Methods and systems for processing waste material

Abstract

A waste processing system 100 comprises a control unit 110 which assesses characteristics of a waste material (S610, Fig. 6) and obtains parameters of the waste processing system (S620, Fig. 6). Operation parameters are determined based on the waste material characteristics and the system parameters (S630, Fig. 6) and the system operated to process the waste material (S640, Fig. 6). The material characteristics may be type of waste, amount, quality, or water content. The waste material may comprise first and second waste materials with the material characteristics being the amounts of the first and second materials. The operation parameters may comprise a rate of release of the waste materials from storage 120, 130, 140, a number of reactor stages 170, 180, 190, a number of conveyor systems 160, heating methods, or power consumption. A system parameter may be an ambient temperature around a thermal reactor. Waste heat may be used to pre-heat the thermal reactor or the waste material. The waste material is typically tyres, plastics, or biomass, which may be processed by torrefaction, pyrolysis, or hydrolysis. A control system and software code executing machine learning algorithms to operate the waste processing system are also claimed.

Description

FIELD The present technology relates generally to waste material processing and in particular relates to methods and systems for processing waste material under improved conditions. BACKGROUND The widespread production and use of materials and products that cannot be easily recycled has become a serious global issue, contaminating the natural environment and endangering wildlife exposed to the waste. This is evident from the amount of plastic materials that can be observed in waterways and the sea. Moreover, the production process of such materials and their products are extremely polluting and damaging to the environment.88 The growing rate of consumption of these materials and products worldwide leads to a growing amount of waste that is not easily recyclable and therefore often discarded without first being processed to reclaim usable material or to render the waste harm free. At present, much of the non-recyclable and not easily recyclable waste either ends up in landfills or is exported to countries where waste management laws are less strict; either way, the waste ultimately ends up in the environment. Techniques currently exist for processing waste material by breaking down the waste material through e.g. torrefaction-based process, including (but not limited to) pyrolysis and hydrolysis. However, pyrolysis, hydrolysis and other thermal treatment methods have high heat demands and are therefore energy intensive. Moreover, these processes frequently result in low quality products being recovered and at low yields. Further, it can be challenging to separate high purity output products and gases from mixed output streams, such as hydrogen from a mixed syngas output. Compounding the issue of high production cost and low profit margins with the relatively low costs associated with the manufacturing of new materials and products, material recovery from waste becomes a less viable option. The core shortcomings are generally not inherently in the reactor units used in waste processing but the way in which they are operated. The reactors are often not optimised for different input materials and their operation typically does not take into account the environment in which they operate; instead, once set up, the reactors are generally operated as standalone units in a fixed operation mode. It is therefore desirable to provide improved waste processing systems and method. SUMMARY In view of the foregoing, an aspect of the present technology provides a computer-implemented method of controlling waste processing in a waste processing system, comprising: assessing source waste material for one or more source waste material characteristics; obtaining one or more system parameters of the waste processing system; determining one or more operation parameters for the waste processing system based on the one or more source waste material characteristics and the one or more system parameters; and operating the waste processing system to process the source waste material based on the determined one or more operation parameters. According to embodiments of the present technology, the source waste material to be processed is first assessed, and operation parameters on which operation of the waste processing system is based are determined according to the assessment of the source waste material as well as system parameters of the waste processing system. In doing so, the operation parameters of the waste processing system may be changed or adjusted dynamically depending on the source waste material and the current status of the system parameters of the waste processing system. Embodiments of the present technology thus enable the waste processing system to be operated more efficiently without implementing any equipment changes or upgrades. In doing so, it is possible to reduce the cost of processing waste materials, in particular harmful and / or non-recyclable or not easily recyclable waste materials, and improving the quantity and / or quality of the output product yields that can be reused or resold. There may be a wide range of various characteristics of the source waste material that can influence how it should be processed. In some embodiments, the one or more source waste material characteristics may comprise a type of source waste material, a composition of the source waste material, quality of the source waste material, a water content of the source waste material, a total amount of the source waste material, or any combination thereof. The various characteristics of the source waste material may determine the temperature at which it should be processed or a heating method used to obtain specific quality or quantity of product yield, or the rate at which the waste material can be processed, etc. The rate at which the source waste material can be optimally processed may depend on various factors such as the type of material to be processed, the quantity and quality of the material, the composition of the material, etc. Thus, in some embodiments, the waste processing system may comprise a source material storage for storing the source waste material, and determining one or more operation parameters may comprise determining a rate of releasing the source waste material from the source material storage based on the one or more source waste material characteristics and the one or more system parameters. There may be instances when the waste processing system processes more than one type of waste material at the same time, e.g. tyre rubber and biomass. Thus, in some embodiments, the source waste material may comprise a first waste material and a second waste material and the waste processing system may comprise a first source material storage for storing the first waste material and a second source material storage for storing the second waste material, and the one or more source waste material characteristics may comprise an amount of the first waste material and an amount of the second waste material. In cases where more than one waste materials are being processed, it may be more optimal to process the more than one waste materials in different ratios which may result in higher processing efficiency, different types of product being produced, different quality of products being produced, etc. Moreover, in some cases, there may be an unequal amount of different source waste materials and it may be desirable to proportion the various source waste materials such that all source waste materials are processed together. Thus, in some embodiments, determining one or more operation parameters may comprise determining a ratio between a rate of releasing the first waste material from the first source material storage and a rate of releasing the second waste material from the second source material storage based on the amount of the first waste material and the amount of second waste material and the one or more system parameters. There may be certain combinations of source waste materials which, when processed together, make the process more efficient. For example, processing tyre rubber together with biomass may reduce energy demands and / or increase yield quality. Varying the proportion of the source waste materials may have varying outcome. Thus, in some embodiments, assessing source waste material may further comprise determining a dependency between an amount of the second waste material and an efficiency of processing the first waste material, and determining a ratio between a rate of releasing the first waste material from the first source material storage and a rate of releasing the second waste material from the second source material storage is performed further based on the dependency. In some