Zero-carbon factory control system and method

Abstract

The invention relates to the technical field of zero-carbon factory control, in particular to a zero-carbon factory control system and method.The control system comprises a data acquisition layer, a central decision-making layer, an execution layer and a block chain evidence storage layer, and the data acquisition layer comprises an energy carbon flow monitoring module, a process carbon flow monitoring module and a resource carbon flow monitoring module; the central decision-making layer comprises a carbon footprint real-time accounting engine, an optimization control module and a digital twin module, and the execution layer comprises an energy scheduling subsystem and a production optimization subsystem. According to the method, full-dimension, real-time and refined sensing and accurate carbon control of carbon data are realized, a control instruction comprehensively considering carbon, energy and efficiency is generated through an advanced algorithm, simulation verification and effect rehearsal are performed on the control instruction in a virtual space, potential problems are found in advance, reliability and safety are improved, and the method is suitable for popularization and application. And meanwhile, deep fusion of carbon management and production operation is realized, and key carbon data and hash values thereof are chained through a block chain evidence storage layer.

Description

Technical Field

[0001] This invention relates to the field of zero-carbon factory control technology, specifically to a zero-carbon factory control system and method. Background Technology

[0002] With the deepening implementation of the "carbon peaking and carbon neutrality" strategy, the green and low-carbon transformation of industrial manufacturing, as one of the main sources of carbon emissions, has become increasingly urgent. Zero-carbon factories, as the ultimate goal of achieving carbon neutrality in the industrial sector, aim to bring the factory's net carbon emissions close to zero through comprehensive measures. This not only requires factories to use green energy but also necessitates refined carbon flow management across all aspects, including energy consumption, production processes, and material flow.

[0003] Currently, most manufacturing plants are still in a relatively early stage of carbon management, mainly facing the following technological bottlenecks:

[0004] First, carbon data perception capabilities are insufficient, and accounting is lagging. Existing methods mainly focus on measuring energy consumption, but lack effective and real-time monitoring of direct carbon emissions during production processes (such as emissions from chemical reactions and escape emissions) and the carbon footprint contained in raw materials. Carbon accounting often relies on periodic, post-hoc manual checks and calculations, resulting in coarse data granularity and poor timeliness, which cannot support real-time and accurate carbon management.

[0005] Second, the control system is disconnected from carbon management objectives. Traditional factory control systems (such as energy management systems and manufacturing execution systems) are usually limited to optimizing energy costs, equipment efficiency, or production quality, which makes it difficult to quickly translate carbon data into specific and executable production and energy dispatch instructions.

[0006] Third, there is a lack of global optimization through multi-system collaboration. The integration of zero-carbon energy facilities such as distributed photovoltaics and energy storage increases the complexity of the factory's energy system. Current management methods mostly involve independent operation or only simple linkage, failing to integrate and coordinate energy dispatch, production scheduling, and process parameter adjustments for optimal optimization. This makes it difficult to achieve the optimal solution for carbon emission reduction and energy costs while ensuring production efficiency.

[0007] Fourth, there is a lack of technological means to ensure the authenticity and immutability of carbon data, making it difficult to provide downstream customers or international trade with recognized carbon footprint credentials. No solutions have yet been proposed to address these technical issues. Summary of the Invention

[0008] To address the problems in related technologies, this invention proposes a zero-carbon factory control system and method to overcome the aforementioned technical issues in existing technologies. The purpose of this invention is to achieve full-dimensional, real-time, and refined perception of carbon data, enabling precise carbon control. Through advanced algorithms, control commands that comprehensively consider carbon, energy, and efficiency are generated. The control commands are simulated and verified in virtual space, and their effects are previewed. This allows for the early detection of potential problems, avoiding the execution of suboptimal or risky commands in the physical factory, greatly improving the reliability and security of decision-making. At the same time, abstract carbon management goals are transformed into concrete and executable operational actions, achieving deep integration of carbon management and production operations. Key carbon data and their hash values ​​are recorded on the blockchain through a blockchain evidence layer, giving the carbon data immutable credibility.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a zero-carbon factory control system, comprising:

[0010] The data acquisition layer is used to collect carbon-related data from the factory in all dimensions. The data acquisition layer includes:

[0011] The energy carbon flow monitoring module is used to monitor the consumption of external energy and its corresponding carbon emission factors in real time.

