Industrial flue gas pollution reduction and carbon reduction cooperative control method and system based on multi-objective optimization

Abstract

The invention belongs to the technical field of pollution reduction and carbon reduction, and discloses an industrial flue gas pollution reduction and carbon reduction cooperative control method and system based on multi-objective optimization. The method comprises the following steps: acquiring standard data of pollution reduction and carbon reduction of industrial flue gas, and constructing a pollution-carbon-economic target data set in combination with data information of emission reduction cost and equipment life; performing real-time dynamic monitoring on industrial load parameter fluctuation, a denitration control system, a waste heat recovery system and gas thermal failure risks in real-time equipment in industrial production, and performing early warning on an abnormal state of dynamic monitoring; and comparing a monitored normal industrial production result with the constructed pollution-carbon-economy target data set to obtain an optimal threshold value of pollution reduction and carbon reduction of the industrial flue gas and economy matching. The method can synchronously reduce the cost by 20% and improve the emission reduction efficiency through a dynamic multi-target algorithm.

Description

Technical Field

[0001] This invention belongs to the field of pollution reduction and carbon reduction technology, and particularly relates to a method and system for coordinated control of industrial flue gas pollution reduction and carbon reduction based on multi-objective optimization. Background Technology

[0002] The exhaust gases from the combustion of fossil fuels such as coal are complex, containing various pollutants including NOx, SO2, and VOCs, as well as large amounts of carbon dioxide and water vapor. The combustion of fossil fuels inevitably increases the complexity and cost of industrial waste gas emission control technologies.

[0003] Existing methods for treating multiple pollutants and carbon dioxide include a carbon dioxide reduction device and method for outer space, as disclosed in patent application CN113278997A. The carbon dioxide reduction device includes: a carbon dioxide purification device for absorbing and treating air containing carbon dioxide in the environment; a power supply device; a liquid circulation and gas drying device; and reactors connected to the carbon dioxide purification device, the power supply device, and the liquid circulation and drying device respectively. The power supply device powers the reactor. The reactor receives the purified solution produced by the carbon dioxide purification device and the alkaline solution output by the liquid circulation and gas drying device, and performs a reduction reaction. Existing methods for treating multiple pollutants and carbon dioxide suffer from long processes and low efficiency. Traditional carbon capture processes require the regeneration, purification, and compression of carbon dioxide from the capture liquid before it can be used for carbon dioxide conversion, which consumes more energy than this process and does not involve co-treatment with nitrogen oxides in flue gas, resulting in a long process for carbon reduction and pollution control.

[0004] The prior art discloses a short-process synergistic treatment device and method for reducing carbon dioxide and pollution in flue gas (publication number CN119075673A, publication date 20241206). The device includes a flue gas oxidation absorption module and a carbon pollution absorbent electrolytic conversion module. The flue gas oxidation absorption module catalytically oxidizes sulfides and nitrogen oxides in the flue gas to form sulfur dioxide and nitrogen dioxide through an oxidation catalyst. Sulfur dioxide is absorbed by an absorbent. Nitrogen dioxide and carbon dioxide in the flue gas are converted into a carbon pollution absorbent containing carbonate ions, bicarbonate ions, nitrite ions, and nitrate ions through a potassium hydroxide absorbent. Finally, the carbon pollution absorbent electrolytic conversion module electrolyzes the carbon pollution absorbent to form ethylene, methylamine, and urea. This method eliminates energy-intensive processes such as collection liquid regeneration and gas purification and compression, shortens the carbon dioxide and multi-pollutant treatment process in the flue gas, reduces energy consumption, and improves treatment efficiency.

[0005] Furthermore, existing technologies differ on the "multi-objective priority": in environmental protection, the pro-industry camp insists on prioritizing both carbon and pollution control, while in industrial economics, a balance between economic efficiency and environmental impact is more important.

[0006] Existing technologies include the multi-objective optimization paper "Multi-objective optimization in industrial practice of energy systems" (IEEE, 2023) from North China Electric Power University, "Intelligent monitoring of supercapacitors" (2024) from the Chinese Academy of Sciences, and the Siemens Industry 4.0 case study ADRC on carbon pollution synergistic optimization in the denitrification system of a steel plant.