embodiments, the waste processing system may comprise a thermal reactor, and obtaining one or more system parameters may comprise obtaining an ambient temperature around the thermal reactor. Instead of always operating the thermal reactor with the same energy input, depending on the ambient temperature of the thermal reactor surroundings, the thermal reactor may require higher energy input e.g. when the ambient temperature is low to maintain a desired operation level, or it may require lower energy input e.g. when the ambient temperature is high for the thermal reactor to reach a desired operation temperature. In some embodiments, determining one or more operation parameters may comprise determining an operation temperature for the thermal reactor based on the one or more source waste material characteristics and the ambient temperature around the thermal reactor. In doing so, the operation temperature and / or energy / power input of the thermal reactor may be dynamically adjusted depending on the current status of the operation environment to maintain a desired level of operation while reducing unnecessary energy expenditure. Within the waste processing facility, there may be other industrial activities that generate heat as a waste product, for example from an electrolyser or a metal hydride reactor, or any other processes or activities that generate waste heat. In some embodiments, obtaining one or more system parameters may comprise determining an availability of waste heat for heat recovery. The amount of waste heat produced from nearby industrial activities or processes may vary depending on many factors, such as (but not limited to) demands, energy and resources availability, etc. As such, the determination of the availability of waste heat enables the waste heat from these industrial activities / processes to be recovered for use by the waste processing system, and further enables the operation parameters, such as operation temperature and / or energy input, of the waste processing system to be dynamically adjusted based on the available waste heat that can be recovered. In some embodiments, the method may further comprise, upon determining that waste heat is available, directing the waste heat into the waste processing system. The available waste heat from nearby industrial activities / processes may be used in various ways within the waste processing system. In some embodiments, the waste processing system may comprise a thermal reactor, and directing the waste heat into the waste processing system may comprise directing the waste heat to the thermal reactor to pre-heat the thermal reactor. By pre-heating the thermal reactor, the amount of energy required by the thermal reactor to reach operation temperature may be reduced, and thus overall energy consumption by the waste processing system is reduced. In some embodiments, the waste processing system may comprise a source material storage for storing the source waste material, and directing the waste heat into the waste processing system may comprise directing the waste heat to the source material storage to pre-heat the source waste material. By pre-heating the source waste material, the amount of energy required, and therefore the time it takes, for the source waste material to reach the required temperature for processing (e.g. thermal breakdown) is reduced, leading to improved efficiency of the process. In some embodiments, the one or more system parameters may further comprise a number of source material storage containing source waste material, a number of reactor stages, a number of conveyor systems for conveying the source waste material, one or more heating methods available to the thermal reactor, one or more power sources, power consumption rate, waste processing system capacity, or a combination thereof. Similar to the idea of recovering waste heat from nearby industrial activities / processes for use in the waste processing system, the waste processing system (e.g. the thermal reactor) generates waste heat that would typically simply dissipate to the surroundings. In some embodiments, the method may further comprise recovering heat produced by the waste processing system by storing the heat in a heat repository and / or by directing the heat to an external process. By storing, re-directing, or otherwise recovering the waste heat produced by the heat processing system, the overall energy efficiency of the waste processing system may be improved. The stored, re-directed or otherwise recovered waste heat may either be used in other industrial activities or processes, or it may be recycled back to the waste processing system. There may be instances when different objectives are desired for the waste processing. For example, there may be times when higher yield, higher quality output product, faster processing, etc. is desired, or there may be cases when the waste processing produces more than one output products of varying quality, quantity and value and there may be different desirable objectives to balance the quality and / or quantity of the different output products, and / or to maximise the total value of the output products, etc. Thus, in some embodiments, the method may further comprise setting a processing objective and determining the one or more operation parameters for the waste processing system further based on the processing objective. In some embodiments, the processing objective may comprise optimising a yield of a predetermined output product, optimising a quality of a predetermined output product, optimising an operation cost, and / or optimising a profit to be obtained amongst a plurality of output products. In some embodiments, the source waste material may comprise tyres, plastic, solid waste from water treatment plants, and / or biomass. In some embodiments, the waste material processing may comprise a torrefaction process, a pyrolysis process, and / or comprises a hydrolysis process. Another aspect of the present technology provides a non-transitory computer readable storage medium storing software code which, when executed on one or more data processors, executes one or more machine learning algorithms previously trained to perform the method as described above. A further aspect of the present technology provides a control system for controlling waste material processing in a waste processing system, the control system comprising: one or more first sensors configured to detect source waste material to generate first sensor data; one or more second sensors configured to detect the waste processing system to generate second sensor data; and a control unit configured to: assess the source waste material for one or more source waste material characteristics based on the first sensor data; determine one or more system parameters of the waste processing system based on the second sensor data; determining one or more operation parameters for the waste processing system based on the one or more source waste material characteristics and the one or more system parameters; and operating the waste processing system to process the source waste material based on the determined one or more operation parameters. In some embodiments, the waste processing system may comprise at least one source material storage configured to store source waste material, and the one or more first sensors may be disposed within, on or around the at least one source material storage. In some embodiments, the one or more first sensors may comprise at least one weight sensor configured to detect a weight of the waste material, at least one material sensor configured to detect a material type of the source waste material, at least one voltage and / or current sensor configured to detect a conductance and / or resistance of the source waste material, or a combination thereof. In some embodiments, the waste processing system may comprise a thermal reactor configured to process the source waste material, and the one or more second sensors may be disposed within, on or around the thermal reactor. In some embodiments, the one or more second sensors may comprise at least one temperature sensor configured to detect a temperature of the thermal reactor and / or at least one ambient temperature sensor configured to detect an ambient temperature around the thermal reactor. A yet further aspect of the present technology provides a waste processing system comprising: at least one source material storage configured to store source waste material; a thermal reactor configured to process the source waste material; a conveyor system configured to convey the source waste material from the at least one source material storage to the thermal reactor; and a control system as described above. Implementations of the present technology each have at least one of the above-mentioned objects and / or aspects, but do not necessarily have all of them. It should be understood that some aspects of the present technology that have resulted from attempting to attain the above-mentioned