules and Facts Database (R) 41, Historical Trend Database (R) 207, Storage database (R) 40, Data Analysis Tools 133A, Data Processing Engine (DPE). The Data Flow Process Manager 203, within the Processing Reactor Plant (PRP) 31, uploads all Local Filtered Data 202, to the remote Administration Management and Control (AMC) 32 in real-time and at predetermined time sequences or intervals, as determined by the Data Processing Engine (DPE) 204.

[0455] In the background the centralised Master Artificial Intelligence Engine (MAIE) (see Fig. 6 item 37), via the remote Master Multi-Path Matrix Manager (M M) (Figs. 2C& 2D item 24), is continually monitoring inputs (Figs. 2C & 2D item 1) and outputs (Figs. 2C & 2D item 4) to progressively adjust (Figs. 2C & 2D item 3) and predict the outcome (Figs. 2C & 2D item 70) required to maximise waste processing, optimise plant performance, generate renewable energy (Figs. 2C & 2D item 5) and to capitalise on any economic opportunities (Figs. 2C & 2D item 44). All of these factors create a Dynamic Manual Ranking (DMR) input on the overall Master Artificial Intelligence Engine (MAIE) (Fig. 7 Item 37) ranking system.

[0456] Analysing all generated data elements, from all locations in real-time, produces the Dynamic Manual Ranking (DMR) system. Some off these required data elements may include, but are not limited to, the current Local Filtered Data (Fig. 7 item 202), the Stored data (Fig. 7 item 40) at the Remote Monitoring and Control Centre (RMCC) (Fig. 7 item 43) of previous scenarios, outcomes and remedies held in the Historical Trend Database (R) (Fig. 7 item 207).

[0457] The Master Artificial Intelligence Engine (MAIE) (Fig. 6 Item 37) is continually and dynamically changing the required evaluations, expectations and probabilities of the required outcomes from the Processing Reactor Plant (PRP) (Fig. 6 item 31). The reasonableness of the assumptions is validated by all influencing factors which may include, but not limited to, operator location (Fig. 6 item 139), external environmental data (Figs. 2A& 2B item 1) or tasks and performance data (Fig. 6 item 111).

[0458] If the Master Artificial Intelligence Engine (MAIE) (Fig. 7 Item 37), computes that the conditions are correct fora reconfiguration of the Processing Reactor Plants (PRP) Fig. 7 item 31) it uses the Plant Communication and Control Manager (PCC) (Fig. 4 item 25) as the control functionality for remote plantwide adjustments as required.

[0459] The derived computation may have included ranking factor inputs (from high to low) or may have removed any factors that are not applicable for the present computation or conditions.

[0460] The Master Artificial Intelligence Engine (MAIE) (Fig. 7 item 37) may also store any conditions that are applicable forthe setup of the Processing Reactor Plant (PRP) (Fig. 6 item 31). Conditions may include, as an example, the feedstock is dry thus there is no requirement to include dryer functionality (Fig. 3 item 82D), the input feedstock is asbestos (Fig. 3 item 77) thus it is dry and requires high temperature in the reactor (Fig. 3 item 88). This would be interim data applicable until the feedstock changes at which time the new data is appended to the type of new feedstock. This type of approach speeds decision-making while still maintaining total system integrity.

[0461] The Data Processing Engine (DPE) (Fig. 7 item 204) and the Master Artificial Intelligence Engine (MAIE) (Fig. 7 Item 37) both rely on the stored ‘resolutions’ or ‘actions’ in the Storage database (R) (Fig. 7 item 40), the Historical T rend Database (R) (Fig. 7 Item 207) and the Rules and Facts Database (R) (Fig. 7 Item 41) as a model to base future decisions and / or assumptions with similar conditions. This is a learning system with historic baseline inputs.

[0462] This particular automation model approach is also used by the Master Artificial Intelligence Engine (MAIE) (Fig. 5 Item 37) in the Image Recognition Database Storage (A) (Fig. 5 Item 51). In this process, as an example, the input feedstock images data set as stored in the Stored Images Data (Fig. 5 Item 118) is used by the Master Artificial Intelligence Engine (MAIE) (Fig. 5 Item 37) to firstly identify, compare and match (Fig. 5 Item 123) the input feedstock. Once an image match condition is successful in the identification process (Fig. 5 Item 120), the Arrange Matrix Control (AMC) (Fig. 5 Item 10) communicates with the Master Multi-Path Matrix Manager (MMM) (Fig. 5 Item 24), to prepare to take the scanned waste feedstock into the Conversion Reactors (Fig. 3 Item 88). All process data is replicated into the Master Artificial Intelligence Engine (MAIE) (Fig. 5 Item 37) for acknowledgement and confirmation.

[0463] The Master Artificial Intelligence Engine (MAIE) (Fig. 7 Item 37), within this invention, is providing multiple functionality where it deliberately takes control of the processfor a desired output. The Master Artificial Intelligence Engine (MAIE) (Fig. 7 Item 37) also, but not limited to, responds to requirements and takes those requirements into consideration for its outcome generation.

[0464] As an example, the following process represents the local and external influencers, manual factors and control functions that are involved within the generation of the ‘Ranking’ scores:

[0465] - Key Performance Indicators (KPI) such as efficiency, accuracy and speed are identified.

[0466] - Process data collection is standardised and organised.

[0467] - Preprocess data is normalized and uniform.

[0468] - Performance outcomes are ranked and defined.

[0469] - Manual evaluation is provided forthe ranking criteria.

[0470] - Averaged consensus score is created for performance outcomes.

[0471] - Weighting factors are assigned to different performance metrics

[0472] The ‘Ranking’ score becomes the numeric used to interpret the relevance of the required control function. This complex algorithm uses permutation analysis and transitional tables to specify the way the data objects are ordered, the processing of data in the specified order and pattern recognition. With the highly specialised and sophisticated ‘Priority Queue’ structures the Master Artificial Intelligence Engine (MAIE) (Fig. 7 Item 37) can handle extremely large data sets and with many input variables.

[0473] As an example the ‘Ranking’ score may be low thus the Master Artificial Intelligence Engine (MAIE) (Fig. 7 Item 37) may respond with no action to control or adjust process functionality or flow. However, a high ‘Ranking’ score will most certainly create a control function that will administer an adjustment for process functionality and / or flow.

[0474] Fig. 7 represents one embodiment of the algorithm as used in the formation of the ‘Ranking’ scores.

[0475] The features that become relevant to the Master Artificial Intelligence Engine (MAIE) (Fig. 7 Item 37) are registered on the Master Artificial Intelligence Engine (MAIE) (Fig. 7 Item 37) database and automatically updated, by the Master Artificial Intelligence Engine (MAIE) (Fig. 7 Item 37), if and when necessary. This process has also a manual override function where the Remote Monitoring and Control Centre (RMCC) (Fig. 6 item 43) can at any time place ‘Manual Override Options’ (MOO) (Fig. 6 Item 137) to manually manipulate, refine, influence and / or adjust the “Ranking’ score and to specify the way the data objects are ordered. The relevance function has a direct bearing on the future outcome and completion ‘Rank’. The MOO is also necessary to supplement the collision resolution strategy where data sets have conflicting states.

[0476] As an example, Fig. 8 shows the Master Artificial Intelligence Engine (MAIE) 37 controlling the Security, Authentication and Heartbeat Activation Process.

[0477] In another example, Fig. 10 shows the Master Artificial Intelligence Engine (MAIE) 37 controlling the Real- Time Spot Carbon T rading Exchange and Blockchain with Smart Contracts.

[0478] In still another example, Fig. 5 shows the Master Artificial Intelligence Engine (MAIE) 37 controlling the Input Feedstock Imaging Recognition System.

[0479] The computational algorithms that form part of the core Master Artificial Intelligence Engine (MAIE) (Fig. 7 Item 37) are innovative and unique in their implementation. The invention provides both a useful waste processing plant as well as a useful resource creation platform. The operational and direct application of the ‘Ranking’ algorithm creates an enormously useful model design. Thus, the Master Artificial Intelligence Engine (MAIE) (Fig. 7 Item 37) with its database of facts and rules can deduce outcomes from any incoming data flow and convert those outcomes to control instructions for remote Processing Reactor Plants (PRP) (Fig. 6 item 31).

[0480] Additionally, with the many and varied ways to use the invention at its core is the ability to harness produced and derived energy at any specific terrain. Thus, rather than create infrastructure, such as data centres that are energy demanding, where an energy distribution grid is found, the invention facilitates for infrastructure to be created where required and the Processing Reactor Plant (PRP) (Fig. 6 item 31) is moved to the infrastructure location to provide the necessary energy. The Master Artificial Intelligence Engine (MAIE) (Fig. 7 Item 37) then delivers a continually refined process, manned or unmanned, that is honed for input feedstock, the environment that it operates in and the market demand for the generated output, whether that is energy and / or commodities.

[0481] The autonomous configuration, structured operation and data driven methods of the Processing Reactor Plant (PRP) (Fig. 6 item 31) limits the requirement of highly skilled process manpower at each local site. The derived system-wide data is used to optimise processes, identify areas of improvement and to make data-driven decisions based on real-world feedback and results.

[0482] KEY ITEM 4.

[0483] Various embodiments provide a totally self-contained, self-powered, small footprint, transportable Processing Reactor Plant (PRP) (Fig. 6 item 31) that can be easily relocated to anywhere in the world where organic waste feedstock is created which addresses the shortcomings of current and existing waste processing plants. It is a self-generating energy system that stores some or all of the energy that it selfgenerates, from the processing of organic waste input feedstock, plus the added energy that the system collects from external renewable energy derived from the solar collectors and / or wind energy harvesters.

[0484] Various embodiments focus on simplicity of design, efficiency of operation, autonomous functionally, flexibility of operations and as all the equipment is contained within a standard shipping container, as an example, it is extremely mobile and transportable. Organic waste can now be processed in various locations where the waste is generated. Renewable energy is generated, stored and locally distributed as the Processing Reactor Plant (PRP) (Fig. 6 item 31) is controlled and monitored from a remote site by the Remote Monitoring and Control Centre (RMCC) (Fig. 6 item 43). Once the organic waste is processed the transportable unit can then be redirected to a new location as the need arises.

[0485] There are many applications of the embodiments of the present invention. The system of one embodiment can be used as the platform for an Industrial Waste Processing Plant (IWP), Municipal Solid Waste Processing Plant (MSW) or Sewage Processing Plant (SPP). In this embodiment, the system forms the fundamental processing platform to manage all aspects of the required plant. Below is a detailed configuration of such a unit, with Figs. 16 to 20 illustrating examples of its implementation.