[0012] The process carbon flow monitoring module is used to monitor the direct carbon emissions of key processes.

[0013] The resource carbon flow monitoring module is used to monitor the carbon flow implied in the flow of materials and waste.

[0014] The central decision-making layer is communicatively connected to the data acquisition layer, and the central decision-making layer includes:

[0015] The real-time carbon footprint calculation engine is used to dynamically calculate the real-time carbon footprint of the entire plant and individual products based on the collected data.

[0016] The optimized control module is used to generate control commands with carbon emissions, energy costs, and production efficiency as common optimization objectives.

[0017] An execution layer, which is communicatively connected to the central decision-making layer, is used to execute the control commands. The execution layer includes:

[0018] The energy dispatch subsystem is used to coordinate and control the operation of distributed energy sources, energy storage systems, and microgrids within the plant;

[0019] The production optimization subsystem is used to adjust the production plan and process parameters according to the control instructions.

[0020] Preferably, the central decision-making layer further includes a digital twin module, which constructs a virtual model corresponding to the factory. The virtual model receives real-time data from the data acquisition layer for synchronization and performs simulation verification and effect preview of the control commands generated by the optimization control module.

[0021] Preferably, it also includes a blockchain evidence storage layer, which is connected to the central decision-making layer, and the blockchain evidence storage layer stores the key carbon data and its hash value generated by the real-time carbon footprint accounting engine on the blockchain.

[0022] Preferably, the process carbon flow monitoring module includes a direct carbon emission quantification unit, which integrates an infrared gas analyzer and a flow meter.

[0023] This invention also provides the following technical solutions:

[0024] A zero-carbon factory control method includes the following steps:

[0025] S1: Real-time data collection of energy, process, and resource carbon flow data of the factory is achieved through the data acquisition layer;

[0026] S2: The central decision-making layer dynamically calculates the carbon footprint based on the collected data, and the optimization control module generates global optimization control instructions;

[0027] S3: The control commands are simulated, verified, and optimized using the digital twin module, and the optimized commands are sent to the execution layer;

[0028] S4: The control instructions are executed through the execution layer to control the coordinated operation of the energy dispatching subsystem and the production optimization subsystem;

[0029] S5: Key carbon data is stored on the blockchain through the blockchain evidence storage layer, and traceable carbon footprint information is generated for the product.

[0030] Preferably, in step S2, the specific process of generating the global optimization control command includes:

[0031] During periods of low external electricity prices and peak photovoltaic power generation within the plant, instructions are generated to control the energy dispatch subsystem to prioritize charging the energy storage system and to control the production optimization subsystem to schedule high-energy-consuming processes during this time period.

[0032] When the calculated real-time carbon footprint approaches a preset threshold, an instruction is generated to control the production optimization subsystem to temporarily reduce non-critical capacity or switch to a low-carbon raw material formula.

[0033] Compared with the prior art, the beneficial effects of the present invention are:

[0034] (1) This invention is a zero-carbon factory control system and method. By setting up a data acquisition layer, it not only monitors the indirect carbon emissions of purchased energy, but also accurately captures the direct emissions in the production process through a direct carbon emission quantification unit that integrates an infrared gas analyzer and a flow meter. At the same time, it tracks the hidden carbon in materials and waste, filling the gap in the traditional system for monitoring process and resource carbon flow. It changes the traditional periodic and manual accounting lagging mode. The carbon footprint real-time accounting engine can dynamically calculate the carbon footprint of the whole plant and unit products, and can monitor key performance indicators in real time, providing timeliness for management decisions.

[0035] (2) This invention is a zero-carbon factory control system and method. By setting up a central decision-making layer, the optimized control module generates control instructions that comprehensively consider carbon, energy and efficiency through advanced algorithms, thus achieving a balance between economy and low carbon. By using a digital twin module to simulate and verify the control instructions and perform effect previews in virtual space, potential problems can be discovered in advance, avoiding the execution of suboptimal or risky instructions in the physical factory, which greatly improves the reliability and safety of decision-making.