object may not satisfy this object and / or may satisfy other objects not specifically recited herein. Additional and / or alternative features, aspects and advantages of implementations of the present technology will become apparent from the following description, the accompanying drawings and the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS Embodiments will now be described, with reference to the accompanying drawings, in which: FIG. 1 shows an exemplary waste processing system according to a first embodiment; FIG. 2 shows an exemplary waste processing system according to a second embodiment; FIG. 3 shows an exemplary waste processing system according to a third embodiment; FIG. 4 shows an exemplary waste processing system according to a fourth embodiment; FIG. 5 shows schematically a system overview of an embodiment of a waste processing control system; and FIG. 6 shows a flow diagram of an exemplary method of controlling a waste processing system according to an embodiment. DETAILED DESCRIPTION The present technology relates to a control system for controlling the operation of a waste processing system, for example (but not limited to) a waste processing system that processes waste through thermal processes such as torrefaction, pyrolysis and / or hydrolysis, to improve or optimise the operational efficiency of the waste processing system and / or the yield and / or quality of the output products from the process. According to embodiments of the present technology, the source waste material to be processed is first assessed, and operation parameters on which operation of the waste processing system is based are determined according to the waste material characteristics as well as system parameters of the waste processing system that reflect its current status. In doing so, the operation parameters of the waste processing system may be changed or adjusted dynamically depending on the waste material used and the current status of the waste processing system. Embodiments of the present technology thus enable the waste processing system to be operated more efficiently without implementing any equipment changes or upgrades. In doing so, it is possible to reduce the cost of processing waste materials, in particular harmful and / or non-recyclable or not easily recyclable waste materials, and improving the quantity and / or quality of the output product yields that can be reused or resold, making the processing of such waste material a viable industrial process. In an illustrative example, for a waste processing system that processes waste material in a reactor unit through a thermal process, a control system according to the embodiments may control the operation of the waste processing system as such: 1. The control system first assesses the input waste stream which is to be processed. This may include measuring the total weight of the waste material, its composition and water content, etc. The external environmental conditions may also be assessed, for example if the ambient temperature is known to fluctuate. The assessments provide the control system (e.g. executing a machine learning algorithm, MLA) with fixed parameters that influence the operation of the waste processing system. 2. The system parameters of the waste processing system are then assessed for the specific elements that are present. For example, the available heat injection methods and / or the number of reaction zones for the specific reactor unit to be used, the length of the conveyor system (e.g. an augur screw system) that conveys the waste material to the reactor unit, etc. 3. Optionally, an objective for the processing (a desired result) may be set, such that (the MLA executing on) the control system can take it into account when determining suitable operation parameters for the waste processing system. For example, an objective may be set to achieve one or more specific goals or results, such as to optimise the process for economics (lowest process cost and / or highest profit, or a balanced approach), for quality of specific material products, or for highest rate of throughput, etc. 4. On the basis of the assessment of the input waste, the assessment of the environmental conditions, the assessment of the system parameters, and optionally, the set objective, (the MLA executing on) the control system can determine a set of optimal operational parameters / conditions to be used, such as the specific operation temperatures for the reactor unit, the heat method(s) used, the timescale, etc., to optimally operate the waste processing system and / or to achieve the set objective. 5. In some cases, after processing the waste material in a thermal reactor unit, a further process may be required to separate the desirable output product from other outputs. For example, metal hydrides may be used to remove hydrogen from a mixed gas stream output by the reactor unit. The control system may be configured to assess the operation conditions e.g. of the reactor unit and the further process, so as to optimise the operation of the further process. For example, the control system may detect, or determine the availability of, waste heat from the reactor unit that can be redirected to the further process and / or stored for later use. A control system according to embodiments of the present technology is able to adjust and readjust various internal control parameters of the waste processing system autonomously based on the blend of the source waste materials to be processed in the reactor unit, taking into account the current status of the waste processing system parameters. The present technology enables optimisation of the operational conditions of the waste processing system and / or the output products produced by the waste processing system in terms of yield and / or quality. The present technology thus enables a waste processing system to operate in a dynamic, rather than a static or fixed, manner for optimal performance. As an illustrative example, a tyre processing plant is described. Composition of vehicle tyres are typically inconsistent across vehicles, each specific tyre being formed of a different percentage of various core components, particularly when e.g. comparing between cars and larger vehicles such as HGVs or large plant. As such, according to the present technology, it is desirable for the control system to analyse the makeup of the tyre crumbs that enter the waste processing plant, which may have originated from a range of different vehicles. The waste processing system may then be operated in various ways based on assessment performed by the control system, for example according to various objectives. 1. Singular output focus An objective may be set such that waste processing focuses on a single desired output product, such as carbon black from the tyres. The operation parameters of the waste processing system may be set to optimally deliver the single desired output, irrespective of the effects on other the yields or quality of other possible output products and / or operational economics. 2. Multiple output (demand based) focus In cases where there are multiple desired outputs, such as both hydrogen gas and carbon black, from processing the source waste material, an objective may be set to e.g. focus on a particular output based for example on demands of the output, and the control system may continually readjust the operation parameters of the waste processing system based on the dynamically changing objective at a given point in time. 3. Economics based focus An economic objective may be set for the control system to optimise the operation of the waste processing system based on economic factors. Such an objective may be to reduce or minimise the operational costs while maintaining a high (or highest possible) yield from product outputs. This mode of operation may depend strongly on external economic factors, such as the sale price of specific product outputs as well as various aspects of operational costs and conditions at the time. As such, a dynamic approach such as proposed by the present technology enables a waste processing system to operate optimally under such changing conditions. Implementation of the present technology further enables operation parameters of a waste processing system to be dynamically readjusted during operation when a new different waste stream enters the system. For example, for a tyre processing plant that can also process other forms of waste such as plastics or biomass, the control system may readjust the operation parameters based for example on varying proportions of tyres, plastics or biomass to optimise the operation efficiency of the waste processing system and / or the quality of product outputs. In some embodiments in which the optimisation is performed by an MLA executing on the control system, the MLA executing on the control system may collect and analyse operation data over time, and in time be able to suggest blends of input waste material to optimise the