[0007] However, existing technologies have the following drawbacks: Traditional methods prioritize a single objective (such as denitrification efficiency), leading to increased carbon emissions or energy consumption, and failing to balance denitrification efficiency and energy consumption. When industrial load fluctuates, fixed parameter control results in efficiency decline; traditional PID control has weak disturbance rejection, with an error rate exceeding 15%. In denitrification control systems, reliance on PID control leads to response lag: denitrification efficiency fluctuations >10% with temperature / fuel changes; increased ammonia slip rate causes secondary pollution. Dynamic disturbance rejection is also poor. Waste heat recovery (ORC) systems have the following drawbacks: working fluid selection and parameter fixation result in thermal efficiency of only 8-12%, lower than the theoretical value of 15%; energy storage coordination deficiencies include: lack of real-time monitoring, internal gas pressure runaway easily leading to thermal failure risk; no early warning mechanism, unable to address factors affecting production such as declining power quality directly causing production interruptions, and low fault identification rate. Summary of the Invention

[0008] To overcome the problems existing in related technologies, the embodiments disclosed in this invention provide a method and system for coordinated control of industrial flue gas pollution reduction and carbon reduction based on multi-objective optimization.

[0009] The technical solution is as follows: a multi-objective optimization-based synergistic control method for industrial flue gas pollution reduction and carbon reduction, comprising the following steps:

[0010] S1 utilizes cloud networks based on carbon accounting databases to obtain standard data on industrial flue gas pollution reduction and carbon reduction. Combined with collected emission reduction cost and equipment lifespan data, a pollution-carbon-economic target data set is constructed at the control terminal; among which, emission reduction cost and equipment lifespan data are economic target data.

[0011] S2 provides real-time dynamic monitoring of industrial load parameter fluctuations, denitrification control systems, waste heat recovery systems, and the risk of thermal failure of internal gas in equipment during industrial production, and provides early warnings for abnormal states in dynamic monitoring.

[0012] S3 compares the monitored normal industrial production results with the constructed pollution-carbon-economic target data set to obtain the optimal threshold for matching industrial flue gas pollution reduction and carbon reduction with economic benefits.

[0013] In step S1, the pollution-carbon-economic target dataset is as follows:

[0014] Pollution-carbon-economic goal equilibrium based on length The data blocks, the industrial flue gas pollution reduction standard data sent by the cloud network are The standard data for reducing carbon emissions from industrial flue gas is: The emission reduction cost data sent by the cloud network is Device lifespan data sent by the cloud network , Number of times sent;

[0015] The constructed dataset of pollution-carbon-economic targets is as follows: .

[0016] In step S2, the industrial load parameters include: rated power of industrial production equipment, temperature value, and fuel consumption value; the improved load parameter optimization method is used to dynamically monitor the fluctuations of industrial load parameters in real time, and the specific steps are as follows:

[0017] S201, for different production stages included in industrial production, and for any one of the rated power, temperature value, or fuel consumption value of industrial production equipment, the first... The diversity branch signal is about the first The adjustment weight for each normal fluctuation range value is: , No. The diversity branch signal is about the first The adjustment weight for each abnormal fluctuation range value is: ,Depend on Each diversity branch and Calculate the first Total adjustment weight for each normal or abnormal fluctuation range value and for:

[0018]

[0019] S202, pp. The signal from the diversity branch is obtained after equalization. The optimization weights for each fluctuation range value are: Based on the definition of the log-likelihood ratio for log-likelihood information, we obtain:

[0020]

[0021] S203, the log-likelihood ratio optimization weights of each diversity branch are combined to obtain the total log-likelihood ratio of the entire production stage of industrial production. The total tuning threshold for each fluctuation range value :

[0022]

[0023] S204. Decode the merging result. Input the merged and optimized weights into the weight output decoder, which is any one of the equalization weights among the rated power, temperature value, or fuel consumption value of the industrial production equipment. Use minimum sum product decoding to complete the iterative decoding and obtain the decoding output result. Determine the overall iteration count and the decoding optimization sum. If the overall iteration count reaches the maximum value or the decoding optimization sum is 0, output the decoding result and end the iteration; otherwise, feed the decoding result back to each equalizer as the decoding output weight information and continue the overall iteration.

[0024] In step S204, iterative decoding is performed using minimum sum-product decoding to obtain the decoding output result, including:

[0025] (1) By variable node Passed to the tuning node Information is recorded as variable information variable node The rated power, temperature, or fuel consumption value of the industrial production equipment is any one of the following: A variable node with a fluctuation range value;

[0026] By tuning nodes Passed to variable node The information is recorded as optimization information. variable node The prior information is The information update process for obtaining the minimum sum product decoding is as follows:

[0027]

[0028]

[0029]

[0030] In the formula, Given information The variance of the Gaussian channel is... The initial value is 0. For variable nodes A set of connected tuning nodes. To optimize nodes A set of connected variable nodes. To remove External and variable nodes A set of connected tuning nodes. To remove External and optimization nodes A set of connected variable nodes; To optimize nodes Passed to variable node The optimization information, To optimize nodes Passed to variable node variable information

[0031] (2) The tuning matrix for decoding is For minimum sum product decoding, each iterative decoding process uses... Decode the decision criteria to obtain the decoding result. Simultaneously calculate the syndrome If the value of the syndrome is 0, output the decoding result and the tuning result. Otherwise, proceed to the next iteration of decoding until the maximum number of iterations is reached; where, For variable nodes Decoding, For variable nodes variable information, To optimize the matrix, For optimizing the decoding results, This is the first tuning variable node. For the second variable node, To optimize nodes A set of connected variable nodes. For variable nodes A set of connected tuning nodes. The tuning information passed from the first tuning variable node to the second variable node.