[0486] Fig. 16 details one example of the Converter Reactor Unit (CRU) 88, 88T, in a single reactor format, where the pre-treated (see Fig. 3 item 2) Input Waste Feedstock 600 enters the unit as a controlled and continuous process, automatically regulated by a lever and a sliding panel 602. The waste feedstock enters the conversion reactor chamber 603 where it is moved through the chamber by auger 476 driven by Motor599. The chamber is heated, by the gas powered Heater / Drier 601, to the appropriate temperature levels and monitored and controlled by sensors 88Sito 88Sn. Any additives to the waste feedstock mixture is inserted into the process from the Additives unit 608. This specialised chamber is designed for thermal decomposition of the organic waste material, in the absence of oxygen. It is and constructed from durable stainless steel to withstand temperatures of over 1000°C and is corrosion resistant with excellent thermal conductivity. This pyrolytic process breaks down the organic waste into smaller molecules, producing valuable products like Bio-Char, Bio-Oil, and Bio-Gas. The Bio-Char is channeled to the collection area 605 for storage 616 and further processing with a turbine (see Fig. 3 item 107C) to generate electricity (see Fig. 3 item 105). The Bio-Gas that is produced is collected 607 and fed into gas turbines 606, 609 to generate electricity for the auger motor 599 and the Heater / Drier 601. Additional Bio-Gas is channeled into the Heater / Drier 601 to raise the temperature inside the converter chamber and guided into the Condenser / Blender 610 to cool the gas allowing the separation of liquid fractions from the gas phase. The resultant Bio-Oil is stored 615 or used with a turbine (see Fig. 3 item 107L) to generate electricity (see Fig. 3 item 105). Safety features within the Converter Reactor Unit (CRU) 88, 88T include, but not limited to, pressure relief valves 603P, emergency venting systems 603V, and gas detection sensors 603G. These mechanisms, but not limited to, ensure the safe operation of the reactor and prevent the build-up of pressure orthe release of harmful gases.

[0487] Fig. 17 details another example of the Converter Reactor Unit (CRU) 88, 88T, in a dual reactor configuration that also includes Liquefied Fertiliser Injectors 598. The main configuration and production of Bio-Char, Bio-Gas and Bio-Oil is similar to those of Fig. 16 with the addition of a second conversion reactor chamber 604. The dual configuration allows for higher temperature operation, to above 1200 °C, in single or dual mode chamber operation. This reactor is driven by a Cooler and Motor unit 611, powered by the Gas Turbine 609 to cool and customise the Bio-Char properties as it leaves the first conversion reactor chamber 603. The Bio-Char is channeled to the collection area (cf. Fig. 16 item 605) for storage (cf. Fig. 16 item 616) and further processing with a turbine (cf. Fig. 3 item 107C) to generate electricity (Fig. 3 item 105). The organic liquefied fertiliser is injected into the heated Bio-Char for infusion into its crystalline lattice for creation of an organic fertiliser with Bio-Char soil rejuvenation properties. Safety features within the Converter Reactor Unit (CRU) 88, 88T include, but not limited to, pressure relief valves 603P, emergency venting systems 603V, and gas detection sensors 603G. These mechanisms, but not limited to, ensure the safe operation of the reactor and prevent the build-up of pressure or the release of harmful gases.

[0488] Fig. 18 illustrates another example of the Converter Reactor Unit (CRU) 88, 88T, in a dual reactor configuration with Liquefied Fertiliser Injectors 598 and also includes a Steriliser unit 617. The main configuration and production of Bio-Char, Bio-Gas and Bio-Oil is similarto those of Fig. 17 with the addition of a Belt Conveyer 618 that transports the input pre-treated waste through a Steriliser unit 617. At the end of this initial process the input waste is channeled to the first of the conversion reactor chambers 603 and proceeds to a similar process as that identified in Fig. 17. By utilising the technique as detailed in fig.18, this embodiment sterilises medical or critical research laboratory waste. The cooling process item 611 within the second conversion reactor chamber 617 lowers the temperature of the sterilised material to prevent recontamination by any residual heat-sensitive microorganisms. The two conversion reactor chambers 603, 604 generate a longer processing path with additional sensors 88Sito 88Sn to create a controlled environment for specific output products. This is a significant step as some byproducts, required in subsequent processes, require high tolerances and consistent quality specifications. Safety features within the Converter Reactor Unit (CRU) 88, 88T include, but not limited to, pressure relief valves 603P, emergency venting systems 603V, and gas detection sensors 603G. These mechanisms, but not limited to, ensure the safe operation of the reactor and prevent the build-up of pressure or the release of ha rmful gases.

[0489] As an example, converting used tyres to carbon black (identified as ‘C black’ in Fig. 1) and then subsequently transforming it into graphite requires high tolerance production with intricate process levels. To achieve high-quality graphite production, specific high-spec levels must be maintained throughout the pyrolytic and conversion processes. This unique and highly sophisticated conversion reactor chamber configuration provides the required process levels for each stage of the process. To convert used tyres to carbon black, following removal of the steel belts, the pyrolytic process should be about 1000 °C for efficient decomposition as the higher temperatures promote the breakdown of complex organic compounds. The dual conversion reactor chambers provide sufficient residence time within the chambers to ensure all components are fully decomposed into carbon black. The oxygen-free environment, within the pyrolytic chambers, prevent the formation of combustion by-products and guarantee the formation of pure carbon black. The Cooler and Motor unit (Fig. 18 item 611) provides efficient and rapid cooling as required by the carbon black after formation to prevent agglomeration. Once the high purity carbon black material is produced from the Processing Reactor Plant (PRP) (Figs. 2A & 2B item 31), with consistent quality production levels, it is subjected to an external graphitisation process, which exposes the material to extremely high temperatures of around 2500 °C to 3000 °C under controlled conditions.

[0490] Fig. 19 illustrates another example of the Converter Reactor Unit (CRU) 88, 88T, in a dual reactor configuration with Liquefied Fertiliser Injectors598 and a Steriliser unit 617. The main configuration and production of Bio-Char, Bio-Gas and Bio-Oil is similar to Fig. 18 with the addition of multiple Organic Rankine Cycle (ORC) generators 620 (cf. Fig. 18 item 620), as an example, that use Bio-Gas 607 produced from the main conversion reactor chamber 603 (with auger 476 driven by motor 599) and channeled into a heat exchanger 621 that is common to all the generators. This embodiment also includes a second inverter reactor 604 that incorporates an auger 476A and a cooler and motor assembly 611 to drive auger 476Aand to cool the bio-char 605.

[0491] The Organic Rankine Cycle (ORC) is a thermodynamic process, using organic fluids, that converts heat into mechanical work. The internal turbine is connected to a generator to produce additional localised renewable electricity 622 that is stored in batteries 614 or processed further by the Energy Control unit (cf. Fig. 3 item 106). The Organic Rankine Cycle (ORC) generators 620 can efficiently utilise low-temperature heat sources 607, making them useful for renewable energy applications such as in this embodiment. Safety features within the Converter Reactor Unit (CRU) 88, 88T include, but not limited to, pressure relief valves 603P, emergency venting systems 603V, and gas detection sensors 603G. These mechanisms, but not limited to, ensure the safe operation of the reactor and prevent the build-up of pressure or the release of ha rmful gases.

[0492] Fig. 20 illustrates another example of a Converter Reactor Unit (CRU) 88, 88T, in a single reactor configuration (Reactor 1) 462 with dual pyrolytic chambers— Chamber 1477 and Chamber 2482— attached to respective condensers 494, 502. This is a smaller and more compact configuration than the configurations detailed in Figs. 16 to 19, as the distributed dual pyrolytic chambers Chamber 1477 and Chamber 2482 provide significant process flexibility. The input waste feedstock 600 can be inserted manually, or loaded automatically as the application requires, via the head loading unit 503 which includes clamping 444, shredding 447, 448, 449, cutting 451 as it is squeezed and compressed 441 by a segmented screwed 437, 445 knob 438 of the lid 442 with an automatic spring opener 446. The continuous process and rate of flow is controlled by a regulator 461 with a lever 454 and a sliding panel 460. Reactor 1 462 has two exit openings, with the first opening 466 connecting to pyrolytic processing Chamber 1477 and the second opening 468 connecting to pyrolytic processing Chamber 2482. Each of these openings work as automated sequential stages to empty the reactor’s processed load into each pyrolytic chamber in alternating sequence. They are never in the same state as each other i.e. one opening is closed while the other is open and vice versa. Thus the time consuming pyrolytic process in each chamber 477, 482 has sufficient time to process the waste properly and efficiently, allowing the Reactor 1462 to operate continually.

[0493] The commencement process sequence, as an example, has all entry and exit slides 454, 463, 471, 481, 484, 473, 489 fastened and sealed shut. To process the first load of input waste feedstock Reactor 1 opens the slider 454 to allow sufficient waste to enter and then closes it when full load capacity is reached. On completion of the conversion process Reactor 1 opens the slider 463 and empties its contents into pyrolytic Chamber 1477. Once all the contents are inside Chamber 1, the slider 463 is closed and Reactor 1462 repeats its loadingof input waste feedstock again. Chamber 1477 is actively converting the waste load it has just received, independently with differing control configurations and routines in parallel with Reactor 1462. To process the second load of input waste feedstock Reactor 1 opens the slider 454 to allow sufficient waste to enter and then closes it when full load capacity is reached. On completion of the conversion process Reactor 1 opens the input slider 471 for Chamber 2 and empties its contents into pyrolytic Chamber 2482. Once all the contents are inside Chamber 2, the slider 471 is closed and Reactor 1462 repeats its loadingof input waste feedstock again. Chamber 2477 is actively converting the waste load it has just received, independently with differing control configurations and routines to Chamber 1 and in parallel with Reactor 1462. As Chamber 1477 and Chamber 2482 complete their processing cycles, independent to Reactor 1462, Bio-Char is produced and collected via respective conduits 493, 491 in respective bio-char storages 497-1, 497-2 (separated by removable partition Pl); Bio-Gas is produced by pyrolytic chambers 477, 482 and can be collected via respective conduits 475, 495 and 490, 499 in respective bio-gas storages 498-1, 498-2 (separated by removable partition P2). Bio-Oil is produced by each Condenser / Blender unit 494, 502 from Bio-Gas produced by pyrolytic chambers 477, 482 and fed into Condenser / Blender units 494, 502 via conduits 475, 490 respectively. The Bio-Oil is then collected via respective conduits 496, 501 in respective bio-oil storages 500-1, 500-2 (separated by removable partition P3).

[0494] In this way different grades and specifications of Bio-Char, Bio-Gas and Bio-Oil are produced from common waste in put feedstock. Supplementary processing units such as Sterilisers (cf. Fig. 19 item 617), Liquefied Fertiliser Injectors (cf. Fig. 19 item 598) and Organic Rankine Cycle generators (cf. Fig. 19 item 620), as examples can also be accommodated and included into various embodiments of the present invention. Safety features within the Converter Reactor Unit (CRU) 88, 88T include, but not limited to, pressure relief valves 462P, emergency venting systems 462V, and gas detection sensors 462G. These mechanisms, but not limited to, ensure the safe operation of the reactor and prevent the build-up of pressure orthe release of harmful gases.