input waste composition e.g. for output product quality or quantity or for operation efficiency. As mentioned above, one consideration that influences the operation of the waste processing system is the external environment in which the waste processing system operates. For instance, the operation parameters required during the winter are expected to (and should) be different to the operation parameters required during the summer, since the temperature gradient between the system and the ambient air temperature would be different. Moreover, if (the reactor unit of) the waste processing system is collocated on the same site with an industrial waste heat source from a different industrial activity or process, such as a bank of electrolysers, the waste heat from the industrial process that would otherwise dissipate may instead be used e.g. to preheat the reactor unit, thereby reducing the energy demands of the waste processing system and increasing overall efficiency. Thus, in some embodiments, (the MLA executing on) the control system may be configured to adjust the operation parameters of the waste processing system based on changes in the external environment, including nearby waste heat. In particular, the control system may be configured to actively detect the availability of waste heat, e.g. via a heat exchange or heat recovery system, and store or redirect the waste heat to e.g. the reactor unit of the waste processing system. Using the example of the electrolysers, the electrolysers may be operated by a fluctuating renewable energy source, in which case the amount of hydrogen produced and therefore waste heat available is expected to be variable. In this example, the available waste heat that can be input to the reactor unit of the waste processing system is variable, and (the MLA executing on) the control unit may control the waste processing system so as to adjust the operation parameters to maintain the desired or optimal operation conditions (e.g. temperature) for the reactor unit as required, for example by increasing or decreasing the amount of heat injection. Alternatively, or additionally, the waste products generated by the waste processing system may be useful to other nearby industrial processes / activities. For example, where there is a storage system comprising metal hydrides used by the electrolysers of the example above, waste heat generated by the reactor unit of the waste processing system may be redirected or otherwise recovered for use by the metal hydride reactors, which require heat to release the hydrogen for the reaction. Such waste heat recovery can reduce the energy demands for the metal hydride reactors, thereby improving the overall efficiency by using waste heat from one process to provide heat for another. In such embodiments, the control system may be configured to control the waste processing system so as to recover waste heat and / or to redirect the waste heat to another process. FIG. 1 schematically shows a non-limiting example of a waste processing system 100, in which different source waste materials are added at different reactions stages of a torrefaction process. In the present embodiment, the waste processing system 100 comprises a control unit 110 configured to control the operation of the waste processing system 100, and three hoppers 120, 130 and 140. The control unit 110 may have stored thereon computer program which when executed perform a control method, or a machine learning algorithm (MLA) previously trained to perform the control method may be executing on the control unit 110. Each of the hoppers 120, 130 and 140 contains or stores different waste materials that are to be processed by thermal decomposition. The number of hoppers / storage units may differ in different embodiments and the waste material stored in each hopper as input feed material may differ as desired. For the purpose of illustration, hopper 120 contains e.g. tyre crumb, hopper 130 contains e.g. LDPE plastic films and hopper 140 contains e.g. biomass. The control system 110, using signals received from one or more sensors (not shown) provided to the hoppers 120, 130, 140, identifies the specific waste material present in each hopper and other characteristics of the waste material such as total weight, water content, etc. The waste processing system 100 further comprises crushing and preparation unit 150, configured to crush or otherwise prepare the source waste material for thermal decomposition. The operation of the crushing and preparation unit 150 is controlled by the control unit 110, which controls, for example, the rate at which waste materials are crushed, the size level to which waste materials are crushed, the volume or weight of each waste material to be prepared within a given time frame, etc. The crushed or otherwise prepared waste materials are then conveyed by a conveyor system 160 (e.g an augur screw) towards reactor units 170, 180 and 190 as needed. The different reactor units 170, 180, 190 may represent different reactor stages e.g. operating at different temperature levels for breaking down different waste materials, or they may represent entirely different types of reactor units operating different processes. According to the present embodiment, the control unit 110 is configured to optimise the operating conditions of each reactor unit 170, 180, 190, e.g. based on signals received from sensors (not shown) provided to the reactor units 170, 180, 190, so as to produce a predetermined (quality and / or yield of) desired output product (a material or a gas), for example as defined by a pre-set objective. In the present embodiment, reactor unit 170 may e.g. receive tyre crumbs and require a far higher operating temperature, while reactor unit 190 may e.g. receive biomass and require a lower operating temperature, as a result of differing chemical characteristics of the different waste materials. In general, it is not essential nor necessary to introduce new material to each reactor unit 170, 180, 190 as the waste material being processed is passed from one reactor unit to the next. Additional material may be introduced only when it is advantageous to process efficiency and or output quality or quantity. For example, biomass may be released from hopper 140 and added to the waste material being processed to improve process efficiency and / or to improve the quality of carbon black being produced. Thus, according to the present embodiment, the control unit 110 is configured to receive signals generated by sensors provided to the waste processing system 100 to assess the characteristics of the source waste material(s) and the current status of the reactor unit(s), so as to determine suitable or optimal operation parameters for various elements of the waste processing unit 100 such as the rate at which each waste stream is released and the operating parameters and conditions of the thermal decomposition process, e.g. crush speed, conveyor speed, reactor operating temperature and / or pressure, etc. It is therefore possible to improve or optimise the quality and / or quantity of the output materials produced by the waste processing system, and / or to improve or optimise the overall energy usage of the system. FIG. 2 schematically show another non-limiting example of a waste processing system 200, in which a multi-stage torrefaction process is implemented to process waste materials. A multi-stage process is believed to produce greater material outputs compared to a single reaction stage process. The waste processing system 200 comprises a control unit 210 configured to control its operation. Source waste material enters hopper 220 and is crushed or otherwise prepared as in the example of FIG. 1. The size to which the source waste material is being crushed and the rate at which it is released are again controlled by the control unit 210 based on characteristics of the waste material. The waste processing system 200 further comprises conveyors (e.g. augur screws) 230, 240, 250 for conveying prepared waste material respectively to reactor units 260, 270, 280. Each of the reactor units 260, 270, 280 is provided with a heat input (heat input 1, heat input 2, heat input 3) configured to inject heat into the respective reactor unit. It should be noted that heat injection is not limited to the start of a reactor; heat may be injected at different locations along each reactor units 260, 270, 280 and the location for heat injection for each reactor units 260, 270, 280 may be the same or different as desired. Each of the conveyor 230, 240, 250, heat inputs, and reactor units 260, 270, 280 may be operated independently having different system parameters, such that their operation parameters under which they operate may be independently determined and set by the control unit 210 e.g. to optimise recovery of materials. The operation parameters and settings for various elements of the waste processing system 200 (e.g. conveyor speed, reactor operating temperature, etc.) vary