[0036] (3) This invention is a zero-carbon factory control system and method. By setting up an execution layer, the abstract carbon management goal is transformed into specific and executable operational actions, thereby achieving a deep integration of carbon management and production operation. By setting up a blockchain evidence storage layer, key carbon data and its hash value are put on the chain, giving the carbon data an immutable credibility and generating traceable carbon footprint information for the product. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the overall structural framework of the present invention.

[0038] In the attached diagram, the following are the reference numerals: 100, Data Acquisition Layer; 101, Energy Carbon Flow Monitoring Module; 102, Process Carbon Flow Monitoring Module; 103, Resource Carbon Flow Monitoring Module; 200, Central Decision-Making Layer; 201, Real-Time Carbon Footprint Calculation Engine; 202, Optimization Control Module; 203, Digital Twin Module; 300, Execution Layer; 301, Energy Dispatch Subsystem; 302, Production Optimization Subsystem; 400, Blockchain Evidence Storage Layer. Detailed Implementation

[0039] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0040] Example

[0041] Please see Figure 1 This invention proposes a technical solution for a zero-carbon factory control system and method: a zero-carbon factory control system comprising:

[0042] Data acquisition layer 100 is used to collect carbon-related data from the factory in all dimensions. Data acquisition layer 100 includes:

[0043] The energy carbon flow monitoring module 101 is used to monitor the consumption of external energy and its corresponding carbon emission factors in real time.

[0044] Process carbon flow monitoring module 102 is used to monitor the direct carbon emissions of key processes;

[0045] Resource carbon flow monitoring module 103 is used to monitor the carbon flow implied in the flow of materials and waste;

[0046] Central decision-making layer 200 is communicatively connected to data acquisition layer 100. Central decision-making layer 200 includes:

[0047] The Carbon Footprint Real-Time Calculation Engine 201 is used to dynamically calculate the real-time carbon footprint of the entire plant and individual products based on the collected data.

[0048] The optimization control module 202 is used to generate control commands with carbon emissions, energy costs and production efficiency as common optimization objectives. Specifically, the optimization control module 202 adopts an algorithm based on deep reinforcement learning. The deep reinforcement learning algorithm models the factory's energy system and production system as a collaborative environment, and uses minimizing the carbon footprint per unit product and the overall energy cost as the reward function. Through interactive learning with the environment, it autonomously iterates and optimizes the control commands.

[0049] Execution layer 300, which is communicatively connected to the central decision-making layer 200, is used to execute control commands. Execution layer 300 includes:

[0050] The energy dispatch subsystem 301 is used to coordinate and control the operation of distributed energy sources, energy storage systems and microgrids within the plant;

[0051] The production optimization subsystem 302 is used to adjust the production plan and process parameters according to control instructions.

[0052] In this embodiment, the action result of the execution layer 300 will be perceived again through the data acquisition layer 100 and fed back to the central decision-making layer 200, forming a real-time feedback closed loop of "monitoring-calculation-optimization-execution-re-monitoring", which enables the system to adaptively adjust its strategy and continuously approach the optimal operating state.

[0053] The Energy Carbon Flow Monitoring Module 101 collects the precise consumption of various purchased energy sources in real time through IoT instruments such as smart meters, gas meters, and steam flow meters. The Energy Carbon Flow Monitoring Module 101 has an embedded, constantly updated carbon emission factor database, which includes regional power grid factors, specific fuel factors, etc. It realizes real-time accounting of indirect carbon emissions through the formula: carbon emissions = activity data × emission factor.

[0054] The process carbon flow monitoring module 102 is key to achieving precise carbon control. At critical emission sources such as boilers, incinerators, and chemical reactors, it uses a non-dispersive infrared gas analyzer to measure the concentrations of greenhouse gases such as CO2 and CH4 in flue gas or exhaust gas in real time, while simultaneously using a flow meter to measure the total flow rate of the waste gas. By calculating emission volume = concentration × flow rate × operating time, it enables minute-level or even second-level monitoring of direct emissions.