[0495] The system configurations detailed in Figs. 16 to 20 provide a balance of functionality, robustness and price and with the following improvements over existing methods:

[0496] There are many applications of the systems according to these various embodiments, such as an Industrial Waste Processing Plant (IWP), Municipal Solid Waste (MSW) processing plant or treated Sewage Processing Plant (SPP). In this embodiment, outlined transportable, small footprint systems form the fundamental processing requirement such as input, preparation, processing and output stages to manage all aspects of the plants.

[0497] By implementing any of the identified configurations, a transportable organic waste processing unit within a shipping container can efficiently process organic waste while being mobile and adaptable to different locations and waste feedstocks.

[0498] KEY ITEM 5.

[0499] Organic waste management has for centuries being a major issue for human civilisations. Many forms of process control, disposal management and disease minimisation have been established in developed and developing countries where human existence is ever increasing on a daily basis. With increasing population, reduced water levels, depleting land, soil erosion, diminishing food levels and decreasing availability of landfill sites comes major global issues for human existence. There is a balance that is needed between these five identified areas of sanitation, the environment, energy, economics and society. Waste resource, once harvested and harnessed in a proper manner, can become the balance between all the above issues. Many large industrial processing plants have been built around the world with varying degrees of success. To date none have been produced in a compact, small footprint for domestic or household use.

[0500] Various embodiments of the invention provide an entiely self-contained, self-powered, small footprint, easy to install system to process waste locally within domestic, commercial or industrial sites. The invention focuses on simplicity of design, efficiency of operation, autonomous functionally and flexibility of operations. This waste to energy converter system, takes in kitchen bio-wastes, treated human excreta, from existing toilets and produces renewable energy, in situ, for local consumption or storage which addresses the shortcomings of current and existing waste processing plants.

[0501] The self-generating energy system of this embodiment may store some or all of the energy that it selfgenerates, from the processing of organic waste input feedstock, plus the added energy that the system integrates with external renewable energy, derived from batteries, hydrogen fuel cells, solar collectors and / orwind energy harvesters, as examples. The remote controlled and monitored unit also produces commodities or by-products and the generation of carbon credits. This new embodiment creates a new waste processing and sanitation system with management methods with the following features:

[0502] Ecological human waste processing at the generation point

[0503] Organic matterand nutrients conservation

[0504] Pathogen reduction

[0505] Odor suppression

[0506] Water recovery

[0507] Uses existing toilet system

[0508] Cost effective process

[0509] Local energy conversion plant (w2e)

[0510] Local energy storage plant (solar, wind, w2e)

[0511] Environmental waste management is a critical global concern. Traditional waste processing methods often lack efficiency and environmental friendliness. The system of various embodiments integrates multiple artificial intelligence agents, cutting-edge algorithms and self-contained configuration to revolutionise waste processing, reduce ecological footprint and improve resource recovery.

[0512] Various embodiments relate to an innovative algorithm-based environmental waste processing system and method. The system incorporates a small, compact and self-contained, in situ organic waste processing unit, eliminating external routine collections of household organic waste and the discharge of household sewage to remote treatment plants. All organic waste is processed at the point of origination. The system utilises advanced algorithms to optimise waste processing operations, enhance efficiency, and minimise environmental impact. This patent application outlines the algorithmic processes, their implementation, and provides examples of their application in various waste management scenarios.

[0513] The diagrams detailed in fig.15 should be read in conjunction with the process flow schematics as detailed in Fig. 14.

[0514] This system removes any water in the organic waste, by the reactor converter (Fig. 15 items 416, 425), contrasting to current methods that use excessive water to transport human waste from toilets via sewage pipes to inefficient central treatment processing plants. These large central sewage treatment plants are expensive to establish, expensive to maintain and highly inefficient. Converter reactor process (Fig. 15 item 420) can be used to avoid the discarding of domestic organic waste (Fig. 15 item 347) into landfills, there is maximum resource recovery with no additional ha rmful effects to the environment. The in situ unitswill allow consumers to truly generate energy to place back into the power grid. This carbonnegative process creates clean commodities from waste material.

[0515] This embodiment, illustrated in Figs. 14 and 15, provides a Micro Processing Reactor Plant (MPRP) for processing waste and treated sewage, from households, small commercial and industrial sites. Referring to Fig. 15, the localised, in situ unit processes organic Input Waste Feedstock 347 such as Bio-Waste 355, creates commodity outputs from resource recovery 400, generates renewable green energy 405 and produces recycled potable water 401. Today's treated sewage and waste converters focus on large installations to process huge volumes of waste material. This system is a compact and small unit, as an example, suitable for connection and operation in, domestic households, small industrial or commercial sites. This embodiment may provide, as an example, a small fixed household installed unit or a compact moveable mobile unit. This embodiment provides a self-contained unit, that can be vertically fabricated to process and convert waste products into re-usable commodities, by-products or generated renewable energy.

[0516] The input waste feedstock 347, comprising of domestic Bio-waste 347A or domestic sanitised waste 351, is directed into the Preparation Head Unit 420 where all the input waste is prepared for processing by the micro pyrolytic converter 425. The Head Preparation Unit 420 comprises of three processing chambers, the initial mini vortex chamber 415, the shredding and dewatering chamber 416 and the measuring, analysis and the additives chamber 418. Within the first chamber 415 of the Preparation Head Unit 420 there are shredders or liquefiers, as an example, to reduce the waste to optimum levels of size, to about 25mm in diameter, that are beneficial to the ensuing pyrolytic process. One such size reduction method, as an example, is by using a mini high speed vortex unit with appropriate noise suppression covers. The waste then progresses to the second chamber 416 for dewatering with the on-board Heater / Drier419, powered by the generated Bio-Gas 426 prior to it being processed by the Condenser / Blender429. The waterthat is removed by this drying process is channelled to the output water collector 401 forfurther processing by the steam fired turbine 405 to generate electricity 405. Once the Preparation Head Unit 420 completes its initial two processes, the third head chamber 418, uses sensors 88S to measure the required waste moisture content to 10% levels. As moisture and sizing levels are obtained the slider423 automatically opens to pass the waste into the, sealed and oxygen free, micro pyrolytic converter425. The auger 427 moves the waste through the chamber where sensors 88Si, 88S2,88Snmonitor the organic characteristics of the processing waste and accordingly control the conversion process. The Bio-Char produced by the pyrolytic converter 425 is channelled to the Bio-Char output holding area 404 after the slider opens 436. The Bio-Gas that is produced during the conversion processes is channelled 426 to the Condenser / Blender unit 429 for further conversion to Bio-Oil 403. Prior to entering the Converter / Blender unit 429 some of the Bio-Gas is directed 431 to a Bio-Gas storage unit 402 for further processing to electricity via the micro turbine 405 as well as directed to the Heater / Drier unit419.

[0517] To maximise the recovery of valuable resources 400 from the input waste feedstock 347, optimise the reactor processing conditions 425 and generate renewed energy 373 this embodiment utilises algorithms (cf. Fig. 14 items 367, 393, 233) to determine optimal operational conditions. The resultant reactor commodity outputs 400 as Bio-Gas 402, Bio-Oil 403, Bio-Char 404 and Water 401, which can be used to drive mini generators or relevant turbines 405 for energy generation, for local storage 383, integration with other energy generators such as solar or wind 384 or distribution 406 into the power grid 288. The Reactor Application and Energy Management Unit (EMU) 408 aggregates all available locally generated energy and via the Micro Reactor Administration and Control Manager (RAM) 388 energises all the required operational applications, modules, services and electronics 428 with the supervision and overview of the Internal Control Manager (ICM) 421. The operational applications 428 include but are not limited to, Control Modules, Electronics Modules, Internal Communications Modules, External Communications Modules, GPS locations Modules, Authentication Modules, Security Modules and Micro Artificial Intelligence (MAI) Modules. Each Module comprises of their respective hardware, software, firmware and interfaces.

[0518] The embodiment incorporates a multi artificial intelligence arrangement with system-wide Premises Master Artificial Intelligence Control Module (PMAI) 367A and a dedicated localised converter artificial intelligence Micro A.1. 428 for the optimisation of localised in situ pyrolytic processing of organic waste feedstock and the integration of artificial intelligence algorithms 367 with Internet of Things (loT) sensors 88Si, 88S2, 88Sn, 88SHand control systems 362. The sensors may include, but not limited to measuring and controlling temperature, pressure, flow rates, humidity, and emissions and are strategically placed in the pyrolytic chamber 88S1, 88S2, 88Snand a long feedstock pathways 88SHwith data transmission to the Communications module (cf. Fig. 14 item 363) via the Gateway Administration and Control Manager (GAM) (Fig. 14 item 360). The data acquisition frequency at which sensor data is collected (in seconds or milliseconds) for real-time monitoring, is controlled and managed by the Micro Reactor Administration and Control Manager (RAM) (Fig. 14 item 388) with storage provided in the Storage database (Fig. 14 item 395) using Reactor ProcessingAlgorithms (RPA) (Fig. 14 item 393). Emissions monitoring sensors 88SHand processes are integrated to ensure compliance with environmental regulations. The collected data (Fig. 14 item 396) is transmitted (Fig. 14 item 389) to the Gateway Administration and Control Manager (GAM) (Fig. 14 item 360) for system-wide analysis and verification. The Gateway Administration and Control Manager (GAM) (Fig. 14 item 360) is capable of handling large volumes of sensor data, fusing data from multiple sensors, via the Gateway Processing Algorithm (GPA) (Fig. 14 item 367), to obtain a comprehensive view of the pyrolytic reactor process 425 and to ensure data accuracy.

[0519] The Premises Master A.I. (PMAI) (Fig. 15 item 367A) administers and controls all procedures, with the Premises Gateway Control (PGC) (Fig. 15 item 362) under instructions from the Remote Monitoring and Control Centre (RMCC) (Fig. 15 item 43) via the Internet (Fig. 15 item 319). Control functionality covers, but not limited to, telemetry for utility meters and associated devices (Fig. 15 item 356), User administration Interfaces (UAI) (Fig. 15 item 341), including media and content (Fig. 14 item 342), energy information (Fig. 14 item 343), medical information (Fig. 14 item 344) and entry into an online personal portal (Fig. 14 item 345). Similarly, the Gateway Administration and Control Manager (GAM) (Fig. 14 item 360) administers functions relating to both internal and external communications (Fig. 14 item 363), receiving and transmitting media content (Fig. 14 item 364), health and medical related data (Fig. 14 item 365) that may be analysed, as an example, from the sanitary waste in the Head Preparation Unit (Fig. 15 item 420) with sensors (Fig. 15 item 88SH), services delivery processes (Fig. 14 item 366), specific applications relevant to the resident or household (Fig. 14 item 373) and online data toolkits (Fig. 14 item 373A) for additional customised applications. The Premises Master A.I. (PMAI) (Fig. 14 item 367A) also uses the RULES and Facts Database (Fig. 14 item 372), the Premises Gateway Control unit (PGC) (Fig. 14 item 362), in conjunction with the Storage database (Fig. 14 item 369) to meticulously orchestrate all required processes and effectively use the internal Machine Learningfunctionality (ML) (Fig. 14 item 370) to constructand adapt specific, personalised processes, procedures and actions relating to each individual household. This adaptive learning behaviour reacts and responds to dynamic system conditions without user interaction. As an example the Premises Product Usage (PPU) (Fig. 14 item 374) monitors activities relating to, but not limited to, heating and cooking (Fig. 14 item 377), toilet flushing (Fig. 14 item 376), byproductfertiliser production (Fig. 14 item 378), power generation and consumption (Fig. 14 item 379), rainwater harvesting (Fig. 14 item 380), by-product storage capacity (Fig. 14 item 382) and connectivity to any other data generating devices (Fig. 14 item 375) that may be essential to the Smart House Interface (Fig. 14 item 346H) orthe Smart City Interface (Fig. 14 item 346C). The Premises Master A.I. (PMAI) (Fig. 14 item 367 A) also uses the localised RULES and Facts Database (Fig. 14 item 398), within the Micro Processing Reactor Plant (MPRP) (Fig. 14 item 386), to predict equipment failures by analysing historical data, enable routine proactive maintenance, reduce downtime and improve overall operational efficiency.