[0032] In step S2, real-time dynamic monitoring is performed on industrial load parameter fluctuations, the denitrification control system, the waste heat recovery system, and the risk of internal gas thermal failure in real-time equipment. This includes: real-time dynamic monitoring of ammonia slip rate in the denitrification control system using an improved gas dynamic disturbance rejection method.

[0033] Utilizing novel nonlinear disturbance rejection The function improves the gas dynamic disturbance rejection controller, resulting in a series controller for ammonia slip rate of the linear integral denitrification control system based on proportional-derivative control.

[0034] in, The function is as follows:

[0035]

[0036] In the formula, For error signals, ; It is a nonlinear factor. For error, This is the error offset coefficient.

[0037] Furthermore, utilizing novel nonlinear disturbance rejection methods Functional improvements to gas dynamic disturbance rejection controllers include:

[0038] (1) Constructing a novel smooth function-based nonlinear disturbance rejection mechanism Differential tracker for functions:

[0039]

[0040] In the formula, For differential tracking functions, To optimize the function, Let be the nodal nonlinear disturbance immunity value of the first variable. For the nonlinear disturbance rejection speed of the node, The nodal nonlinear disturbance immunity value of the second variable. To determine the controller's response speed, The initial sampling time, Let be the nonlinear disturbance rejection value of the node with the first variable in the next sampling interval. This represents the nonlinear disturbance immunity value of the node representing the second variable at the next sampling interval. The sampling interval time. Let be the nodal nonlinear disturbance rejection value of the u-th variable;

[0041] ; This represents the optimized synthesis function, used to quickly track the target value input for ammonia slip rate. The formula can be expressed as:

[0042]

[0043] In the formula, The initial sampling time is the second derivative of the response speed. , It is an integer. The initial sampling time for the variable node. This represents the position of the node representing the first variable after the initial sampling time. These are the sampling times for the first variable node and the second variable node, respectively. These are the node sampling times for the first variable and the node sampling times for the second variable, respectively. This is the tracking ammonia escape rate function based on the second derivative of the initial sampling time constraint factor under velocity and response speed. This is a tracking ammonia escape rate function based on position and movement distance constraint factors. For the sampling time of the variable node, For the distance traveled, This is a function for tracking ammonia escape rate based on sampling time and motion distance constraint factors;

[0044] (2) Determine the set of nonlinear feedback control:

[0045]

[0046] In the formula, These are error signals 1-6 respectively. These are the differential tracking values ​​under states a, b, and c, respectively. This represents the differential tracking offset value in state 1. These represent the variable information passed from the first variable node to the first tuning node, the variable information passed from the first variable node to the second tuning node, the variable information passed from the second variable node to the first tuning node, the variable information passed from the second variable node to the second tuning node, the variable information passed from the third variable node to the first tuning node, the variable information passed from the third variable node to the second tuning node, the variable information passed from the fourth variable node to the first tuning node, the variable information passed from the fourth variable node to the second tuning node, and the variable information passed from the fifth variable node to the first tuning node. These are the 1st to 3rd tuning nodes, For novel nonlinear disturbance rejection, These are the first smoothing coefficient and the second smoothing coefficient, respectively. This is the initial error. As a compensation factor, This represents the superimposed output of the ammonia slip rate controlled by three cascaded nonlinear PDs. Indicates the proportional adjustment factor; This represents the differential adjustment factor.

[0047] Furthermore, the heat recovery efficiency of the waste heat recovery system is monitored in real time using the heat ratio method.

[0048] Y=M 余热回收 Heat in industrial production (M) 前 %

[0049] The pressure sensor is used to perform real-time dynamic monitoring of the risk P of thermal failure of gas inside the equipment.

[0050] In step S3, the optimal threshold for balancing industrial flue gas pollution reduction and carbon reduction with economic considerations is obtained, including:

[0051]

[0052] In the formula, This represents the weight set of industrial flue gas pollution reduction and carbon reduction versus economic benefits after real-time dynamic monitoring and optimization of industrial load parameter fluctuations, denitrification control systems, waste heat recovery systems, and real-time equipment internal gas thermal failure risks in industrial production. This provides standard data for reducing pollution and carbon emissions from industrial flue gas, as well as data on emission reduction costs and equipment lifespan, creating an ideal combination of industrial flue gas pollution reduction, carbon reduction, and economic benefits.