[0055] The Resource Carbon Flow Monitoring Module 103 interfaces with the enterprise's ERP (Enterprise Resource Planning) and MES (Manufacturing Execution System) to acquire data on raw material input, product output, and waste generation. The module has a built-in Life Cycle Assessment (LCA) database for various materials, and calculates the flow of implicit carbon through material balance and database matching.

[0056] The Carbon Footprint Real-Time Calculation Engine 201 can perform detailed calculations in three levels and units: Scope 1 (direct emissions), Scope 2 (indirect emissions from purchased energy), and Scope 3 (other indirect emissions).

[0057] The energy dispatch subsystem 301 dynamically determines the source and destination of electricity based on instructions. For example, during the midday peak of photovoltaic power generation, priority is given to ensuring electricity for production, and excess electricity is used to charge energy storage; during the nighttime off-peak electricity price period, electricity is purchased from the grid and used to charge energy storage in preparation for peak use the next day.

[0058] The production optimization subsystem 302 receives carbon constraint instructions and flexibly adjusts the manufacturing process. For example, it precisely schedules high-energy-consuming equipment such as kilns and air compressors that operate intermittently to run during low-carbon electricity periods; or it suspends auxiliary facilities in the workshop or adjusts the product production sequence when carbon footprint is tight.

[0059] Please see Figure 1 As shown, the central decision-making layer 200 further includes a digital twin module 203. The digital twin module 203 constructs a virtual model corresponding to the factory. The virtual model receives real-time data from the data acquisition layer 100 for synchronization and performs simulation verification and effect preview of the control commands generated by the optimization control module 202.

[0060] In this embodiment, the digital twin module 203 first performs high-speed simulation at the millisecond level in the virtual model to predict the carbon, energy, and efficiency results after execution. This not only verifies the effectiveness of the instructions but also discovers potential conflicts and risks (such as whether it will lead to grid power exceeding limits), thereby achieving "pre-optimization" of control instructions and greatly improving the safety and reliability of actual control.

[0061] Please see Figure 1As shown, it further includes a blockchain evidence storage layer 400, which is connected to the central decision-making layer 200. The blockchain evidence storage layer 400 stores the key carbon data and its hash value generated by the carbon footprint real-time accounting engine 201 on the blockchain.

[0062] In this embodiment, whenever the real-time carbon footprint calculation engine 201 completes a calculation cycle (e.g., 1 hour), it generates a unique digital fingerprint (hash value) for key data (such as total carbon emissions, photovoltaic power generation, and product carbon footprint) and writes it to the blockchain. The hash value is unique and tamper-proof. Any slight alteration to the original data will result in a significant change in the hash value. Any stakeholder (such as customers or regulatory agencies) can verify whether the currently acquired carbon data matches the hash value initially uploaded to the blockchain, thus trusting the authenticity of the data without needing to trust the company itself. This provides indisputable electronic evidence for product carbon footprint labeling and carbon tariff declarations.

[0063] Please see Figure 1 As shown, the process carbon flow monitoring module 102 further includes a direct carbon emission quantification unit, which integrates an infrared gas analyzer and a flow meter.

[0064] In this embodiment, the direct carbon emission quantification unit adopts a fusion technology of "sensor + material balance".

[0065] A zero-carbon factory control method includes the following steps:

[0066] S1: Real-time acquisition of energy, process, and resource carbon flow data of the factory through data acquisition layer 100;

[0067] S2: The central decision-making layer 200 dynamically calculates the carbon footprint based on the collected data, and the optimization control module 202 generates global optimization control instructions;

[0068] S3: The control commands are simulated, verified, and optimized using the digital twin module 203, and the optimized commands are sent to the execution layer 300;

[0069] S4: Control commands are executed through execution layer 300 to control the coordinated operation of energy dispatch subsystem 301 and production optimization subsystem 302;

[0070] S5: Key carbon data is stored on the blockchain through the blockchain evidence layer 400, and traceable carbon footprint information is generated for the product.