[0520] Continous system-wide monitoring via the Monitor module (Fig. 14 item 349) collects and analyses realtime data from sensors such as examples from the Preparation Head Unit sensors (Fig. 15 item 88SH), the pyrolytic chamber sensors (Fig. 15 items 88Si,88S2,88Sn), telemetry from utility meters and devices, such as gas, water and electricity (Fig. 14 item 356), and also from health and medical sensor data (Fig. 14 item 350). The Personal Portal (Fig. 14 item 345), along with the Online Data Toolkit (Fig. 14 item 373A), is utilised to create customised, personalised and service specific applications for the household or individual residents. The Reactor Processing Algorithms (RPA) (Fig. 14 item 367) continually monitors and automatically adapts to any changing conditions by incorporating a structured approach to quality control, integrating threshold analysis and process optimisation techniques.

[0521] Carbon T rading Exchange:

[0522] Referring to Fig. 14, Blockchain technology and smart contracts, as detailed in this embodiment, have the potentialto revolutionise various aspects of residential and household services, includingservices 366, media content 364, and energy provision 406. Blockchain, with a high-level of security, provides a decentralised and transparent platform for transactions relatingto billing, payments, personal data and information services. Utilising blockchain, households can maintain a digital ledger of their assets including warranty information, maintenance history, and even a digital twin of the house, making it easier to manage or sell their property. Smart contracts 69P are self-executing contracts with the terms of the agreement directly written into code. For household services like cleaning or maintenance, smart contracts 69P can automate payment once the service 366 is completed, ensuring both parties fulfil their obligations. The Rules and Facts Database 372 can hold all information as required to complete a household Smart Contracts 69P relating to reactor output 400, available energy for distribution 406, battery or hydrogen fuel cell information or service application specifics, as examples. The Storage database 395 can also provide recipient beneficiary data, event timelines or executable dates to complete any Smart Contract as required by the householder or resident or small business operator. In this embodiment smart contracts can also facilitate peer-to-peer energy tradingwithin residential communities. As energy is created from internal sources such as the Reactor Output 400 and externally from solar or wind energy 384, the Total Energy Storage unit 383 can make some energy available to the Available Energy for Distribution 406 as excess to the household requirement. This excess renewable energy can be sold into the grid 288 or, as an example, to connected neighbours in the community, by using the inbuilt Smart Contracts 69P feature. Blockchain and Smart Contracts 69P are utilised for carbon trading activities through the Carbon Trading Exchange (CTE) (cf. Fig. 15 item 45) as this embodiment is carbon negative, with carbon sequestration potential and complies with Circular Economy principles.

[0523] When Bio-Char, which is rich in stable carbon, is added to soil, it can enhance soil fertility and structure while storing carbon. This sequestration of carbon dioxide (CO2) from the atmosphere is a keyfactorfor earning Certified Emission Reduction (CER) credits under the Clean Development Mechanism (CDM) of the Kyoto Protocol. As this embodiment integrates the production of Bio-Char (Fig. 15 item 404) with bioenergy generation (Fig. 15 item 383) in a way that reduces greenhouse gas emissions compared to conventional energy sources, it qualifies for CER credits as verified by the United Nations Framework Convention on Climate Change (UNFCCC). The reduction in emissions is calculated concerning a baseline scenario, and the credits are given for the avoided emissions. Thus, this process will be registered and itemised to receive CER credits. The GPS location Unit (Fig. 15 item 482) combined with the Premises Gateway Control unit (PGC) (Fig. 14 item 362) will monitor in real time, all the input (Fig. 15 item 347), processing (Fig. 15 item 425) and output (Fig. 15 item 400) to provide a Carbon Trading Exchange (CTE) (Fig. 15 item 45) data authentication towards carbon credits. The Blockchain and Smart Contracts module (Fig. 14 item 69P) synchronising with the Carbon Audit and Trading Engine (CATE) (Fig. 14 item 255) ensures trust and transparency, for the derived Carbon Trading sensitive data. As the Reactor Output (Fig. 14 item 400) registers the Bio-Char production (Fig. 14 item 400), from the pyrolytic reactor (Fig. 15 item 425), carbon credits are generated and lodged for sale with the Carbon Trading Exchange (CTE) Biochar has the potential to improve soil quality, increase agricultural productivity, and reduce the need for chemical fertilizers. By promoting sustainable agricultural practices, biochar projects can contribute to reducing emissions associated with conventional farming methods. The Smart Contracts (Fig. 15 item 69P) verify the authenticity of the data accumulated on the blockchain digital ledger and then facilitate for the financial transaction. Bio-Char has the potential to improve soil quality, increase agricultural productivity, and reduce the need for chemical fertilisers. By promoting sustainable agricultural practices, Bio-Char projects can contribute to reducing emissions associated with conventional farming methods.

[0524] Gateway Administration:

[0525] Again referringto Fig. 14, the User Administration Interface (UAI) module 341 displays a Graphical User Interface (GUI) 346 for operators, residents or other users to monitorthe system, adjust settings, and view real-time data as required. Alert mechanisms of critical issues or deviations from quality standards are incorporated via the Micro Reactor Administration and Control Manager (RAM) 388 historical Storage database 395 and reports for analysis and compliance reporting are generated from the Gateway Administration and Control Manager (GAM) 360 Storage database 369. Historical data, from all household systems is aggregated in the Remote Monitoring and Control Centre (RMCC) 43 Storage (R) 40 for analysis and any compliance reporting 133. Data backup and recovery routines, to prevent data loss, are managed by the Gateway Administration and Control Manager (GAM) 360 via the Services module 366 that also provides Premises Off-line Access Control (POA) 340. Access and process security is provided bythe Reactor Security Manager (RSM) 397.

[0526] Hydrogen:

[0527] Referringto Fig. 15, as all the essential components to produce hydrogen are provided the illustrated embodiment of the Micro Processing Reactor Plant (MPRP), it is proposed that hydrogen be produced in situ.

[0528] The embodiment incorporates an inbuilt process to generate hydrogen, with an electrolyser401H, through electrolysis 401H, a process that splits water (H2O) into hydrogen (H2) and oxygen (O2) usingthe generated electricity 405. A hydrolysercan also be used to break down the organic materials or compounds derived from the pyrolytic process by reacting them with water. Turbine generators 405 as powered by their respective sources, such as Bio-Gas 402 into the gas fired turbine, Bio-Oil 403 into the oil fired turbine and Bio-Char 404 into the char fired turbine to produce renewable electricity. By utilising these renewable sources to power the turbine generators, the entire process becomes eco-friendly and sustainable. Either individually, or as an aggregated source, via the Total Energy and Hydrogen Storage unit 383, that also combines external energy generated by solar and wind energy sources 384, electricity is provided to the Electrolyser401H, a device that performs electrolysis. Electrolysers have two electrodes, an anode (positive) and a cathode (negative), separated by an electrolyte solution. When electrical current 405, 383 passes through the water 401 in the electrolyser401H is broken down into hydrogen gas and oxygen gas according to the chemical reaction: 2 H20 (liqu id) -> 2H2(gas) + O2(gas)

[0529] The produced hydrogen gas is then collected and stored in a high-pressure tank or other suitable storage systems 383H. These tanks are designed to store hydrogen safely at high pressures to maximize storage capacity within the limited space of a household.

[0530] When energy is needed, by the Micro Reactor Plant (MRP) 399, the stored hydrogen is fed into a fuel cell. A fuel cell combines hydrogen gas from the storage tank with oxygen from the airto produce electricity, water, and heat through the electrochemical reaction:

[0531] 2H2(gas) + O2(gas) -> 2H2O(li qu id) + electricity

[0532] The electricity generated by the fuel cell is then used to power the Micro Reactor Plant (MRP) 399, and any other household appliances via the Reactor Application and Energy Management Unit (EMU) 408 and the Premises Product Usage (PPU) 374. This electricity can be used directly in the home or stored in batteries 383H for later use when demand is higher or when renewable sources are not generating electricity 384 (e.g., at night for solar power systems). The Premises Gateway Control unit (PGC) 362 using the Premises MasterA.I. (PMAI) 367Avia its Telemetry Utility Meters and Devices 356 maysupplyany excess energy to other connected communal subscribers by using the Smart City Interface (SCI) 43C or the Available Energy and Hydrogen For Distribution module 406 to the main power grid local communal Micro-Grid 288 as applicable. The advantages of this approach is that hydrogen fuel cells produce electricity without emitting greenhouse gases or pollutants, making it a clean energy solution and providing energy independence from the main energy grid.

[0533] Data Flow:

[0534] Referringto Fig. 14, the independent Premises Management and Control (PMC) 341 and the Premises Gateway Control (PGC) 362 utilise distributed, decentralised management functionality to process, control, collect, administer, authenticate and validate all localised data underthe remote management of the Master Artificial Intelligence Engine (MAIE) 37 via the Remote Monitoring and Control Centre (RMCC) 43.

[0535] The characteristics and functionality of the Micro Processing Reactor Plant (MPRP) 386 is established via a remote Administration Management and Control (AMC) 32 integrating a Master Artificial Intelligence Engine (MAIE) 37, with data lookup tables with data records in Rules and Facts database 41, algorithms used to perform computations 233, instructions, actions and self-contained applications in the Micro Reactor Application Engine (MRE) 410, Premises Management Application Engine (PME) 412, Universal Security Registry (USR) 41U and associated Data Analysis (R) 133 within a Remote Monitoring and Control Centre (RMCC) 43, as an example. The Master Artificial Intelligence Engine (MAIE) 37 also manages the actions, activities and undertakings for the Blockchain and Smart Contracts 69 by recording all the necessary information relating to the input stages, from all the relevant functions such as the input (cf. Fig. 15 item 347), the arrangement and pre-processing activities (Fig. 15 item 420), the conversion reactor functions (Fig. 15 item 425) and the direct connection to the Carbon T rading Exchanges (CTE) (Fig. 15 item 45). The Administration Management and Control (AMC) (Fig. 14 item 32) manages the Carbon Trading Algorithms (CTA) (cf. Fig. 10 item 256) and via the Applications Management (Fig. 10 item 35) controls the Carbon Audit and Trading Engine (CATE) 255. The Master Artificial Intelligence Engine (MAIE) 37 manages the actions, activities and undertakings for the Blockchain and Smart Contracts 69.