[0053] Another objective of this invention is to provide a multi-objective optimization-based coordinated control system for industrial flue gas pollution reduction and carbon reduction. This system implements the aforementioned multi-objective optimization-based coordinated control method for industrial flue gas pollution reduction and carbon reduction. The system includes:

[0054] The data acquisition module is used to perform real-time dynamic monitoring of industrial load parameter fluctuations, denitrification control systems, waste heat recovery systems, and the risk of thermal failure of internal gas in equipment during industrial production, and to provide early warnings for abnormal states in the dynamic monitoring.

[0055] The dynamic monitoring module is used to monitor industrial load parameter fluctuations, denitrification control systems, waste heat recovery systems, and the risk of thermal failure of internal gas in equipment in real time during industrial production, and to provide early warnings for abnormal states of dynamic monitoring.

[0056] The optimal threshold acquisition module is used to compare the monitored normal industrial production results with the constructed pollution-carbon-economic target data set to obtain the optimal threshold for matching industrial flue gas pollution reduction and carbon reduction with economic benefits.

[0057] Furthermore, the system is mounted on a computer-readable storage medium storing a computer program. When executed by a processor, the computer program can realize the functions of the aforementioned multi-objective optimization-based industrial flue gas pollution reduction and carbon reduction collaborative control system.

[0058] Combining all the above technical solutions, the beneficial effects of this invention are as follows: This invention solves the problem of traditional control separating pollution-carbon-economic objectives, avoiding the problems of increased emission reduction costs and reduced equipment lifespan; the active disturbance rejection control (ADRC) of this invention solves dynamic disturbance rejection, reducing the error rate to <5%; the supercapacitor of this invention can be monitored in real time, reducing the risk of voltage collapse, and embedding thermodynamic characteristic early warning; the waste heat recovery system of this invention optimizes the working fluid and adapts parameters through ORC, improving thermal efficiency by 50% and reducing the recovery period to 3 years; this invention can simultaneously reduce costs by 20% and improve emission reduction efficiency through a dynamic multi-objective algorithm. Attached Figure Description

[0059] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure;

[0060] Figure 1 This is a flowchart of the industrial flue gas pollution reduction and carbon reduction synergistic control method based on multi-objective optimization provided in the embodiments of the present invention. Detailed Implementation

[0061] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0062] Example 1, as Figure 1 As shown, the industrial flue gas pollution reduction and carbon reduction synergistic control method based on multi-objective optimization includes:

[0063] S1 utilizes cloud networks based on carbon accounting databases to obtain standard data on industrial flue gas pollution reduction and carbon reduction. Combined with collected emission reduction cost and equipment lifespan data, a pollution-carbon-economic target data set is constructed at the control terminal; among which, emission reduction cost and equipment lifespan data are economic target data.

[0064] S2 provides real-time dynamic monitoring of industrial load parameter fluctuations, denitrification control systems, waste heat recovery systems, and the risk of thermal failure of internal gas in equipment during industrial production, and provides early warnings for abnormal states in dynamic monitoring.

[0065] S3 compares the monitored normal industrial production results with the constructed pollution-carbon-economic target data set to obtain the optimal threshold for matching industrial flue gas pollution reduction and carbon reduction with economic benefits.

[0066] For example, in step S1, the pollution-carbon-economic target dataset is:

[0067] Pollution-carbon-economic goal equilibrium based on length The data blocks, the industrial flue gas pollution reduction standard data sent by the cloud network are The standard data for reducing carbon emissions from industrial flue gas is: The emission reduction cost data sent by the cloud network is Device lifespan data sent by the cloud network , Number of times sent;

[0068] The constructed dataset of pollution-carbon-economic targets is as follows: .

[0069] For example, in step S2, the industrial load parameters include: rated power of industrial production equipment, temperature value, and fuel consumption value;

[0070] Real-time dynamic monitoring of industrial load parameter fluctuations using improved load parameter optimization methods includes:

[0071] S201, for different production stages included in industrial production, and for any one of the rated power, temperature value, or fuel consumption value of industrial production equipment, the first... The diversity branch signal is about the first The adjustment weight for each normal fluctuation range value is: , No. The diversity branch signal is about the first The adjustment weight for each abnormal fluctuation range value is: ,Depend on Each diversity branch and Calculate the first Total adjustment weight for each normal or abnormal fluctuation range value and for:

[0072]

[0073] S202, pp. The signal from the diversity branch is obtained after equalization. The optimization weights for each fluctuation range value are: Based on the definition of the log-likelihood ratio for log-likelihood information, we obtain:

[0074]

[0075] S203, the log-likelihood ratio optimization weights of each diversity branch are combined to obtain the total log-likelihood ratio of the entire production stage of industrial production. The total tuning threshold for each fluctuation range value :

[0076]

[0077] S204. Decode the merging result. Input the merged and optimized weights into the weight output decoder, which is any one of the equalization weights among the rated power, temperature value, or fuel consumption value of the industrial production equipment. Use minimum sum product decoding to complete the iterative decoding and obtain the decoding output result. Determine the overall iteration count and the decoding optimization sum. If the overall iteration count reaches the maximum value or the decoding optimization sum is 0, output the decoding result and end the iteration; otherwise, feed the decoding result back to each equalizer as the decoding output weight information and continue the overall iteration.

[0078] For example, in S204, iterative decoding is performed using minimum sum-product decoding, and the resulting decoded output includes:

[0079] (1) By variable node Passed to the tuning node Information is recorded as variable information variable node The rated power, temperature, or fuel consumption value of the industrial production equipment is any one of the following: A variable node with a fluctuation range value;

[0080] By tuning nodes Passed to variable node The information is recorded as optimization information. variable node The prior information is The information update process for obtaining the minimum sum product decoding is as follows:

[0081]

[0082]

[0083]

[0084] In the formula, Given information The variance of the Gaussian channel is... The initial value is 0. For variable nodes A set of connected tuning nodes. To optimize nodes A set of connected variable nodes. To remove External and variable nodes A set of connected tuning nodes. To remove External and optimization nodes A set of connected variable nodes; To optimize nodes Passed to variable node The optimization information, To optimize nodes Passed to variable node variable information

[0085] (2) The tuning matrix for decoding is For minimum sum product decoding, each iterative decoding process uses... Decode the decision criteria to obtain the decoding result. Simultaneously calculate the syndrome If the value of the syndrome is 0, output the decoding result and the tuning result. Otherwise, proceed to the next iteration of decoding until the maximum number of iterations is reached; where, For variable nodes Decoding, For variable nodes variable information, To optimize the matrix, For optimizing the decoding results, This is the first tuning variable node. For the second variable node, To optimize nodes A set of connected variable nodes. For variable nodes A set of connected tuning nodes. The tuning information passed from the first tuning variable node to the second variable node.

[0086] A modified gas dynamic disturbance rejection method is used for real-time dynamic monitoring of ammonia slip rate in a denitrification control system; this includes: [The modified gas dynamic disturbance rejection method is used for real-time dynamic monitoring of ammonia slip rate in a denitrification control system.]

[0087] Utilizing novel nonlinear disturbance rejection The function improves the gas dynamic disturbance rejection controller, resulting in a series controller for ammonia slip rate of the linear integral denitrification control system based on proportional-derivative control.

[0088] in, The function is as follows:

[0089]

[0090] In the formula, For error signals, ; It is a nonlinear factor. For error, This is the error offset coefficient.

[0091] The improved active disturbance rejection controller utilizing novel nonlinear functions also includes:

[0092] (1) Constructing a novel smooth function-based nonlinear disturbance rejection mechanism Differential tracker for functions:

[0093]

[0094] In the formula, For differential tracking functions, To optimize the function, Let be the nodal nonlinear disturbance immunity value of the first variable. For the nonlinear disturbance rejection speed of the node, The nodal nonlinear disturbance immunity value of the second variable. To determine the controller's response speed, The initial sampling time, Let be the nonlinear disturbance rejection value of the node with the first variable in the next sampling interval. This represents the nonlinear disturbance immunity value of the node representing the second variable at the next sampling interval. The sampling interval time. Let be the nodal nonlinear disturbance rejection value of the u-th variable;

[0095] ; This represents the optimized synthesis function, used to quickly track the target value input for ammonia slip rate. The formula can be expressed as:

[0096]

[0097] In the formula, The initial sampling time is the second derivative of the response speed. , It is an integer. The initial sampling time for the variable node. This represents the position of the node representing the first variable after the initial sampling time. These are the sampling times for the first variable node and the second variable node, respectively. These are the node sampling times for the first variable and the node sampling times for the second variable, respectively. This is the tracking ammonia escape rate function based on the second derivative of the initial sampling time constraint factor under velocity and response speed. This is a tracking ammonia escape rate function based on position and movement distance constraint factors. For the sampling time of the variable node, For the distance traveled, This is a function for tracking ammonia escape rate based on sampling time and motion distance constraint factors;

[0098] (2) Determine the set of nonlinear feedback control:

[0099]