[0071] Furthermore, in step S2, the specific process of generating global optimization control instructions includes:

[0072] During periods of low external electricity prices and peak photovoltaic power generation within the plant, instructions are generated to control the energy dispatch subsystem 301 to prioritize charging the energy storage system and to control the production optimization subsystem 302 to schedule high-energy-consuming processes during this time period.

[0073] When the calculated real-time carbon footprint approaches a preset threshold, an instruction is generated to control the production optimization subsystem 302 to temporarily reduce non-critical capacity or switch to a low-carbon raw material formula.

[0074] Working principle of the invention:

[0075] Phase 1: Real-time perception of the carbon situation across all dimensions (data acquisition layer 100)

[0076] The system uses data acquisition layer 100 to perform comprehensive and seamless dynamic monitoring of the factory's carbon flow.

[0077] Indirect carbon emission monitoring: The energy carbon flow monitoring module 101 collects the consumption of purchased electricity, gas and steam in real time through IoT instruments such as smart meters and gas meters, and calls the embedded carbon emission factor database to calculate the range of carbon emissions in real time using the formula carbon emission amount = activity data × emission factor.

[0078] Direct carbon emission monitoring: The process carbon flow monitoring module 102 uses a non-dispersive infrared (NDIR) gas analyzer and flow meter at key process emission points (such as boilers and reactors) to achieve minute-level accurate measurement of direct carbon emissions within a certain range by using the formula emission amount = concentration × flow rate × operating time.

[0079] Implicit Carbon Flow Monitoring: The Resource Carbon Flow Monitoring Module 103 connects to the enterprise's ERP / MES system to acquire material and waste data, and combines it with the built-in Life Cycle Assessment (LCA) database to calculate the range of three carbon flows implicit in raw material inputs and waste outputs.

[0080] Phase Two: Carbon Footprint Accounting and Intelligent Decision-Making (Central Decision-Making Level 200)

[0081] Dynamic accounting: The carbon footprint real-time accounting engine 201 gathers data from three parties and dynamically calculates the real-time carbon footprint of the entire plant, workshops and unit products according to international standards (such as GHG Protocol), and displays it in layers according to scope one, two and three.

[0082] The optimization control module 202 employs a deep reinforcement learning (DRL) algorithm to model the factory as a collaborative environment. Using minimizing the "carbon footprint per unit product" and "overall energy cost" as its reward function, it autonomously generates optimal control commands through continuous learning. For example, it autonomously decides that during periods of "peak solar power consumption + low electricity prices," a combined strategy of "high-energy-consuming production + energy storage charging" should be implemented.

[0083] Phase 3: Virtual Verification and Pre-Drilling of Instructions - Digital Twin Module 203

[0084] Before instructions are issued to the physical factory, digital twin module 203 initiates a safety rehearsal:

[0085] It operates in sync with the factory and receives the same real-time data.

[0086] The instructions generated by the optimized control module 202 are simulated in the virtual model at the millisecond level.

[0087] Verification and Optimization: Simulation predicts the carbon, energy, and efficiency results after the instruction is executed, and checks for potential risks (such as exceeding power limits). If the prediction results are unsatisfactory or risks exist, the instruction is fed back to the decision-making level for correction, achieving "pre-optimization" of the instruction and ensuring the safety and efficiency of the issued instruction.

[0088] Phase 4: Multi-system collaborative and precise execution (Execution layer 300)

[0089] The validated optimization instructions were sent to execution layer 300:

[0090] Energy Dispatch: The energy dispatch subsystem 301 strictly executes instructions. For example, during the midday peak of photovoltaic power generation, the microgrid is instructed to prioritize the use of green electricity and store excess power in the energy storage system; during off-peak hours at night, the system is instructed to purchase electricity from the grid to supplement energy for production and energy storage.

[0091] Production Optimization: The production optimization subsystem 302 flexibly adjusts operations. Based on carbon constraints, the production optimization subsystem 302 precisely schedules the operating periods of high-energy-consuming equipment such as kilns and air compressors to the low-carbon power range; when the real-time carbon footprint approaches the warning threshold, it automatically suspends workshop auxiliary facilities or switches to low-carbon raw material formulas, realizing online carbon emission control.