depending on the nature of the source waste material. For example, plastic waste requires a higher operating temperature than biomass. According to the present embodiment, the control unit 210 assesses various parameters of the waste processing system 200 available to it for changing or adjusting depending on the specific setup of the system, and determines the operation parameters under which the waste processing system 200 is to operate, e.g. the speed of each conveyor at different process stages, the specific levels at which to inject heat, etc., based for example on a desired objective, e.g. desired output materials (e.g. hydrogen, methane, carbon black, etc.), optimal efficiency, etc. FIG. 3 schematically show a further non-limiting example of a waste processing system 300, similar to the waste processing systems 100 and 200, but differ in that different source waste materials entering into the system via hopper 320 and hopper 330 are combined prior to processing. It is believed that, in doing so, production rate and / or energy efficiency of the process may be improved or optimised. Operation of the waste processing system 300 is again controlled by a control unit 310. Each of the hoppers 320 and 330 contains a different waste material, for example hopper 320 may contain tyres while hopper 330 may contain plastics. The control unit 310, through signals received from one or more sensors provided to the hoppers 320, 330, identifies the waste material in each hopper 320, 330 as well as other characteristics such as the water content of each waste material and the available weight of the waste material. These characteristics of each waste material influence the ratios or proportions of each waste material in the input stream mixture to be used in the process instead of an ideal ratio or proportion. For example, the ideal blend of tyres to plastic for energy efficiency may be 50 / 50; however, if there is substantially more tyre available than plastics, the control system 310 may determine whether to operation the reaction process at a 50 / 50 ratio until the plastics run out, or to operate at a less efficient blend of e.g. 80 / 20 ratio between tyres and plastics such that overall more waste material is processed. The control system 310 may determine the suitable ratio based e.g. on a pre-set objective of e.g. energy efficiency or total material output. If one of the source waste materials has higher water content compared to the other, the ratio of waste materials being blended prior to the reaction process may require significant adjustment to ensure that once the water is removed from that material, the process operates at an optimal level. Hopper locks 325, 335 are respectively provided to hoppers 320, 330, which open to release waste material at the desired mixing ratio to be carried to a reactor unit 350 by a conveyor system 340, under control by the control unit 310. Moreover, the control unit 310 is determines when to open each hopper lock 325, 335 based on the current status of reactor unit 350. For example, a new batch of waste material is released when it is determined that processing of the current batch of material is nearly complete. This facilitates a continuous process in which more material is introduced to the reactor unit 350 at specific times such that the temperature dynamics within the reactor unit 350 is not affected while maintaining a desired level of efficiency. FIG. 4 shows a further non-limiting example of a waste processing system according to an embodiment, in which within the same industrial space which the waste processing system 400a operates there is another industrial process 400b nearby. The waste processing system 400a again comprises a control unit 410 configured to control the operation of the waste processing system 400a. The waste processing system 400a may, for example, implement a hydrolysis process, in which case reactor unit 440 is a hydrolysis reactor. The nearby process 400b may, for example, be an electrolyser and reactor unit 470 is a metal hydride reactor. In the present example, waste material 420 and waste material 430 enter the reactor 440 under the control of the control unit 410 e.g. at a specific rate and / or ratio, and the reactor unit 440 processes the waste material mixture under the control of the control unit e.g. to inject heat at a particular level or to operate at a specific temperature, as described above. In the present example, the reactor unit 440 produces output material 450 (e.g. carbon black) and output material 460 (e.g. mixed gas output). The output material 460, in the present example, is used as an input material to the reactor unit 470 of the nearby process 400b for further processing, e.g. to separate hydrogen from the mixed gas output stream. The reactor unit 470 processes the output material 460 to produce output material 480 (e.g. syngas) and output material 490 (e.g. purified hydrogen). The output materials 480, 490 may be further processed to extract other useful material or be packaged to be used by end users. For example, hydrogen gas may be compressed to be used by e.g. industrial customers, fuel cell vehicle operators, etc., while syngas may be further processed e.g. for power generation. In addition, in the present embodiment, the control unit 410, through use of suitable sensors provided e.g. to the reactor units 440, 470, is configured to determine the availability of waste heat generated by the reactor units 440, 470 during operation. Suitable heat recovery system may be implemented to the waste processing system 400a and / or the nearby process 400b such that, upon detection of waste heat being available, the control unit 410 controls the heat recovery system (not shown) to redirect waste heat generated by the reactor unit 440 to the reactor unit 470, and redirect waste heat generated by the reactor unit 470 to the reactor unit 440, for example to pre-heat source materials prior to processing by the reactor units 440, 470 and / or to heat the reactor units 440, 470 so as to reduce the overall energy consumption. FIG. 5 shows schematically a system overview of the input and output parameters of a control system 500 for a waste processing system, the control system comprising a control unit such as control units 110, 210, 310, 410 and one or more sensors provided internally or externally to the waste processing system. As shown in FIG. 5, inputs to the control system, in the present example, include waste heat availability 511 (e.g. detected by one or more temperature sensors on heat recovery input), ambient temperature 512 (e.g. detected by one or more temperature sensors on outer reactor housing), system parameters 513 (e.g. number of reactor stages and conveyor systems, number of material containers / hoppers, production capacities, heating methods available, power sources and consumption, e.g. detected by voltage / current sensors, temperature sensors, material / weight sensors, etc.), current system status 514 (e.g. reactor temperatures, material levels, progress of the process, e.g. detected by voltage / current sensors, temperature sensors, material / weight sensors, etc.), waste material characteristics 515 (e.g. material analysers and / or weight sensors in hoppers, water content sensors), processing objective 516 (e.g. specific type or quality of output materials or gasses, economic objectives such as low production costs or high production rate), and (where applicable) economic vectors 517 (e.g. energy price, market rate of various output materials, etc.). Then, after processing by (the control unit of) the control system 500, e.g. using one or more MLAs, the control system 500 outputs operation parameters including material extraction method 521, which may for example include metal hydride reactor for removing hydrogen, solid material collection hopper for collecting carbon black, gas capture and purification system, etc., material extraction point 522, which may for example be the stage at which a material output is extracted in a multi-stage process (e.g. Output 1, Output 2 and Output 3 in FIG. 2) or the location at which a material output is extracted along a reactor unit (e.g. half way along, at the end, etc.), conveyor (screw) speed 523 (e.g. detected by load and / or speed sensors), operation time 524, reactor pressure 525 (e.g. detected by pressure sensors), reactor heat input method 526, and reactor operation temperature 527 (e.g. detected by temperature sensors). FIG. 6 shows a flow diagram of a non-limiting example of a method of controlling a waste processing system by a control unit according to an embodiment. The method 600 begins at S610, with the control unit assessing source waste material to obtain one or more source waste material characteristics, the source waste material being the material to be input to the waste processing unit for processing. For example, the one or more source waste material characteristics may include a type of source waste material, a composition of the