[0100] In the formula, These are error signals 1-6 respectively. These are the differential tracking values ​​under states a, b, and c, respectively. This represents the differential tracking offset value in state 1. These represent the variable information passed from the first variable node to the first tuning node, the variable information passed from the first variable node to the second tuning node, the variable information passed from the second variable node to the first tuning node, the variable information passed from the second variable node to the second tuning node, the variable information passed from the third variable node to the first tuning node, the variable information passed from the third variable node to the second tuning node, the variable information passed from the fourth variable node to the first tuning node, the variable information passed from the fourth variable node to the second tuning node, and the variable information passed from the fifth variable node to the first tuning node. These are the 1st to 3rd tuning nodes, For novel nonlinear disturbance rejection, These are the first smoothing coefficient and the second smoothing coefficient, respectively. This is the initial error. As a compensation factor, This represents the superimposed output of the ammonia slip rate controlled by three cascaded nonlinear PDs. Indicates the proportional adjustment factor; This represents the differential adjustment factor.

[0101] The heat recovery efficiency of the waste heat recovery system is monitored in real time using the heat ratio method.

[0102] Y=M 余热回收 Heat in industrial production (M) 前 %

[0103] The pressure sensor is used to perform real-time dynamic monitoring of the risk P of thermal failure of gas inside the equipment.

[0104] Example 1: Step S3 compares the monitored normal industrial production results with the constructed pollution-carbon-economic target data set to obtain the optimal threshold for matching industrial flue gas pollution and carbon reduction with economic benefits, including:

[0105]

[0106] In the formula, This represents the weight set of industrial flue gas pollution reduction and carbon reduction versus economic benefits after real-time dynamic monitoring and optimization of industrial load parameter fluctuations, denitrification control systems, waste heat recovery systems, and real-time equipment internal gas thermal failure risks in industrial production. This provides standard data for reducing pollution and carbon emissions from industrial flue gas, as well as data on emission reduction costs and equipment lifespan, creating an ideal combination of industrial flue gas pollution reduction, carbon reduction, and economic benefits.

[0107] Example 2: The industrial flue gas pollution reduction and carbon reduction collaborative control system based on multi-objective optimization provided by the present invention includes:

[0108] The data acquisition module is used to perform real-time dynamic monitoring of industrial load parameter fluctuations, denitrification control systems, waste heat recovery systems, and the risk of thermal failure of internal gas in equipment during industrial production, and to provide early warnings for abnormal states in the dynamic monitoring.

[0109] The dynamic monitoring module is used to monitor industrial load parameter fluctuations, denitrification control systems, waste heat recovery systems, and the risk of thermal failure of internal gas in equipment in real time during industrial production, and to provide early warnings for abnormal states of dynamic monitoring.

[0110] The optimal threshold acquisition module is used to compare the monitored normal industrial production results with the constructed pollution-carbon-economic target data set to obtain the optimal threshold for matching industrial flue gas pollution reduction and carbon reduction with economic benefits.

[0111] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments.

[0112] This invention also provides a computer device comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0113] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps described in the various method embodiments above.

[0114] This invention also provides an information data processing terminal, which, when executed on an electronic device, provides a user input interface to implement the steps described in the above method embodiments. The information data processing terminal is not limited to mobile phones, computers, or switches.

[0115] This invention also provides a server that, when executed on an electronic device, provides a user input interface to implement the steps described in the above method embodiments.

[0116] This invention provides a computer program product that, when run on an electronic device, enables the electronic device to implement the steps described in the various method embodiments above.

[0117] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0118] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for coordinated control of industrial flue gas pollution reduction and carbon reduction based on multi-objective optimization, characterized in that, The method includes the following steps: S1 utilizes cloud networks based on carbon accounting databases to obtain standard data on industrial flue gas pollution reduction and carbon reduction. Combined with collected emission reduction cost and equipment lifespan data, a pollution-carbon-economic target data set is constructed at the control terminal; among which, emission reduction cost and equipment lifespan data are economic target data. S2 provides real-time dynamic monitoring of industrial load parameter fluctuations, denitrification control systems, waste heat recovery systems, and the risk of thermal failure of internal gas in equipment during industrial production, and provides early warnings for abnormal states in dynamic monitoring. S3 compares the monitored normal industrial production results with the constructed pollution-carbon-economic target data set to obtain the optimal threshold for matching industrial flue gas pollution reduction and carbon reduction with economic benefits.

2. The method for coordinated control of industrial flue gas pollution reduction and carbon reduction based on multi-objective optimization according to claim 1, characterized in that, In step S1, the pollution-carbon-economic target dataset is as follows: Pollution-carbon-economic goal equilibrium based on length The data blocks, the industrial flue gas pollution reduction standard data sent by the cloud network are The standard data for reducing carbon emissions from industrial flue gas is: The emission reduction cost data sent by the cloud network is Device lifespan data sent by the cloud network , Number of times sent; The constructed dataset of pollution-carbon-economic targets is as follows: .

