Gasification slag mixed biomass fuel blending combustion control method and system

Abstract

The invention discloses a gasification slag mixed biomass fuel blending combustion control method and system, and relates to the field of energy and environmental engineering. The method comprises the steps of collecting sensor data for data fusion, and preprocessing original data; according to the preprocessed data, an environment combustion synergy index is constructed, and the raw materials are classified; screening an optimal mixing ratio by utilizing dynamic game scores; temperature and flame stability parameters are collected in real time to construct a closed-loop feedback system, and intelligent adjustment of the combustion process is achieved; and a dynamic emission risk index is constructed, and closed-loop optimization control of pollutant emission is realized in combination with variable regression and a dynamic adjustment algorithm. Advanced technologies such as sensor data fusion, environment combustion synergy index, dynamic game scoring and feedback control are combined, on the premise that the combustion efficiency and stability are ensured, the blending combustion strategy of the gasified slag mixed biomass fuel can be optimized, and pollutant emission is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the field of energy and environmental engineering, and specifically to a method and system for controlling the co-firing of gasification slag mixed biomass fuel. Background Art

[0002] As a renewable energy source, the utilization of biomass energy not only helps to reduce the dependence on traditional fossil fuels, but also can improve the combustion efficiency and environmental protection performance through reasonable management and control. With the development of technology, how to efficiently and low-pollution co-fire gasification slag mixed biomass fuel has become a research hotspot in the field of combustion technology. Especially in the case of mixing multiple raw materials, there are many challenges in the stability of the combustion process, pollutant emission control, and economic optimization.

[0003] Currently, traditional combustion control methods mostly rely on fixed parameters set manually and inflexible operation modes, which may lead to unstable combustion processes, fuel waste, and excessive pollutant emissions. In addition, due to the diverse composition and properties of raw materials, how to dynamically adjust the co-firing ratio and control the combustion process for different raw materials and different combustion conditions is still a difficult problem to be solved. Summary of the Invention

[0004] Based on the above-mentioned disadvantages of the prior art, the purpose of the present invention is to provide a method and system for controlling the co-firing of gasification slag mixed biomass fuel to solve the above technical problems.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: A method for controlling the co-firing of gasification slag mixed biomass fuel, including: Initializing and calibrating the sensors, and collecting sensor data for data fusion to preprocess the original data; Constructing an environmental combustion coordination index based on the preprocessed data to conduct quality determination and intelligent classification of the raw materials; Constructing an optimal blending ratio model based on the classification results, and screening the optimal blending ratio using dynamic game scoring; Constructing a closed-loop feedback control system, and collecting temperature and flame stability parameters in real time and inputting them into the closed-loop feedback system to achieve intelligent regulation of the combustion process; Constructing a dynamic emission risk index, and combining variable regression and dynamic adjustment algorithms to achieve closed-loop optimal control of pollutant emissions.

[0006] The present invention is further configured to install multi-modal sensors to collect the physical and chemical parameters of the raw materials, upload the collected original data to the cloud for cleaning and denoising, and obtain a preprocessed data set by fusing the sensor data through the method of weighted average: , where is the preprocessed data set, is the number of sensors, is the sensor weight, is the sensor output; the preprocessed data set obtained by data fusion: , where is the mercury content of the raw material, is the chlorine content of the raw material, is the potassium content of the raw material, is the sodium content of the raw material, is the sulfur content of the raw material, is the ash fusion temperature of the raw material, is the moisture content of the raw material, is the volatile content of the raw material, is the fixed carbon content of the raw material, is the gross calorific value of the raw material.

[0007] The present invention is further configured as the calculation logic of the environmental combustion synergy index: , is the environmental combustion synergy index; classification rule: , where and are preset thresholds.

[0008] The present invention is further configured as the method comprising: constructing a candidate blending ratio for the generated preprocessed fuel; screening the candidate blending ratio according to the dynamic game score to obtain the optimal blending ratio; candidate blending ratio construction logic: , where is the environmental constraint, is the calorific value constraint, is the sulfur constraint.

[0009] The present invention is further configured as the environmental constraint calculation logic being: , where is the environmental combustion synergy index of the raw material, is the quality screening threshold; calorific value constraint calculation logic: , where is the calorific value of the raw material, is the minimum calorific value requirement, is the calorific value of the clean fuel; sulfur constraint calculation logic: , where is the sulfur content of the raw material, is the sulfur emission limit, is the sulfur content of the clean fuel.