source waste material, quality of the source waste material, a water content of the source waste material, a total amount of the source waste material, or any combination thereof. In some case, the source waste material may comprise a first waste material and a second waste material, and the one or more source waste material characteristics may then include an available amount of the first waste material and an available amount of the second waste material. Then at S620, the control unit obtains one or more system parameters of the waste processing system. This enables the control unit to determine the current status of the waste processing system. The waste processing system may comprise a thermal reactor unit (e.g. a torrefaction, pyrolysis and / or hydrolysis reactor unit), and the control unit may obtaining obtain the ambient temperature around the thermal reactor as one of the system parameters. The one or more system parameters may further comprise the number of source material storage containing source waste material, the number of reactor stages, the number of conveyor systems for conveying the source waste material, one or more heating methods and heat injection locations available to the thermal reactor, one or more power sources, power consumption rate, waste processing system capacity, or a combination thereof. Based on the one or more source waste material characteristics obtained at S610 and the one or more system parameters obtained at S620, the control unit determines, at S630, one or more operation parameters for the waste processing system. The waste processing system may comprise a source material storage (e.g. hoppers (120, 130, 140, etc.) for storing the source waste material. Then, the determination of one or more operation parameters may include determining a rate of releasing the source waste material from the source material storage based on the one or more source waste material characteristics and the one or more system parameters. Where there are more than one source waste materials to be process, e.g. a first source waste material and a second source waste material, the determination of one or more operation parameters may include determining a ratio between a rate of releasing the first waste material e.g. from the first source material storage (e.g. hopper 1) and a rate of releasing the second waste material from the second source material storage (e.g. hopper 2) based on the amount of the first waste material and the amount of second waste material and the one or more system parameters. The control unit may assess the source waste material by determining any dependency between an amount of the second waste material and an efficiency of processing the first waste material, and then determines the ratio between the rate of releasing the first waste material and the rate of releasing the second waste material further based on the determined dependency. Where the waste processing unit comprises a thermal reactor unit, the determination of one or more operation parameters may comprise determining an operation temperature for the thermal reactor based on the one or more source waste material characteristics and the ambient temperature around the thermal reactor. Then the control unit, at S640, operates the waste processing system to process the source waste material based on the determined one or more operation parameters. In some embodiments, the control unit may, at S650, determine an availability of waste heat for heat recovery as one of the system parameters. Then, upon determining that waste heat is available, the control unit may direct the waste heat into the waste processing system. For example, directing the waste heat into the waste processing system may comprise directing the waste heat to the thermal reactor to pre-heat the thermal reactor, and / or directing the waste heat to the source material storage to pre-heat the source waste material. In some embodiments, the control unit may, at S660, control (a waste heat recovery system of) the waste processing system to recover heat produced by the waste processing system e.g. by storing the heat in a heat repository and / or by directing the heat to an external process. It may be desirable, in some embodiments, to set a processing objective for the control unit and for the control unit to determine the one or more operation parameters for the waste processing system based on the processing objective. For example, the processing objective may include optimising a yield of a predetermined output product, optimising a quality of a predetermined output product, optimising an operation cost, and / or optimising a profit to be obtained amongst a plurality of output products. The method described herein may be performed by one or more MLAs executing on a control unit such as control units 110, 210, 310, 410 provided to the waste processing system or remotely from a computer or computer system arranged to communicate with the waste processing system. The following gives a brief overview of a number of different types of machine learning algorithms for embodiment(s) in which one or more MLAs are used. However, it should be noted that the use of an MLA in these embodiment(s) is a non-limiting example of implementing the present technology, and the use of an MLA is not essential. Overview of MLAs There are many different types of MLAs known in the art. Broadly speaking, there are three types of MLAs: supervised learning-based MLAs, unsupervised learning-based MLAs, and reinforcement learning-based MLAs. Supervised learning MLA process is based on a target - outcome variable (or dependent variable), which is to be predicted from a given set of predictors (independent variables). Using this set of variables, the MLA generates a function using training data that maps inputs to desired outputs during training. The training process continues until the MLA achieves a desired level of accuracy on validation data. Examples of supervised learning-based MLAs include: Regression, Decision Tree, Random Forest, Logistic Regression, etc. Unsupervised learning MLA does not involve predicting a target or outcome variable but learns patterns from untagged data. Such MLAs are capable of selforganization to capture patterns as probability densities, and are used e.g. for clustering a population of values into different groups. Clustering is used in many fields including pattern recognition, image analysis, bioinformatics, data compression, computer graphics, etc. Examples of unsupervised learning MLAs include: apriori algorithm and k-means algorithm. Reinforcement learning MLA is trained to take actions or make decisions that maximize cumulative reward (e.g. a user-provided score). During training, the MLA is exposed to a training environment where it learns through trial and error to develop an optimal or near-optimal policy that maximizes reward. In doing so, the MLA learns from past experience and attempts to capture the best possible knowledge to make desirable decisions. An example of reinforcement learning MLA is a Markov Decision Process. It should be understood that different types of MLAs having different structures or topologies may be used for various tasks. One particular type of MLAs includes artificial neural networks (ANN), also known as neural networks (NN). Neural Networks (NN) Generally speaking, a given NN consists of an interconnected group of artificial "neurons", which process information using a connectionist approach to computation. NNs are used to model complex relationships between inputs and outputs (without actually knowing the relationships) or to find patterns in data. NNs are first conditioned in a training phase in which they are provided with a known set of "inputs" and information for adapting the NN to generate appropriate outputs (for a given situation that is being attempted to be modelled). During this training phase, the given NN adapts to the situation being learned and changes its structure such that the given NN will be able to provide reasonable predicted outputs for given inputs in a new situation (based on what was learned). Thus, rather than attempting to determine a complex statistical arrangements or mathematical algorithms for a given situation, the given NN aims to provide an "intuitive" answer based on a "feeling" for a situation. The given NN is thus regarded as a trained "black box", which can be used to determine a reasonable answer to a given set of inputs in a situation giving little importance to what happens inside the "box". NNs are commonly used in many such situations where an appropriate output based on a given input is important, but exactly how that output is derived is of lesser importance or is unimportant. For example, NNs are commonly used to optimize the distribution of web-traffic between servers and in data processing, including filtering, clustering, signal separation, compression, vector generation and the like. Deep Neural Networks In some non-limiting embodiments of