3. The method for coordinated control of industrial flue gas pollution reduction and carbon reduction based on multi-objective optimization according to claim 1, characterized in that, In step S2, the industrial load parameters include: rated power of industrial production equipment, temperature value, and fuel consumption value; the improved load parameter optimization method is used to dynamically monitor the fluctuations of industrial load parameters in real time, and the specific steps are as follows: S201, for different production stages included in industrial production, and for any one of the rated power, temperature value, or fuel consumption value of industrial production equipment, the first... The diversity branch signal is about the first The adjustment weight for each normal fluctuation range value is: , No. The diversity branch signal is about the first The adjustment weight for each abnormal fluctuation range value is: ,Depend on Each diversity branch and Calculate the first Total adjustment weight for each normal or abnormal fluctuation range value and for: S202, pp. The signal from the diversity branch is obtained after equalization. The optimization weights for each fluctuation range value are: Based on the definition of the log-likelihood ratio for log-likelihood information, we obtain: S203, the log-likelihood ratio optimization weights of each diversity branch are combined to obtain the total log-likelihood ratio of the entire production stage of industrial production. The total tuning threshold for each fluctuation range value : S204. Decode the merging result. Input the merged and optimized weights into the weight output decoder, which is any one of the equalization weights among the rated power, temperature value, or fuel consumption value of the industrial production equipment. Use minimum sum product decoding to complete the iterative decoding and obtain the decoding output result. Determine the overall iteration count and the decoding optimization sum. If the overall iteration count reaches the maximum value or the decoding optimization sum is 0, output the decoding result and end the iteration; otherwise, feed the decoding result back to each equalizer as the decoding output weight information and continue the overall iteration.

4. The method for coordinated control of industrial flue gas pollution reduction and carbon reduction based on multi-objective optimization according to claim 3, characterized in that, In step S204, iterative decoding is performed using minimum sum-product decoding to obtain the decoding output result, including: (1) By variable node Passed to the tuning node Information is recorded as variable information variable node The rated power, temperature, or fuel consumption value of the industrial production equipment is any one of the following: A variable node with a fluctuation range value; By tuning nodes Passed to variable node The information is recorded as optimization information. variable node The prior information is The information update process for obtaining the minimum sum product decoding is as follows: In the formula, Given information The variance of the Gaussian channel is... The initial value is 0. For variable nodes A set of connected tuning nodes. To optimize nodes A set of connected variable nodes. To remove External and variable nodes A set of connected tuning nodes. To remove External and optimization nodes A set of connected variable nodes; To optimize nodes Passed to variable node The optimization information To optimize nodes Passed to variable node variable information (2) The tuning matrix for decoding is For minimum sum product decoding, each iterative decoding process uses... Decode the decision criteria to obtain the decoding result. Simultaneously calculate the syndrome If the value of the syndrome is 0, output the decoding result and the tuning result. Otherwise, proceed to the next iteration of decoding until the maximum number of iterations is reached; where, For variable nodes Decoding, For variable nodes variable information, To optimize the matrix, For optimizing the decoding results, This is the first tuning variable node. For the second variable node, To optimize nodes A set of connected variable nodes. For variable nodes A set of connected tuning nodes. The tuning information passed from the first tuning variable node to the second variable node.

5. The method for coordinated control of industrial flue gas pollution reduction and carbon reduction based on multi-objective optimization according to claim 2, characterized in that, In step S2, real-time dynamic monitoring is performed on industrial load parameter fluctuations, the denitrification control system, the waste heat recovery system, and the risk of internal gas thermal failure in real-time equipment. This includes: real-time dynamic monitoring of ammonia slip rate in the denitrification control system using an improved gas dynamic disturbance rejection method. Utilizing novel nonlinear disturbance rejection The function improves the gas dynamic disturbance rejection controller, resulting in a series controller for ammonia slip rate of the linear integral denitrification control system based on proportional-derivative control. in, The function is as follows: In the formula, For error signals, ; It is a nonlinear factor. For error, This is the error offset coefficient.