[0092] Phase 5: Trusted Evidence Storage and Closed-Loop Feedback

[0093] Blockchain Evidence Storage: The blockchain evidence storage layer 400 generates a unique and tamper-proof digital fingerprint (hash value) for key carbon data in each accounting cycle (e.g., 1 hour) and puts it on the chain, generating a traceable "carbon footprint ID card" for the product and laying the foundation for data credibility.

[0094] Closed-loop feedback: The results of actions taken by the execution layer 300 are immediately perceived by the data acquisition layer 100 and fed back to the central decision-making layer 200 as new status data. Based on the feedback results, the system learns and continuously iterates to optimize the strategy, forming a real-time adaptive closed loop of "monitoring-calculation-optimization-verification-execution-evidence storage-re-monitoring", driving the factory to continuously move closer to the zero-carbon goal.

[0095] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A zero-carbon factory control system, characterized in that, include: A data acquisition layer (100) is used to collect carbon-related data from the factory in all dimensions. The data acquisition layer (100) includes: The energy carbon flow monitoring module (101) is used to monitor the consumption of external energy and its corresponding carbon emission factors in real time. Process carbon flow monitoring module (102) is used to monitor the direct carbon emissions of key processes; Resource carbon flow monitoring module (103) is used to monitor the carbon flow implied in the flow of materials and waste; A central decision-making layer (200) is communicatively connected to a data acquisition layer (100), the central decision-making layer (200) comprising: The real-time carbon footprint calculation engine (201) is used to dynamically calculate the real-time carbon footprint of the entire plant and unit products based on the collected data. The optimization control module (202) is used to generate control commands with carbon emissions, energy costs and production efficiency as common optimization objectives; An execution layer (300), which is communicatively connected to the central decision-making layer (200), is used to execute the control commands. The execution layer (300) includes: The energy dispatch subsystem (301) is used to coordinate and control the operation of distributed energy sources, energy storage systems and microgrids within the plant; The production optimization subsystem (302) is used to adjust the production plan and process parameters according to the control instructions.

2. The zero-carbon factory control system according to claim 1, characterized in that: The central decision-making layer (200) also includes a digital twin module (203), which constructs a virtual model corresponding to the factory. The virtual model receives real-time data from the data acquisition layer (100) for synchronization and performs simulation verification and effect preview of the control commands generated by the optimization control module (202).

3. A zero-carbon factory control system according to claim 1, characterized in that: It also includes a blockchain evidence storage layer (400), which is connected to the central decision-making layer (200). The blockchain evidence storage layer (400) stores key carbon data and their hash values ​​generated by the real-time carbon footprint accounting engine (201) on the blockchain.

4. A zero-carbon factory control system according to claim 1, characterized in that: The process carbon flow monitoring module (102) includes a direct carbon emission quantification unit, which integrates an infrared gas analyzer and a flow meter.

5. A method for a zero-carbon factory control system as described in claims 1-4, characterized in that, Includes the following steps: S1: Real-time data acquisition of energy, process and resource carbon flow data of the plant is obtained through the data acquisition layer (100); S2: The central decision-making layer (200) dynamically calculates the carbon footprint based on the collected data, and the optimization control module (202) generates global optimization control instructions; S3: The control instructions are simulated, verified and optimized by the digital twin module (203), and the optimized instructions are sent to the execution layer (300); S4: The control command is executed through the execution layer (300) to control the coordinated operation of the energy dispatch subsystem (301) and the production optimization subsystem (302); S5: Key carbon data is stored on the blockchain through the blockchain evidence storage layer (400), and traceable carbon footprint information is generated for the product.

6. The zero-carbon factory control method according to claim 5, characterized in that: In step S2, the specific process of generating global optimization control instructions includes: During periods of low external electricity prices and peak photovoltaic power generation within the plant, instructions are generated to control the energy dispatch subsystem (301) to prioritize charging the energy storage system and to control the production optimization subsystem (302) to schedule high-energy-consuming processes during this time period. When the calculated real-time carbon footprint approaches a preset threshold, an instruction is generated to control the production optimization subsystem (302) to temporarily reduce non-critical capacity or switch to a low-carbon raw material formula.

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