[0536] The real-time spot Carbon Trading Exchange (CTE) (see Fig. 15 item 45), the Remote Monitoring and Control Centre (RMCC) (Fig. 15 item 43), the Blockchain and Smart Contracts (Fig. 15 item 69P), the Premises Master A.I. (PMAI) (Fig. 15 item 367A) and the Micro Reactor Administration and Control Manager (RAM) (Fig. 15 item 388) are all interlinked via the Premises Gateway Control (PGC) (Fig. 15 item 362). Within the Remote Monitoring and Control Centre (RMCC) (Figs. 2A& 2B item 43) the Administration Management and Control (AMC) (Figs. 2A& 2B item 32) utilises Algorithms (R) (Figs. 2A& 2B item 36), Applications (R) (Figs. 2A& 2B item 35) and Rules and Facts (R) (Figs. 2A& 2B item 41) to provide automated maintenance routines. Proactive maintenance, reducing downtime and improving operational efficiency is achieved by analysing historical data.

[0537] Safety features within the Micro Reactor Plant (MRP) (Fig. 15 item 399) include, but not limited to, pressure relief valves (Fig. 15 item 603P), emergency venting systems (Fig. 15 item 603V), and gas detection sensors (Fig. 15 item 603G). These mechanisms, but not limited to, ensure the safe operation of the reactor and prevent the build-up of pressure or the release of harmful gases.

[0538] Smart City Interface:

[0539] Integrating a household pyrolysis process into a smart city development (Fig. 14 item 346C) involves the use of advanced technologies to optimise waste processing (Fig. 15 item 425), renewable energy generation (Fig. 15 item 405), and environmentally sustainable energy distribution methods (Fig. 15 item 406). This embodiment incorporates a Micro Processing Reactor Plant (MPRP) (Fig. 15 item 399), equipped with a pyrolytic converter (Fig. 15 item 425), advanced Internet of Things (loT) sensors (Fig. 15 items 88Si, 88S2, 88Sn, 88SH) and artificial intelligence controlled systems (Fig. 14 item 367A). The highly optimised pyrolysis process is monitored continuously, to ensure the Micro Processing Reactor Plant (MPRP) (Fig. 15 item 399) operates efficiently and safely, converts the organic input waste feedstock (Fig. 15 item 347) into Bio-Char (Fig. 15 item 404), Bio-Gas (Fig. 15 item 402) and Bio-Oil (Fig. 15 item 403). Each of the reactor output commodities (Fig. 15 item 400) are utilised within the city framework. Bio-Char (Fig. 15 item 404) is used as a soil amendment in urban agriculture projects, Bio-Gas (Fig. 15 item 402) and Bio-Oil (Fig. 15 item 403) used as renewable energy to power streetlights, public buildings and the generated energy (Fig. 15 item 383) used for electric vehicle charging stations. Within the system, the Available Energy and Hydrogen for Distribution module (Fig. 15 item 406) provides energy to a smart power grid, or communal micro-grid. Demand-response mechanisms, to balance energy supply (Fig. 15 item 406) and demand (Fig. 15 item 408), is controlled in real-time by the Premises Product Usage module (PPU) (Fig. 14 item 374). By integrating the compact household Micro Processing Reactor Plant ( RRP) (Fig. 14) into a smart city development, cities can effectively manage organic waste, reduce greenhouse gas emissions, generate renewable energy and promote a more sustainable urban environment.

[0540] Cleaning & Maintenance:

[0541] In the micro reactor pyrolytic chamber (Fig. 15 item 425), organic input waste feedstock (Fig. 15 item 347) is subjected to high temperatures in the absence of oxygen, leadingto thermal decomposition. To facilitate self-cleaning, the inner walls of the chamber can be coated with a non-stick material (such as ceramic or high-temperature resistant polymer) that prevents the accumulation of residues. The chamber's internal temperature is maintained above 500°C to ensure complete pyrolysis of organic waste. At this temperature, most organic materials break down into gases, liquids, and char, leaving minimal residues. A mechanical or automated scraping system periodically sweeps the chamber walls to dislodge any remaining particles. These particles, along with ash and charred residues, are collected and removed from the chamber. To further ensure cleanliness, a backflush cleaning method is engaged. This method involves injecting a high-temperature steam (above 700°C) into the chamber, via the inlet (Fig. 15 item 426), in the opposite direction of the usual waste flow, with the slider (Fig. 15 item 423 open and the Head Unit top (Fig. 15 item 414) being closed. The high-temperature back flush effectively purges any remaining residues, ash, or volatile gases sticking to the chamber walls and the head processing unit. Afterthe backflush, any gases, detected by the gas sensor (Fig. 15 item 603G), are passed through a scrubbing system (Fig. 15 item 422), to remove impurities and pollutants before being released into the atmosphere. Scrubbing can involve wet scrubbers, filters, or other purification methods.

[0542] The pyrolytic chamber is equipped with temperature sensors (Fig. 15 items 88S 88S2, 88Sn) and (Fig. 15 item 88SH), and automation systems that continuously monitor and control the chamber's internal temperature (Fig. 15 item 428). This method ensures that the chamber operates within the specified temperature range for effective pyrolysis and cleaning. Data from the sensors are analysed to optimize the cleaning cycles. Machine learning algorithms (Fig. 14 item 370) with the Rules and Facts Database (Fig. 14 item 41) are applied to predict the ideal cleaning intervals based on the type and quantity of organic input waste feedstock (Fig. 14 item 347) previously processed. Implementing this self-cleaning and back flush cleaning method in the pyrolytic chamber (Fig. 15 item 425), ensures efficient processingof organic input waste feedstock (Fig. 15 item 347), while minimising residues and maintaining the chamber's longevity and effectiveness. Additionally, it promotes environmental sustainability by reducing emissions and pollution associated with incomplete pyrolysis.

[0543] KEY ITEM 6.

[0544] The diagrams detailed in fig.13 should be read in conjunction with the process flow schematics as detailed in Fig. 6. Rural and regional areas often lack electricity and reliable communication networks. Embodiments of the present invention address these challenges by combining organic waste processing, sustainable energy generation and advanced communication technologies in transportable units, to create a resilient and flexible ad hoc communication network with autonomous operation in isolated regions. This innovation has the potential to significantly improve living conditions and emergency response capabilities in isolated and disaster-prone areas which addresses the shortcomings of current and existing waste processing plants.

[0545] Referring to Fig. 13, the illustrated embodiment provides a system for generating sustainable electricity (cf. Fig. 3 item 5) in remote and rural locations, through the internal pyrolytic conversion of organic waste (cf. Fig. 3 item 3) within transportable units 650, 651, 652, 653 as Processing Reactor Plants (PRP) (cf. Figs. 2A& 2B item 31), equipped with advanced communication control units (Fig. 6 item 176) to create an ad hoc communication network. Each PRP is fitted with a satellite dish (Fig. 12 item 290), for satellite communication (Fig. 6 item 138) and 315, connects to local wire less towers (Fig. 6 item 138) and 321, interconnects to the internet (Fig. 6 item 138) and 319, interconnects to other Processing Reactor Plants (PRP) (Fig. 6 item 140) and 650, 651, 652, 653 and integrates with first responder communication links (Fig. 6 item 162) and 331, 332, 336. All units are connected to the centralised remote monitoring and control centre 43, facilitating efficient management of organic waste processing, power generation and communication networks.

[0546] Each transportable unit 650, 651, 652, 653 is equipped with a Processing Reactor Plant (PCC) 31A, 31B, 31C, 31D (and see Fig. 6 item 31) capable of processing organic waste (Fig. 3 items 1, 2) and converting the waste (Fig. 3 item 3) into electricity through pyrolysis (Fig. 3 item 5), ensuring a continuous and sustainable power supply. The generated electricity is stored in high-capacity batteries (Fig. 3 item 104) to provide a stable power source even during periods of low organic waste availability. Additionally, the Energy Generation Storage and Control unit (Fig. 11 item 5) provides energy output (Fig. 11 item 293) to the external Direct Energy Storage (Fig. 11 item 294) that combines the energy generated by Solar and Wind sources (Fig. 11 item 76E) to then process to the Energy Distribution Process unit (Fig. 11 item 287) to either connect to the Energy Power Grid (Fig. 11 item 288), to other external connections (Fig. 11 item 273) or used as External Energy to Transportable Units (Fig. 11 item 286). Similarly, the Direct Energy Storage (Fig. 11 item 294) that combines the energy generated by Solar and Wind sources (Fig. 11 item 76E) can provide energy back into the high-capacity batteries (Fig. 3 item 104) to stabilise power source requirements during periods of low organic waste availability.

[0547] Wireless Network:

[0548] The multi-modal, digital communication network, with voice, video and data (Fig. 6 item 155), also incorporates fixed location, small, compact domestic, commercial and industrial Micro Processing Reactor Plants (MRPP) (Fig. 13 items 386A, 386B) and (Fig. 14 item 363). This private independent wireless network provides improvements as an overlay network, to any existing wireless coverage areas and provides new wireless coverage where there are no existing services.

[0549] The Plant Communication and Control (PCC) (Fig. 6 item 25) network is designed to connect local communities to fast wireless communications (Fig. 6 item 155), protocol based security (Fig. 6 item 160), and accessible connectivity (Fig. 6 items 142, 143, 144) to services including health, social care, education and first responders. As the community grows so too the amount of waste that is generated, with additional Processing Reactor Plants (PCC) (Fig. 6 item 31) required to process the waste which in turn creates additional network capacity with more communication nodes in each installed Processing Reactor Plant (PCC) (Fig. 6 item 31). By simply processing the local waste in the Processing Reactor Plants (PCC) (Fig. 6 item 31) a multipurpose communication network is created. This embodiment of the digital data network can also provide telehealth services, the use of electronic information and telecommunication technologies, to manage, monitor or support long-distant and remote clinical health care, health-related education, health administration and public health. Similarly, this wireless network can also provide social care services, communal education and internet access with other mission-critical services.

[0550] Multi-Core Communications:

[0551] Incoming wireless signa Is are identified by the Multi-Path Communications Control unit (MCC) (Fig. 6 item 26) and channeled into their origination identity path such as a Remote Wireless Network, a Local Wireless Network or an Independent Wireless Network. The functions within each identifier is that the Remote Processing Plant Communications (RPPC) (Fig. 6 item 142) selects from satellite (Fig. 6 item 147), wireless (Fig. 6 item 148), broadband (Fig. 6 item 149) or any other remote wireless network type. The Local Processing Plant Communications (LPPC) (Fig. 6 item 143) selects between Bluetooth (Fig. 6 item 151), WiFi (Fig. 6 item 152), ZigBee (Fig. 6 item 153) or any other local processing network type. The Independent Wireless Network Communications (IWNC) selects from services (Fig. 6 item 158) relatingto voice, video or data (Fig. 6 item 156) and CBR, CIR or BE (Fig. 6 item 157), scalable Quality of Service (Fig. 6 item 159), communications protocols (Fig. 6 item 160) and Priority User Channel Selector (PUS) (Fig. 6 item 161) such as first responders (Fig. 6 item 162) for fire (Fig. 6 item 168), for police (Fig. 6 item 169) for ambulance (Fig. 6 item 170), local government user (Fig. 6 item 163), private user (Fig. 6 item 164) or neural data user (Fig. 6 item 26C).