6. The method for coordinated control of industrial flue gas pollution reduction and carbon reduction based on multi-objective optimization according to claim 5, characterized in that, Utilizing novel nonlinear disturbance rejection Functional improvements to gas dynamic disturbance rejection controllers include: (1) Constructing a novel smooth function-based nonlinear disturbance rejection mechanism Differential tracker for functions: In the formula, For differential tracking functions, To optimize the function, Let be the nodal nonlinear disturbance immunity value of the first variable. For the nonlinear disturbance rejection speed of the node, The nodal nonlinear disturbance immunity value of the second variable. To determine the controller's response speed, The initial sampling time, Let be the nonlinear disturbance rejection value of the node with the first variable in the next sampling interval. This represents the nonlinear disturbance immunity value of the node representing the second variable at the next sampling interval. The sampling interval time. Let be the nodal nonlinear disturbance rejection value of the u-th variable; ; This represents the optimized synthesis function, used to quickly track the target value input for ammonia slip rate. The formula is as follows: In the formula, The initial sampling time is the second derivative of the response speed. , It is an integer. The initial sampling time for the variable node. This represents the position of the node representing the first variable after the initial sampling time. These are the sampling times for the first variable node and the second variable node, respectively. These are the node sampling times for the first variable and the node sampling times for the second variable, respectively. This is the tracking ammonia escape rate function based on the second derivative of the initial sampling time constraint factor under velocity and response speed. This is a tracking ammonia escape rate function based on position and movement distance constraint factors. For the sampling time of the variable node, For the distance traveled, This is a function for tracking ammonia escape rate based on sampling time and motion distance constraint factors; (2) Determine the set of nonlinear feedback control: In the formula, These are error signals 1-6 respectively. These are the differential tracking values ​​under states a, b, and c, respectively. This represents the differential tracking offset value in state 1. These represent the variable information passed from the first variable node to the first tuning node, the variable information passed from the first variable node to the second tuning node, the variable information passed from the second variable node to the first tuning node, the variable information passed from the second variable node to the second tuning node, the variable information passed from the third variable node to the first tuning node, the variable information passed from the third variable node to the second tuning node, the variable information passed from the fourth variable node to the first tuning node, the variable information passed from the fourth variable node to the second tuning node, and the variable information passed from the fifth variable node to the first tuning node. These are the 1st to 3rd tuning nodes, For novel nonlinear disturbance rejection, These are the first smoothing coefficient and the second smoothing coefficient, respectively. This is the initial error. As a compensation factor, This represents the superimposed output of the ammonia slip rate controlled by three cascaded nonlinear PDs. Indicates the proportional adjustment factor; This represents the differential adjustment factor.

7. The method for coordinated control of industrial flue gas pollution reduction and carbon reduction based on multi-objective optimization according to claim 6, characterized in that, The heat recovery efficiency of the waste heat recovery system is monitored in real time using the heat ratio method. Y=M 余热回收 Heat in industrial production (M) 前 % The pressure sensor is used to perform real-time dynamic monitoring of the risk P of thermal failure of gas inside the equipment.

8. The method for coordinated control of industrial flue gas pollution reduction and carbon reduction based on multi-objective optimization according to claim 1, characterized in that, In step S3, the optimal threshold for balancing industrial flue gas pollution reduction and carbon reduction with economic considerations is obtained, including: In the formula, This represents the weight set of industrial flue gas pollution reduction and carbon reduction versus economic benefits after real-time dynamic monitoring and optimization of industrial load parameter fluctuations, denitrification control systems, waste heat recovery systems, and real-time equipment internal gas thermal failure risks in industrial production. This provides standard data for reducing pollution and carbon emissions from industrial flue gas, as well as data on emission reduction costs and equipment lifespan, creating an ideal combination of industrial flue gas pollution reduction, carbon reduction, and economic benefits.

9. A collaborative control system for industrial flue gas pollution reduction and carbon reduction based on multi-objective optimization, characterized in that, The system implements the multi-objective optimization-based synergistic control method for reducing pollution and carbon emissions from industrial flue gas as described in any one of claims 1-8, and the system includes: The data acquisition module is used to perform real-time dynamic monitoring of industrial load parameter fluctuations, denitrification control systems, waste heat recovery systems, and the risk of thermal failure of internal gas in equipment during industrial production, and to provide early warnings for abnormal states in the dynamic monitoring. The dynamic monitoring module is used to monitor industrial load parameter fluctuations, denitrification control systems, waste heat recovery systems, and the risk of thermal failure of internal gas in equipment in real time during industrial production, and to provide early warnings for abnormal states of dynamic monitoring. The optimal threshold acquisition module is used to compare the monitored normal industrial production results with the constructed pollution-carbon-economic target data set to obtain the optimal threshold for matching industrial flue gas pollution reduction and carbon reduction with economic benefits.

10. The industrial flue gas pollution reduction and carbon reduction synergistic control system based on multi-objective optimization according to claim 9, characterized in that, The system is mounted on a computer-readable storage medium that stores a computer program. When the computer program is executed by a processor, it can realize the functions of the aforementioned multi-objective optimization-based industrial flue gas pollution reduction and carbon reduction collaborative control system.

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