[0010] The present invention is further configured as the dynamic game score calculation logic: , where is the dynamic game score, is the candidate blending ratio, is the total number of parameters, is the total number of non-compliant parameters, is the parameter actual value, is the safety threshold of the parameter, is the parameter weight, is the sensitivity, is the global penalty coefficient, is the penalty function, , where is the actual value obtained as the parameter changes with the blending ratio, is the reference value, is the penalty coefficient, is the penalty term; Optimal blending ratio calculation logic: , is the optimal blending ratio.

[0011] The present invention is further configured to monitor the combustion state in real time to construct a combustion state set: , where is the combustion temperature, is the flame stability; Fuel injection rate calculation logic: , where is the injection rate, is the predicted injection rate at the next moment, and are adjustment coefficients, is the set target temperature, is the set target flame stability; Fuel injection rate limit constraint: , is the maximum fuel injection rate allowed by the system, and the injection rate at the next moment is dynamically adjusted according to the current combustion state to ensure that the combustion process always remains within the set temperature and stability range.

[0012] The present invention is further configured to construct the dynamic emission risk index logic: , where is the dynamic emission risk index, is the nitrogen oxide concentration, is the sulfur oxide concentration, is the particulate matter concentration, is the nitrogen oxide concentration reference value, is the sulfur oxide concentration reference value, is the particulate matter concentration reference value, is the weight coefficient, is the dynamic change rate of the nitrogen oxide concentration; If the dynamic emission risk index is greater than the set threshold, trigger the control algorithm to optimize the blending ratio.

[0013] The present invention is further configured to optimize the blending ratio calculation logic: , where is the optimized blending ratio adjustment amount, is the proportional term, is the target emission risk threshold, is the integral term; the adjusted blending ratio is: .

[0014] The present invention also provides a co - firing control system for gasification slag - mixed biomass fuel, and the system includes: Data acquisition and fusion module: Initialize and calibrate the sensors, and collect sensor data for data fusion to pre - process the original data; Raw material intelligent classification module: Construct an environmental combustion coordination index based on the pre - processed data, and perform quality determination and intelligent classification on the raw materials; Blending ratio optimization module: Construct an optimal blending ratio model based on the classification results, and use dynamic game scoring to screen the optimal blending ratio; Intelligent closed - loop combustion control module: Construct a closed - loop feedback control system, and input the real - time collected temperature and flame stability parameters into the closed - loop feedback system to realize intelligent regulation of the combustion process; Emission monitoring and feedback optimization module: Construct a dynamic emission risk index, and combine variable regression and dynamic adjustment algorithms to realize closed - loop optimization control of pollutant emissions.

[0015] The present invention provides a co - firing control method and system for gasification slag - mixed biomass fuel. The method initializes and calibrates the sensors, collects sensor data for data fusion to pre - process the original data; constructs an environmental combustion coordination index based on the pre - processed data, and performs quality determination and intelligent classification on the raw materials; constructs an optimal blending ratio model based on the classification results, and uses dynamic game scoring to screen the optimal blending ratio; constructs a closed - loop feedback control system, inputs the real - time collected temperature and flame stability parameters into the closed - loop feedback system to realize intelligent regulation of the combustion process; constructs a dynamic emission risk index, and combines variable regression and dynamic adjustment algorithms to realize closed - loop optimization control of pollutant emissions. The beneficial effects generated include: Data fusion and intelligent classification: By collecting the physical and chemical parameters of raw materials through multi - modal sensors and combining data fusion technology, it can accurately judge the quality and combustion characteristics of different raw materials and perform intelligent classification. According to the environmental combustion coordination index, it can effectively distinguish high - quality, pre - treatment - required or prohibited fuels, providing accurate input for subsequent co - firing optimization.

[0016] Optimal blending ratio model and dynamic game scoring: Based on the preprocessed data, the candidate blending ratios are screened through the dynamic game scoring method to select the optimal blending ratio. This method fully considers various constraints such as environment, calorific value, and sulfur emissions, can optimize the fuel ratio, improve combustion efficiency, and reduce harmful emissions.

[0017] Closed-loop optimization and pollutant emission control: When the dynamic emission risk index exceeds the set threshold, the control system automatically starts the optimization algorithm to adjust the blending ratio and achieve closed-loop optimization control of pollutant emissions. This automatic adjustment mechanism can flexibly adjust the strategy according to the combustion state and emission situation to reduce the generation of pollutants.

[0018] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specifically gives the specific implementation manners of this application. Brief Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings: Figure 1 It is a flowchart of a method for controlling the co-firing of gasification slag mixed biomass fuel shown in an exemplary embodiment of the present invention; Figure 2 It is a schematic structural diagram of a system for controlling the co-firing of gasification slag mixed biomass fuel shown in an exemplary embodiment of the present invention. Detailed Description of the Invention

[0020] The following will illustrate the embodiments of the present invention with reference to the drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for explaining the present invention and not for limiting the protection scope of the present invention.