[0552] On receiving each user type, such as a Private User (Fig. 6 item 165), a local Government User (Fig. 6 item 166), a First Responder User (Fig. 6 item 167) ora Neural Data User (Fig. 6 item 26U) the Multiplexchannel Selector (MCS) (Fig. 6 item 172), within the Independent Wireless Network Node (IWNN) (Fig. 6 item 176), activates and configures the Wireless Communication Access Point or Concentrator (Fig. 6 item 176) to establish the UserType Channels (Fig. 6 items 173, 174, 175, 26T). This embodiment provides an ad hoc Multi-Core Communication Network (MCCN) by providingfoursimultaneous configurable and prioritised communication channels in one system. The Remote Processing Plant Communications (RPPC) (Fig. 6 item 142) can, in times of a local emergency, reconfigured the entire network from fourseparate channels to one single, high bandwidth, high availability wireless broadband network exclusively for emergency services use.

[0553] Multi-Path Communications:

[0554] Referring to Fig. 6, the Plant Communication and Control (PCC) 25 provides seamless data interchange, remote network connectivity 138, via satellite, wireless orfixed networks, to local networks 140, to additional Processing Reactor Plants (PRP) 136, for local operator access 139for security and authentication processing 23 and for Virtual Private Network (VPN) Gateway and IP Secure Tunnelling 141. The Security Authentication and Heartbeat module (SAH) (item 23) interfaces with the local operator 139 via face, iris, finger or other biometrics, smart cards or security tokens to facilitate functional access.

[0555] The Multi-Path Communications Control (MCC) 26 controls the Remote Processing Plant Communications (RPPC) 142 for remote networks 145, the Local Processing Plant Communications (LPPC) 143 for local networks 146 and the Independent Wireless Network Communications (IWNC) 144 for diversified and extended communication pathways. The MCC unit also establishes connectivity to remote networks 138 and other Processing Reactor Plants (PRP) 31. In this way an ad hoc wireless communication network is established (cf. Fig. 13) which includes connectivity to satellites (Fig. 13 item 313), satellite dishes (Fig. 13 item 315), wireless towers (Fig. 13 item 321), internet gateways (Fig. 13 item 319) and emergency services vehicles (Fig. 13 item 331, 332, 336). A remote monitoring functionality within the Multi-Path Comms Control unit (MCC) 26 enables remote monitoring and control capabilities with the Remote Monitoring and Control (RMCC) (Fig. 13 item 43) and off-site and remote based personnel 137.

[0556] Management of internal communications within the Processing Reactor Plant (PRP) 31 is controlled by the Master Multi-Path Matrix Manager (MMM) 24 and managed by the Plant Communication and Control unit (PCC) 25. The Multi-Path Comms Control unit (MCC) 26 controls all communication activities such as Local Processing Plant Comms (LPPC) 143 for local network selection 146, Remote Processing Plant Comms (RPPC) 142 for remote network selection 145 and Independent Wireless Network Comms (IWNC) 144 for various wireless services 155 or Priority User Channel Selector (PUS) 161 such as first responder users 162. The Remote Monitoring and Control Centre (RMCC) 43 via the Administration Management and Control unit (AMC) 32 incorporates the Comms Processing Engine (CPE) 177, the Comms Channel Allocation Manager (CAM) 178 and the Master Artificial Intelligence Engine (MAIE) 37 which interfaces with the Wireless Communication Access Point / Concentrator 176.

[0557] Ad hoc Neural Network:

[0558] Neurons, as the basic information process! ng structures in the human central nervous system, act as input recipients, information processors and output providers to other neurons. They are the processing channel for all information flow within the human body. While information transmission and processing is the basic consideration of the Neural Networks other work has focused on the makeup of information and knowledge as Artificial Intelligence.

[0559] Referring to Fig. 13, ad hoc Indirect Wireless Neural Communication Paths (IWNCP) 333, 334, 335, 337, 326,

[0560] 327. 328. 329. 322. 330. 338. 323. 324. 316. 314. 318. 320. 325. 326, 701, 702, 704 enhance the direct communication paths of the transportable Processing Reactor Plants (PRP) (cf. Fig. 6 item 31) for data integrity, disaster recovery and first responder conditions 331, 332336. The multipath communications (local short range wireless, long range wireless and satellite) ensures the built-in redundancy will deliver the data to its destination at all times.

[0561] This embodiment employs ad hoc Indirect Wireless Neural Communication Paths (IWNCP) 333, 334, 335,

[0562] 337. 326, 327, 328, 329, 322, 330, 338, 323, 324, 316, 314, 318, 320, 325, 326, 701, 702, 704, enabling communication between neural interfaces through a network infrastructure comprising of satellite links, wire less towers, and the internet, facilitated by an overlay network of wireless-ready waste processing plants. This innovation allows forthe exchange of neural signals and data between interconnected devices, facilitating advanced brain-machine interfaces and neurological research. As traditional communication methods for neural interfaces are limited, this invention explores a novel approach, establishing indirect wireless neural communication paths that enable real-time data exchange between neural devices, leveraging existing communication infrastructure, including satellite links, wireless towers, and the internet.

[0563] Indirect Wireless Neural Communication Paths (IWNCP) involve the transmission of neural signals between brain-machine interfaces or neural devices through intermediate nodes within an established communication network. These paths are dynamic, adapting based on network availability and device proximity, ensuring uninterrupted and low-latency neural signal transmission. Devices equipped with neural interfaces capture and process neuralsignals, transformingthem into digital data packets ready for transmission. Intermediate nodes within the network, such as satellite communication systems, wireless towers, and internet servers, act as relay points. They receive neural data packets, process them, and forward them towards the intended destination, establishing indirect neural communication paths. A Dynamic Path Selection Algorithm (DPSA) (cf. Fig. 6 item 26N), assesses local (Fig. 6 item 140), or remote (Fig. 6 item 138), network conditions, via the Multi-Path Communications Control (MCC) (Fig. 6 item 26), device proximity (Fig. 6 item 26D), and data prioritisation (Fig. 6 item 26C). Based on this analysis, it selects the most optimal path for neural signal transmission, ensuring real-time and efficient communication between neural devices 700, 703.

[0564] The satellite links 313 provide global coverage, ensuring neural interfaces in remote or isolated areas can connect to the network. The wireless towers 321 offer regional coverage, enhancing connectivity in densely populated or urban areas and the internet connectivity 319 enables data exchange, storage, and processing, allowing for advanced neural data analysis and research.

[0565] The advantages with this embodiment is that it provides real-time neural communication that facilitates instantaneous exchange of neural signals between devices 700, 703, enabling applications in neurology, prosthetics, and brain-computer interfaces. The global accessibility utilises satellite links 313 for connectivity in remote locations, ensuring access to advanced neurological care and research opportunities worldwide. The network resilience adapts to network disruptions by dynamically rerouting communication paths, ensuring continuous neural data exchange. The embodiment also promotes collaborative research by connecting neural devices globally, fostering scientific advancements and knowledge sharing.

[0566] The concept of ad hoc Indirect Wireless Neural Communication Paths (IWNCP) represents a significant leap forward in neural interface technology. By leveraging existing communication infrastructure, including satellite links 313, wireless towers 321, and the internet 319, this innovation enables real-time, global neural communications. This invention opens new avenues for neurological research, medical applications, and collaborative efforts, ultimately improving the quality of life for individuals with neurological conditions and advancing the field of neuroscience.

[0567] The ad hoc network 550 is self-organised rather than planned or pre-arranged in any configuration. The communication ad hoc network 550 is autonomously established with and between each of the Processing Reactor Plants (PRP) 31A, 31B, 31C, 31D and any existing wireless communication equipment such as satellites 313, satellite dishes 315, wireless towers 321 and internet gateways 319. Emergency vehicles 331, 332, 336 can connect to the ad hoc network 550, local devices 700, 703 and Digital Signage Systems (DSS) 317 can connect and receive digital content to display on their video panels. The bidirectional ad hoc network 550 can receive and transmit data, voice and video content (Fig. 6 item 155) from any connected device.

[0568] Digital Signage:

[0569] The Processing Reactor Plant (PRP) (Fig. 13 item 653), as detailed in the flow diagrams (Fig. 6 item 31), is capable of processing organic waste (Fig. 3 items 1, 2) and converting that waste (Fig. 3 item 3) into electricity through pyrolysis (Fig. 3 item 5), ensuring a continuous and sustainable power supply. The generated electricity, stored in high-capacity batteries (Fig. 3 item 104) to provide a stable power source even during periods of low organic waste availability, combines with the energy generated by local solar and wind sources (Fig. 13 item 31D) to further supplement any power source requirements. With abundant, localised available energy, digital signage panels (Fig. 13 item 317), as an example, can be installed on the sides of the Processing Reactor Plant (PRP) (Fig. 13 item 653) to display advertising content, road authority signage, location based warning signs, health or environmental images or message, any other forms of video or fixed media content and any information sharing content. All such externally created digital content (Fig. 13 item 705) is sent to the Processing Reactor Plant (PRP) (Fig. 13 item 653) to display the content, via the Remote Monitoring and Control Centre (RMCC) (Fig. 13 item 43). The Administration Management and Control unit (AMC) (Fig. 6 item 32) utilises the Communication Processing Engine (CPE) (Fig. 6 item 177) and the Communication Channel Allocation Manager (CAM) (Fig. 6 item 178) to establish user protocols and then the Master Artificial Intelligence Engine (MAIE) (Fig. 6 item 37) to provide Data Processing Algorithms (DPA) (Fig. 6 item 206) and the Rules and Facts Database (Fig. 6 item 41) in identifying and establishing the media type, information format, as examples. The Applications Manager (Fig. 6 item 35) with the Data Analysis module (Fig. 6 item 133A) review customer data, market data, trading data and any other economic specifics relating to the digital content and the financial transaction. Once all requirements are proven the Data Processing Engine (DPE) (Fig. 6 item 204) enables the flow of the digital content, to the appropriate Processing Reactor Plant (PRP) (Fig. 13 item 653), for display and monitors any event or duration triggers as detailed within the display agreements.