the present technology, the NN can be implemented as a deep neural network. It should be understood that NNs can be classified into various classes of NNs. Below are a few non-limiting example classes of NNs. Recurrent Neural Networks (RNNs) RNNs are adapted to use their "internal states" (stored memory) to process sequences of inputs. This makes RNNs well-suited for tasks such as unsegmented handwriting recognition and speech recognition, for example. These internal states of the RNNs can be controlled and are referred to as "gated" states or "gated" memories. It should also be noted that RNNs themselves can also be classified into various sub-classes of RNNs. For example, RNNs comprise Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Bidirectional RNNs (BRNNs), and the like. LSTM networks are deep learning systems that can learn tasks that require, in a sense, "memories" of events that happened during very short and discrete time steps earlier. Topologies of LSTM networks can vary based on specific tasks that they "learn" to perform. For example, LSTM networks may learn to perform tasks where relatively long delays occur between events or where events occur together at low and at high frequencies. RNNs having particular gated mechanisms are referred to as GRUs. Unlike LSTM networks, GRUs lack "output gates" and, therefore, have fewer parameters than LSTM networks. BRNNs may have "hidden layers" of neurons that are connected in opposite directions which may allow using information from past as well as future states. Residual Neural Network (ResNet) Another example of the NN that can be used to implement non-limiting embodiments of the present technology is a residual neural network (ResNet). Deep networks naturally integrate low / mid / high-level features and classifiers in an end-to-end multilayer fashion, and the "levels" of features can be enriched by the number of stacked layers (depth). Convolutional Neural Network (CNN) CNNs are also known as shift invariant or space invariant artificial neural networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation equivariant responses known as feature maps. They are most commonly applied to analyze visual imagery and have applications in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain-computer interfaces, and financial time series. CNNs are regularized fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. CNNs use relatively little preprocessing compared to other image classification algorithms and learn to optimize the filters (or kernels) through automated learning. To summarize, the implementation of at least a portion of the one or more MLAs in the context of the present technology can be broadly categorized into two phases - a training phase and an in-use or deployed phase. First, the given MLA is trained in the training phase using one or more appropriate training data sets. Then, once the given MLA learned what data to expect as inputs and what data to provide as outputs, the given MLA is executed using in-use data in the in-use or deployed phase. Further, while deployed, the given MLA may continue to learn from the in-use data based for example on user feedback. The various MLAs described above may refer to the same or different MLA. If multiple MLAs are implemented, one or some or all of the MLAs may be executed on the device, and one or some or all of the MLAs may be executed on a server (e.g. a cloud server) in communication with the device via a suitable communication channel. It will be understood by those skilled in the art that the embodiments above may be implemented in any combinations, in parallel or as alternative strategies as desired. As will be appreciated by one skilled in the art, the present techniques may be embodied as a system, method or computer program product. Accordingly, the present techniques may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present techniques may take the form of a computer program product embodied in a computer readable medium having computer readable program code embodied thereon. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable medium may be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Computer program code for carrying out operations of the present techniques may be written in any combination of one or more programming languages, including object-oriented programming languages and conventional procedural programming languages. For example, program code for carrying out operations of the present techniques may comprise source, object or executable code in a conventional programming language (interpreted or compiled) such as C, or assembly code, code for setting up or controlling an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array), or code for a hardware description language such as VerilogTM or VHDL (Very high-speed integrated circuit Hardware Description Language). The program code may execute entirely on the user's computer, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network. Code components may be embodied as procedures, methods or the like, and may comprise sub-components which may take the form of instructions or sequences of instructions at any of the levels of abstraction, from the direct machine instructions of a native instruction set to high-level compiled or interpreted language constructs. It will also be clear to one of skill in the art that all or part of a logical method according to the preferred embodiments of the present techniques may suitably be embodied in a logic apparatus comprising logic elements to perform the steps of the method, and that such logic elements may comprise components such as logic gates in, for example a programmable logic array or application-specific integrated circuit. Such a logic arrangement may further be embodied in enabling elements for temporarily or permanently establishing logic structures in such an array or circuit using, for example, a virtual hardware descriptor language, which may be stored and transmitted using fixed or transmittable carrier media. The examples and conditional language recited herein are intended to aid the reader in understanding the principles of the present technology and not to limit its scope to such specifically recited examples and conditions. It will be appreciated that those skilled in the art may devise various arrangements which, although not explicitly described or shown herein, nonetheless embody the principles of the present technology and are included within its scope as defined by the appended claims. Furthermore, as an aid to understanding, the above description may describe relatively simplified implementations of the present technology. As persons skilled in the art would understand, various implementations of the present technology may be of a greater complexity. In some cases, what are believed to be helpful examples of modifications to the present technology may also be set forth. This is done merely as an aid to understanding, and, again, not to limit the scope or set forth the bounds of the present technology. These modifications are not an exhaustive list, and a person skilled in the art may make other modifications while nonetheless remaining within the scope of the present technology. Further, where no examples of modifications have been set forth, it should not be interpreted that no modifications are possible and / or that what is described is the sole manner of implementing that element of the present technology. Moreover, all statements herein reciting principles, aspects, and implementations of the technology, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof, whether they are currently known or developed in the future. Thus, for example, it will be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the present technology. Similarly, it will be appreciated that any flowcharts, flow diagrams, state transition diagrams, pseudo-code, and the like represent various processes which may be substantially represented in computer-readable media and so executed by a computer or processor, whether or not such computer or processor is explicitly shown. The functions of the various elements shown in the figures, including any functional block labeled as a "processor", may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term "processor" or "controller" should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read-only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage. Other hardware, conventional and / or custom, may also be included. Software modules, or simply modules which are implied to be software, may be represented herein as any combination of flowchart elements or other elements indicating performance of process steps and / or textual description. Such modules may be executed by hardware that is expressly or implicitly shown. It will be clear to one skilled in the art that many improvements and modifications can be made to the foregoing exemplary embodiments without departing from the scope of the present techniques.