[0021] It should be noted that the illustrations provided in the following embodiments only schematically illustrate the basic concept of the present invention. Therefore, only the components related to the present invention are shown in the drawings, rather than being drawn according to the number, shape, and size of the components in actual implementation. The types, quantities, and proportions of the components in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0022] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0023] Embodiment 1 A method for controlling the co-firing of gasification slag and biomass fuel, as Figure 1 shown, includes: Initialize and calibrate the sensor, and collect sensor data for data fusion to preprocess the original data; Construct an environmental combustion cooperation index based on the preprocessed data to judge the quality of the raw materials and conduct intelligent classification; Construct an optimal blending ratio model based on the classification results, and use dynamic game scoring to screen the optimal blending ratio; Construct a closed-loop feedback control system, and collect temperature and flame stability parameters in real time and input them into the closed-loop feedback system to achieve intelligent adjustment of the combustion process; Construct a dynamic emission risk index, and combine variable regression and dynamic adjustment algorithms to achieve closed-loop optimization control of pollutant emissions.

[0024] The present invention is further configured to install multi-modal sensors to collect the physical and chemical parameters of the raw materials, upload the collected original data to the cloud for cleaning and denoising, and obtain a preprocessed data set by fusing the sensor data through the method of weighted average: , where is the preprocessed data set, is the number of sensors, is the sensor weight, is the sensor output; the preprocessed data set obtained by data fusion: , where is the mercury content of the raw material, is the chlorine content of the raw material, is the potassium content of the raw material, is the sodium content of the raw material, is the sulfur content of the raw material, is the ash melting point of the raw material, is the moisture content of the raw material, is the volatile matter of the raw material, is the residual carbon content of the raw material, is the higher heating value of the raw material. Specifically, first, install multi-modal sensors to collect the physical and chemical parameters of the raw material, and upload these raw data to the cloud for cleaning and denoising. During the preprocessing process, use the weighted average method to fuse the data from multiple sensors to improve the accuracy and reliability of the data; represents constructing a set by using the weighted average method for the data collected by each sensor.

[0025] The present invention is further set as the calculation logic of the environmental combustion synergy index: , is the environmental combustion synergy index; classification rule: , where and are preset thresholds. Specifically, represents quantifying the potential of dioxin generation, represents amplifying the slagging risk of a high potassium-sodium ratio, represents the penalty for environmental risk when the ash melting point is lower than 1000 degrees Celsius, represents the environmental risk, represents the non-linear antagonistic effect between calorific value and moisture, represents the dynamic balance between unburned carbon and volatile matter. Classify according to the obtained environmental combustion synergy index. High-quality fuels can be directly burned as fuels without blending. Pretreated fuels need to be blended with other fuels to be burned as fuels. Prohibited fuels cannot be used as fuels or added as raw materials to fuels.

[0026] The present invention is further set as the method includes: Construct a candidate blending ratio for the generated pretreated fuel; Screen the candidate blending ratios according to the dynamic game score to obtain the optimal blending ratio; Candidate blending ratio construction logic: , where is the candidate blending ratio; is the environmental constraint, is the calorific value constraint, is the sulfur constraint. Specifically, generate candidate fuel blending ratio schemes that meet the requirements of environmental protection, calorific value, and sulfur emissions through multiple constraint conditions. To ensure that the fuel blend meets all constraint conditions simultaneously, select the most stringent limit value among the three constraint conditions as the final blending ratio.

[0027] The present invention is further set as the environmental constraint calculation logic is: , where is the environmental combustion synergy index of the raw material, is the quality screening threshold; calorific value constraint calculation logic: , where is the calorific value of the raw material, is the minimum calorific value requirement, is the calorific value of the clean fuel; sulfur constraint calculation logic: , where is the sulfur content of the raw material, is the sulfur emission limit, is the sulfur content of the clean fuel. Specifically, is the upper limit of the blending ratio based on environmental emission risks, which is the boundary condition for restricting pollutant emissions after fuel blending. The quality screening threshold is set according to environmental protection regulations, is to ensure that the calorific value of the blended fuel is not lower than the boiler design requirements. The minimum calorific value requirement is determined by the boiler design parameters. The calorific value of the clean fuel is the calorific value of the blended biomass fuel, is to control the sulfur content of the blended fuel within the limit to avoid equipment corrosion or environmental protection penalties caused by exceeding the limit. The sulfur emission limit is a value set according to environmental protection regulations. The sulfur content of the clean fuel is the sulfur content of the blended biomass fuel.