[0570] VSAT:

[0571] The embodiments provide communication and waste management systems designed to meet the demands of information data generation, high transmission rates, data accuracy and security, rapid connectivity response, and automatic location tracking. The system integrates a high-performance stabilized Very Small Aperture Terminal (VSAT) antenna (Fig. 12 item 290) mounted on top of a Processing Reactor Plant (PRP) (Fig. 12 item 31) with examples of internal sensors (Fig. 3 item 88S) and external instrumentation sensors (Fig. 12 items 295, 296, 297, 298, 299, 300, 301) all gathering data. The system enables efficient waste management, real-time data collection and wireless connectivity to a centralised Remote Monitoring and Control Centre (RMCC) (Fig. 13 item 43). Multiple such Processing Reactor Plants (PRP) (Fig. 13 items 650, 651,652, 653) are deployed with all broadcasting data and receiving com ma nds from the same centralised Remote Monitoring and Control Centre (RMCC) (Fig. 13 item 43) as an integrated solution for waste management and communication.

[0572] The VSAT antenna is designed with advanced stabilisation technology to ensure continuous and stable communication even in adverse environmental conditions. It automatically adjusts its position to maintain a strong connection with the satellite, meeting the demands of high transmission rates and data accuracy. The communications infrastructure provides a satellite communication's antenna, an Antenna Control Unit (ACU), Tracking Device Unit (TDU) and Inertial Measurement Unit (IMU). The high- performance stabilized VSAT antenna system establishes a high-speed satellite connection, enabling seamless communication to the centralised Remote Monitoring and Control Centre (RMCC) (Fig. 13 item 43) of real-time collected data from loT sensors. This data includes, but not limited to, waste conversion metrics, environmental conditions, and system performance information. Integrated loT sensors (Fig. 3 item 88S) are deployed within the Processing Reactor Plant (PRP) (Fig. 13 item 31) to monitor various parameters such as temperature, humidity, waste levels, and converter efficiency. These sensors collect real-time data, providing valuable insights into the waste conversion process and PRP conditions and providing real-time data for informed decision-making. All deployed Processing Reactor Plants (PRP) (Fig. 7 item 31) are connected to the centralised Remote Monitoring and Control Centre (RMCC) (Fig. 7 item 43). Here, advanced software processes the incoming data (Fig. 7 item 32), allowing operators to monitor the status of each Processing Reactor Plants (PRP) (Fig. 13 items 650, 651,652, 653), track waste conversion efficiency (Fig. 7 item 200), and ensure optimal performance (Fig. 7 item 133A). The centre also facilitates remote configuration and troubleshooting (Fig. 6 item 137), maximising system uptime (Fig. 7 item 115).

[0573] The system incorporates a C-band, 4-axes marine stabilised VSAT antenna compatible with C-band satellites and a 2.4 metres reflector RF aperture. High elevation problems are experienced with traditional 3-axes systems and occur in a wide belt around the equator and the satellite is close to zenith. This problem creates a loss of signal, restarts and downtime. This embodiment is designed with a 4-axes solution allowing the RF equipment to move freely and to maintain an optimal position towards the satellite without big and sudden movements even during harsh conditions. The 4-axes system does not experience dead angles and therefore does not experience any high-elevation problems. This high reliability makes the VSAT antennas suitable for use as the main connection point as the system can carry large volumes of information without interruption and the tracking system can identify and locate any selected satellite within 6-8 seconds, startingfrom its parked position. The system achieves high-tracking accuracy (a tracking loss of +-0.1 dB), which is comparable to a fixed antenna. The tracking accuracy value is used to measure performance instead of pointing accuracy, as it includes both losses due to pointing and RF beam misalignments. The high tracking accuracy is a result of unique tracking method, a predetermination tracking parameter and the 4-axes design, where the fourth axis refers to the linear polarisation-axle. With constant information flow between the Processing Reactor Plants (PRP) (Fig. 7 item 31) and the Remote Monitoring and Control Centre (RMCC) (Fig. 7 item 43), it is imperative that satellite connectivity is both continuous and enables high level of data transmission in extreme weather conditions. The system provides high precision tracking and pointing accuracy, while being lightweight and robust. By addressing the challenges of high transmission rates, data accuracy, rapid response, and efficient waste disposal, this system contributes to a cleaner environment and enhanced communication capabilities for various applications.

[0574] Edge Computing:

[0575] The embodiment uses an edge based computing process that brings computational and data storage closer to the data sources and to the edge of the network to improve response times and data bandwidth.

[0576] The embodiment uses a localised edge based configuration A.I. Edge Manager (AIEM) (Fig. 7 item 111E), within the Unit Cell Data (UCD (Fig. 7 item 114) and managed by the Master Multi-Path Matrix Manager (MMM) (Fig. 7 item 24) and (Fig. 4 item 24) to interface (Fig. 4 item 109), process data (Fig. 4 item 115), and localised routine system tasks (Fig. 4 item 111), rather than a centralised remote cloud-based system. This configuration structure can rapidly determine and resolve localised issues and to automatically synchronise (Fig. 4 item 108) or scale up ordown based on demand. This solution can dynamically allocate localised resources as needed. The embodiment focuses on tasks (Fig. 4 item 111) such as data pre-processing (Fig. 4 item 115), model optimisation (Fig. 4 item 112), and real-time inference (Fig. 4 item 109). The system optimises these processes for edge devices (Fig. 7 item 1HE), ensuring minimal latency and resource consumption.

[0577] Integrated loT sensors (Fig. 3 item 88S) and (Fig. 7 item 179) within the Processing Reactor Plants (PRP) (Fig. 7 item 31) collect real-time data, as an example, on waste levels, temperature, humidity, and system performance. This data is processed locally by the A.I. Edge Manager (AIEM) (Fig. 7 item 111 E), providing valuable insights for waste management decisions and optimising the pyrolytic waste conversion process (Fig. 3 item 2).

[0578] Referringto Fig. 7, the embodiment allows users to specify distributed machine learning models HIM tailored for specific edge resources 192 within a complex and dynamic environment. Integrated with loT sensors, the system optimises waste processing efficiency and real-time environmental monitoring. Additionally, the invention explores fundamental theories and algorithms of edge computing 206 and presents APIs and applications 35, including Intelligent Blockchain 69 and Edge A.1. 133A, to enhance waste management processes. The A.I. Edge system is designed to operate in the challenging environment of Processing Reactor Plants (PRP) 31, housed within transportable containers (see Fig. 12 item 31). By enabling distributed machine learning at the edge 111M, this system optimises resource utilisation, waste processing efficiency and enhances processing without overburdening the limited computational resources at the edge.

[0579] The system includes Edge A.I. applications 35 that enable real-time image recognition for identifying different types of waste (see Fig. 3 item 1) and arranging the input waste feedstock (Fig. 3 item 2). By deploying machine learning models 111M directly on edge devices 179, the system identifies organic waste types and effectively configures the Conversion Reactors (Fig. 3 item 3), facilitating efficient waste processing, effective resource recovery and factual traceability of waste management operations.

[0580] The A.I. Edge system HIE incorporates a localised Intelligent Blockchain technology 111B to create an immutable and transparent ledger for waste management transactions. Each stage of waste input (Fig. 3 item 1), arranging (Fig. 3 item 2), converting (Fig. 3 item 3), output (Fig. 3 item 4) to energy conversion (Fig. 3 item 5), is recorded on the localised Intelligent Blockchain 111B, ensuring traceability and accountability. The centralised Blockchain and Smart Contracts 69, within the Remote Monitoring and Control Centre (RMCC) 43 enable automated and secure transactions, enhancing the overall efficiency and reliability of waste management operations and connect to the Carbon Trading Exchange (CTE) (see Fig. 10 item 45).

[0581] Infographics: All deployed Processing Reactor Plants (PRP) (Fig. 7 item 31) are connected to the centralised Remote Monitoring and Control Centre (RMCC) (Fig. 7 item 43). Advanced software processes the incoming data via the Administration Management and Control (AMC) (Fig. 7 item 32), allowing operators to monitor the status of each Processing Reactor Plants (PRP) (Fig. 13 items 650, 651,652, 653), track waste conversion efficiency (Fig. 7 item 200), and ensure optimal performance (Fig. 7 item 133A). The centre also facilitates remote configuration and troubleshooting (Fig. 6 item 137), maximising system uptime (Fig. 7 item 115).

[0582] Again referringto Fig. 7, the Infographics provide a graphical representation of the huge amount of data that is collected, every 30 minutes (or more frequent as required), from every global Processing Reactor Plant (PRP) 31. The data is collated 193, processed 115, analysed 200, interpreted 206 and, if desired, presented in real-time forviewing 204.

[0583] As each monitored Processing Reactor Plant (PRP) 31 is installed an interactive map is activated. Realtime infographics are inbuilt into the system to extract valuable resource data from every applicable sensor 179. The interactive dashboards display infographics that include information from each PRP such as, but not limited to:

[0584] 'transportables' data represents the number of Processing Reactor Plant (PRP) (Fig. 3 item 31) that are currently in service around the planet, working (24 hours per day) to process input waste feedstock (Fig. 3 item 1).

[0585] 'bio-char' data represents the Tonnes Per Day (TPD) of Bio-Char (Fig. 3 item 101) that is produced from each Processing Reactor Plant (PRP) (Fig. 3 item 31). This commodity is sold at market rates via Commodity Trade Exchanges (Fig. 10 item 44) to global buyers. The Bio-Char is then on-sold as retail packaged products or as wholesale quantities to farmers, fertiliser manufactures, building material companies or industry. Bio-Char is considered a ‘Green’ alternative to coal for firing boilers, kilns, incinerators and other direct combustion devices.

[0586] 'emissions' data represents the sequestered Tonnes of CO2Per Day (TPD) that is captured and stored in the soil rather than released into the atmosphere. Carbon Trading Exchanges (CTE) (Fig. 10 item 45) process Spot Certified Emission Reduction Credit transactions (Fig. 10 item 271).

[0587] 'biodiversity' data represents the % of local bio diversification, in proximity to the Processing Reactor Plant (PRP) (Fig. 3 item 31), that has beneficial results.

[0588] 'energy' data represents the kilowatt Hour (kWh) of daily electricity generated (Fig. 3 item 105) from each Processing Reactor Plant (PRP) (Fig. 3 item 31).

[0589] 'crops' data represents the Tonne Per Day (TPD) of crop output using Bio-Char (Fig. 3 item 101) based sequestration and Infused Bio-Char (Fig. 3 item 99A) as organic fertiliser.

[0590] 'water' data represents the Litres Per Day of water (Fig. 3 item 103) that is recovered from each Processing Reactor Plant (PRP) (Fig. 3 item 31).

[0591] 'people' data represents the number of people employed to operate each Processing Reactor Plant (PRP) (Fig. 3 item 31) around the planet.

[0592] Each interactive dashboard for the Processing Reactor Plant (PRP) (Fig. 7 item 31, is identified by geographical locations, such as zone, country, state and city, by GPS locations, VSAT locations, IP addresses and contain information such as, but not limited to:

[0593] On-site resources

[0594] Project progress

[0595] Waste input

[0596] Process data

[0597] Counters

[0598] Commodity output Energy generation Financials (revenues / expenses)

[0599] KEY ITEM 7.