Claims

1. A computer-implemented method of controlling waste processing in a waste processing system, comprising:assessing source waste material for one or more source waste material characteristics;obtaining one or more system parameters of the waste processing system; determining one or more operation parameters for the waste processing system based on the one or more source waste material characteristics and the one or more system parameters; andoperating the waste processing system to process the source waste material based on the determined one or more operation parameters.

2. The method of claim 1, wherein the one or more source waste material characteristics comprises a type of source waste material, a composition of the source waste material, quality of the source waste material, a water content of the source waste material, a total amount of the source waste material, or any combination thereof.

3. The method of claim 1 or 2, wherein the waste processing system comprises a source material storage for storing the source waste material, and determining one or more operation parameters comprises determining a rate of releasing the source waste material from the source material storage based on the one or more source waste material characteristics and the one or more system parameters.

4. The method of claim 1, 2 or 3, wherein the source waste material comprises a first waste material and a second waste material and the waste processing system comprises a first source material storage for storing the first waste material and a second source material storage for storing the second waste material, and the one or more source waste material characteristics comprises an amount of the first waste material and an amount of the second waste material.

5. The method of claim 4, wherein determining one or more operation parameters comprises determining a ratio between a rate of releasing the first waste material from the first source material storage and a rate of releasing thesecond waste material from the second source material storage based on the amount of the first waste material and the amount of second waste material and the one or more system parameters.

6. The method of claim 5, wherein assessing source waste material further comprises determining a dependency between an amount of the second waste material and an efficiency of processing the first waste material, and determining a ratio between a rate of releasing the first waste material from the first source material storage and a rate of releasing the second waste material from the second source material storage is performed further based on the dependency.

7. The method of any preceding claim, wherein the waste processing system comprises a thermal reactor, and obtaining one or more system parameters comprises obtaining an ambient temperature around the thermal reactor.

8. The method of claim 7, wherein determining one or more operation parameters comprises determining an operation temperature for the thermal reactor based on the one or more source waste material characteristics and the ambient temperature around the thermal reactor.

9. The method of claim 7 or 8, wherein obtaining one or more system parameters comprises determining an availability of waste heat for heat recovery.

10. The method of claim 9, further comprising, upon determining that waste heat is available, directing the waste heat into the waste processing system.

11. The method of claim 10, wherein the waste processing system comprises a thermal reactor, and directing the waste heat into the waste processing system comprises directing the waste heat to the thermal reactor to pre-heat the thermal reactor.

12. The method of claim 10 or 11, wherein the waste processing system comprises a source material storage for storing the source waste material, and directing the waste heat into the waste processing system comprises directing the waste heat to the source material storage to pre-heat the source waste material.

13. The method of any of claims 7 to 12, wherein the one or more system parameters further comprises a number of source material storage containing source waste material, a number of reactor stages, a number of conveyor system for conveying the source waste material, one or more heating methods available to the thermal reactor, one or more power sources, power consumption rate, waste processing system capacity, or a combination thereof.

14. The method of any preceding claim, further comprising recovering heat produced by the waste processing system by storing the heat in a heat repository and / or by directing the heat to an external process.

15. The method of any preceding claim, further comprising setting a processing objective and determining the one or more operation parameters for the waste processing system further based on the processing objective.

16. The method of claim 15, wherein the processing objective comprises optimising a yield of a predetermined output product, optimising a quality of a predetermined output product, optimising an operation cost, and / or optimising a profit to be obtained amongst a plurality of output products.

17. The method of any preceding claim, wherein the source waste material comprises solid waste from water treatment plants, tyres, plastics, and / or biomass.

18. The method of any preceding claim, wherein the waste material processing comprises a torrefaction process, a pyrolysis process, and / or comprises a hydrolysis process.

19. A non-transitory computer readable storage medium storing software code which, when executed on one or more data processors, executes one or more machine learning algorithms previously trained to perform the method of any preceding claim.

20. A control system for controlling waste material processing in a wasteprocessing system, the control system comprising:one or more first sensors configured to detect source waste material to generate first sensor data;one or more second sensors configured to detect the waste processing system to generate second sensor data; anda control unit configure to:assess the source waste material for one or more source waste material characteristics based on the first sensor data;determine one or more system parameters of the waste processing system based on the second sensor data;determining one or more operation parameters for the waste processing system based on the one or more source waste material characteristics and the one or more system parameters; andoperating the waste processing system to process the source waste material based on the determined one or more operation parameters.

21. The control system of claim 20, wherein the waste processing system comprises at least one source material storage configured to store source waste material, and the one or more first sensors are disposed within, on or around the at least one source material storage.

22. The control system of claim 21, wherein the one or more first sensors comprise at least one weight sensor configured to detect a weight of the waste material, at least one material sensor configured to detect a material type of the source waste material, at least one voltage and / or current sensor configured to detect a conductance and / or resistance of the source waste material, or a combination thereof.

23. The control system of any of claims 20 to 22, wherein the waste processing system comprises a thermal reactor configured to process the source waste material, and the one or more second sensors are disposed within, on or around the thermal reactor.

24. The control system of claim 23, wherein the one or more second sensors comprise at least one temperature sensor configured to detect a temperature ofthe thermal reactor and / or at least one ambient temperature sensor configured to detect an ambient temperature around the thermal reactor.

25. A waste processing system comprising:5 at least one source material storage configured to store source wastematerial;a thermal reactor configured to process the source waste material;a conveyor system configured to convey the source waste material from the at least one source material storage to the thermal reactor; and10 a control system of any of claims 20 to 24.

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