[0600] Background:

[0601] The Carbon Trading Program came into force on 16 February 2005 as part of the Kyoto Protocol with global coverage of over 160 countries, excluding the United States and Australia. The program has focus on the reductions of Global Greenhouse Gas (GHG) emissions, is underwritten by the signatory governments and is governed by global legislation enacted under the auspices of the United Nations. The signatory Governments are separated into developed countries, who have accepted GHG emission reduction obligations and developing countries, categorised as those who have no GHG emission reduction obligations.

[0602] If a developed country fails to meet its Kyoto Protocol target it is penalised by having its reduction targets decreased by 30% in the next period. The established targets are that between 2008 and 2012 developed countries have to reduce their GHG emissions by around 5% below their 1990 levels.

[0603] Within the Kyoto Protocol there are ‘flexible mechanisms’ which allow developed countries to meet their GHG targets by taking reduction measures domestically or by purchasing GHG emission reductions from elsewhere. These emission reductions can be bought either from financial exchanges, such as the EU Emissions Trading Scheme, or from projects that reduce emissions under the Clean Development Mechanism (CDM), under the Joint Implementation (JI). So when a GHG emission reduction project is implemented it will receive Carbon Credits, which can then be sold to buyers. Carbon credits produced using CDM is called Certified Emission Reductions (CERs). The rules governing the CDM state that projects must meet certain requirements in orderto qualify as CDM status. These requirements include:

[0604] - Compliance with the project approval process and sustainability development criteria.

[0605] - Compliance with the project validation and registration process.

[0606] - Compliance with the monitoring requirements.

[0607] - Compliance with the verification and certification requirements.

[0608] - Compliance with the rules governing the issuance of CERs.

[0609] The ‘Buying’ and ‘Selling’ of carbon credits has created the fundamentals of the carbon market. Companies now have a more cost effective option to offset their carbon emission by buying, through the carbon trading exchange, rather than developing and investing in clean projects that generate CERs for emissions compliance.

[0610] Process Flow Control (PFC) is a normal system function in any processing plant. In this invention the PFC is derived and then directed into a ‘Business Process Control’ (BPC) system (market factors, price structures, demand requirements etc) with an ‘Artificial Intelligence Control’ (AIC) function (rational, assumptions, justifications, expected outcomes etc). This new method calculates the amount of carbon that is produced in real time for carbon sequestration calculations. This methodology is authenticated and approved by independent auditing and certification authorities that certify the input, processing plant and output computation results. This creates the baseline methodology with enhancements for applicability, relevance and specific outcomes.

[0611] This audited computational result is then registered and displayed, in real time, in a remote Carbon T rading Exchange (CTE) as a sellable item. From this displayed sellable carbon sequestrated item a Carbon Trading buyer can purchase via the CTE. This transaction happens in real time as the remote plant is producin the carbon sequestration values. This carbon sequestration methodology is pre-certified as a ‘known’ input undergoes a transformation through a ‘know’ process and delivers a ‘known’ output from the processing plant.

[0612] Underthe United Nations Framework Convention On Climate Change (UNFCCC) and the Kyoto Protocol’s Clean Development Mechanism (CDM) methodology process allows a project sponsor to submit, through a Designated Operational Entity (DOE) the concepts for emission reduction creation. The CDM Methodology Panel and their expert advisors with the CDM Executive Board review the project for legitimacy as a carbon project.

[0613] The invention has internal flow methodologies that follow the guidelines for carbon sequestration as solid waste disposal on land, agriculture manure management and the control of greenhouse gases such as, but not limited to:

[0614] - Carbon dioxide (CO2)

[0615] - Methane (CH4)

[0616] - Nitrous Oxide (N20)

[0617] - Hydroflurocarbons (HFCs)

[0618] - Perflurocarbons (PFCs)

[0619] - Sulphur Hexafluoride (SFe)

[0620] The displayed sellable carbon sequestrated item on the Carbon Trading Exchange is a Certified Emission Reductions (CERs). The use of the Bio-Gas and Bio-Char can result in avoidance of climate change levies and can also generate Certified Emission Reduction Credits or equivalent.

[0621] Embodiment:

[0622] With the continuous generation of real-time data within a pre-audited and certified embodiment of the invention, as part of a waste processing plant, allows for a unique system for ‘spot trading of Carbon Emissions Credits’ which addresses the shortcomings of current and existing waste processing plants.

[0623] Bio-Char (Fig. 3 item 101), as produced from the Conversion Reactors (Fig. 3 item 88), generally is accepted as a potential tool for carbon sequestration and climate change mitigation. Bio-Char can attract Certified Emission Reduction (CER) credits (Fig. 10 item 271) under the Clean Development Mechanism (CDM) of the Kyoto Protocol. This embodiment integrates the processing of waste input feedstock (Fig. 3 item 1), the production of Bio-Char (Fig. 3 item 101), the creation of a Blockchain based digital ledger (Fig. 10 item 69), the creation of Smart Contracts (Fig. 10 item 69), the certifying of CER credits (Fig. 10 item 257) and facilitating the buy / sell transaction of the credits by registering those credits on a Carbon Trading Exchange (CTE) (Fig. 10 item 45).

[0624] Referring to Fig. 7, one of the Output Commodity Products 70 for the buyer marketplace 44 is Bio-Char 101.

[0625] Referringto Fig. 10, when the Bio-Char is produced from the Processing Reactor Plant (PRP) 31, the Master Multi-Path Matrix Manager (MMM) 24 informs the Administration Management and Control (AMC) 32, within the Remote Monitoring and Control Centre (RMCC) 43 that carbon certificates are required 257, Blockchain and Smart Contracts need to be generated 69 and the Carbon Trading Exchange 45 interfacing is to be established. This unique automated process provides the real-time ‘spot’ Carbon Trading functionality and incorporates internal tasks forthe Compliance and Certification Authority (CCA) 257.

[0626] The Administration Management and Control (AMC) 32 manages the Carbon T rading Algorithms (CTA) 256 and via the Applications Management 35 controls the Carbon Audit and Trading Engine (CATE) 255. The Master Artificial Intelligence Engine (MAIE) 37 manages the actions, activities and undertakings for the Blockchain and Smart Contracts 69. To qualify for CER credits, Bio-Char projects need to follow rigorous verification and monitoring processes to demonstrate the actual emissions reduced or sequestered. These processes ensure transparency and accountability, making Bio-Char projects eligible for CER credits. The embodiment provides all the necessary data integrity to verify and comply with all requirements and guidelines via the Compliance and Certification Authority (CCA) 257.

[0627] The Carbon Trading Algorithm 256 administers business rules and artificial intelligence agents in the control of carbon trading transactions. It is also equipped with Smart Contracts within Blockchain digital ledgers 69 that are directly connected to Carbon Trading Exchanges (CCTE) 268. The Real Time Spot Carbon Trading Exchange (CTE) 45, the Remote Monitoring and Control Centre (RMCC) 43, the Compliance and Certification Authority (CCA) 257 and the relationship with the Processing Reactor Plant (PRP) 31 as it relates to the Output Commodity Product 70 for the buyer marketplace 44. The Administration Management and Control (AMC) 32 manages the Carbon Trading Algorithms (CTA) 256 and via the Applications Management 35 controls the Carbon Audit and Trading Engine (CATE) 255. The Master Artificial Intelligence Engine (MAIE) 37 manages the actions, activities and undertakings for the Blockchain and Smart Contracts 69.

[0628] The invention’s distributed recovery process also saves truck or tanker transport costs required for the delivery of the waste to central waste incinerators or landfill sites thus saving addition thousands of tonnes of CO2emissions by replacing fossil fuels.

[0629] KEY ITEM 8.

[0630] The physical reactor based configuration of the Processing Reactor Plant (PRP) (Figs. 2A& 2B item 31) is standardised for global installations whereas the local input feedstock variances are processed within the Input Matrix Control unit (IMC) (Figs. 2A& 2B item7), actioned in the Arrange Matrix Control unit ...

Claims

Claims:

1. A system for processingof organic waste, the system comprising: a converter reactor; an input waste feed material identification subsystem; one or more energy generators; one or more sensors for acquiring at least processing data; a communication system for remote monitoring and control of the system; and artificial intelligence agents for making localised decisions; wherein the system is configured to autonomously select or control the processing according to at least the identity of the waste feed material, and the system is configured to be controlled according to at least the processing data.

2. A system as claimed in claim 1, comprising an interface for receiving commodity requirements and / or market demands, wherein the system is configured to autonomously select or control the processing according to commodity requirements and / or market demands.

3. A system as claimed in claim 2, comprising or configured to be controlled according to smart contracts within blockchain digital ledgers.

4. Asystem as claimed in claim 3, wherein the smart contracts are connected to or valid with a carbon trading exchange.

5. Asystem as claimed in any one of the preceding claims, wherein the converter reactor is a pyrolytic reactor.

6. Asystem as claimed in any one of the preceding claims, wherein the system: is modular and / or portable; and / or is housed or housable in one or more standard shipping containers.

7. Asystem as claimed in any one of the preceding claims, comprising one or more bio-gas driven turbines, wherein the system is at least partially self-powered thereby.

8. Asystem as claimed in any one of the preceding claims, comprising a master management and control module for managing and controllingthe system.

9. Asystem as claimed in any one of the preceding claims, comprising an input waste feed material recognition module.

10. A system as claimed in anyone of the preceding claims, comprising: process matrix control and configuration management and setup; and / or global remote monitoring and control functionality.

11. A system as claimed in anyone of the preceding claims, wherein the system is configured to be connected to the Internet of Things (IOT), such that one or more processes implemented by the system are controllable and / or monitorable via the internet.

12. A system as claimed in anyone of the preceding claims, comprising any one or more of a Localised Artificial Intelligence, a Decentralized Artificial Intelligence, and process-based Artificial Intelligence.

13. A system as claimed in anyone of the preceding claims, configured: to generate electricity from one or more outputs of the processing; and / or. to produce hydrogen with an external electrolyser and / or graphite from Bio-Char output and / or graphene from graphite output and / or Biodiesel or Green Diesel from Bio-Oil output.

14. A system as claimed in anyone of the preceding claims, comprising a High Speed Quantum Nodes based network and / or a random or pseudo-random number generator.

15. A system as claimed in anyone of claims 1 to 13, comprising a pseudo-random number generator and configured to create and employ highly secure Digital Cryptographic Keys using random or pseudorandom numbers generated by the random or pseudo-random number generator.

16. A system as claimed in anyone of the preceding claims, configured: to generate carbon credits pursuantto sequestration of carbon products outputted by the system; and / or for integration into a real time spot or other carbon credits trading exchange.

17. A system as claimed in anyone of the preceding claims, configured to support a Carbon Credits Redemption Loyalty Program.

18. A system as claimed in any one of the preceding claims, comprising a plurality of converter reactors couples in series or in parallel.

19. A system for pyrolytic processingof organic waste feedstock, comprising the system of any one of claims 1 to 18.

20. A method for pyrolytically processing organic waste feedstock, comprising: providing organic waste feedstock to the system of any one of claims 1 to 18; and pyrolytically processing the organic waste feedstock with the system of any one of claims 1 to 18.

Images