[0028] The present invention is further set as the dynamic game scoring calculation logic: , where is the dynamic game score, is the candidate blending ratio, is the total number of parameters, is the total number of exceeded parameters, is the parameter 's actual value, is the safety threshold of the parameter, is the parameter weight, is the sensitivity, is the global penalty coefficient, is the penalty function, , where is the actual value obtained as the parameter changes with the blending ratio, is the reference value, is the penalty coefficient, is the penalty term; optimal blending ratio calculation logic: , is the optimal blending ratio. Specifically, through the dynamic game score scores the candidate blending ratio to select the optimal solution that takes into account environmental protection, economy and equipment safety; For parameter scoring items, if the score is lower than the safety threshold, it indicates that the parameter is safe. If it is close to the safety threshold, it proves that other parameters need to be weighed. By Adjusting parameter sensitivity, penalty function It means applying an exponential penalty to the over-standard parameters to ensure that slight overlimits are tolerable and severe overlimits are directly rejected; It means selecting the blending ratio with the highest comprehensive score to ensure the optimal score.

[0029] The present invention is further set to construct a combustion state set by real-time monitoring of the combustion state: , where is the combustion temperature, is the flame stability; Fuel injection rate calculation logic: , where is the injection rate, is the predicted injection rate at the next moment, and are adjustment coefficients, is the set target temperature, is the set target flame stability; Fuel injection rate limit constraint: , is the maximum fuel injection rate allowed by the system. The injection rate at the next moment is dynamically adjusted according to the current combustion state to ensure that the combustion process always remains within the set temperature and stability range. Specifically, by real-time monitoring of the combustion temperature and the flame stability , the fuel injection rate is dynamically adjusted to keep the combustion process stable within the set target range, represents the temperature adjustment coefficient, usually with a value range between and represents the stability adjustment coefficient, which is used to adjust according to the flame fluctuation tolerance, usually with a value range between and. The maximum fuel injection rate is the maximum value based on the fuel pump capacity and pipeline design, is the temperature difference, is the stability difference, means restricting the minimum value of the injection rate at the next moment to 0, means restricting the maximum value of the injection rate at the next moment to the maximum fuel injection rate allowed by the system, means preventing fuel injection interruption or over-injection beyond the equipment upper limit.

[0030] The present invention is further set to the construction logic of the dynamic emission risk index: , where is the dynamic emission risk index, is the nitrogen oxide concentration, is the sulfur oxide concentration, is the particulate matter concentration, is the nitrogen oxide concentration reference value, is the sulfur oxide concentration reference value, is the particulate matter concentration reference value, is the weight coefficient, is the dynamic change rate of nitrogen oxide concentration; if the dynamic emission risk index is greater than the set threshold, it triggers the optimization of the blending ratio of the control algorithm. Specifically, represents the static pollutant risk, represents that when the nitrogen oxide concentration and sulfur oxide concentration exceed the limit, the risk increases exponentially, represents when the concentration exceeds the limit, the risk index grows non-linearly, represents the transient change risk term. When the nitrogen oxide concentration suddenly rises, it will significantly increase the dynamic emission risk index and trigger early intervention. The weight coefficient is used to control the transient risk weight, usually has a value range between and.

[0031] The present invention is further configured to optimize the calculation logic of the blending ratio: , where is the optimized blending ratio adjustment amount, is the proportional term, is the target emission risk threshold, is the integral term; the adjusted blending ratio is: . Specifically, based on the deviation between the dynamic emission risk index and the target threshold, the fuel blending ratio is dynamically adjusted through a proportional-integral control algorithm to achieve closed-loop optimization of the emission risk. The proportional term represents rapid response according to the current deviation to reduce instantaneous risk. The integral term is used to accumulate historical deviations and eliminate long-term steady-state errors, represents the blending ratio update, adding the optimized blending ratio adjustment amount to the current optimal blending ratio to ensure that the emissions approach the target threshold.

[0032] Embodiment 2 Please refer to Figure 2 , the exemplary gasification slag mixed biomass fuel co-firing control system includes: Data acquisition and fusion module: Initialize and calibrate the sensors, and collect sensor data for data fusion to preprocess the original data; Raw material intelligent classification module: Construct an environmental combustion collaboration index based on the preprocessed data to determine the quality of raw materials and conduct intelligent classification; Blending ratio optimization module: Construct an optimal blending ratio model based on the classification results, and use dynamic game scoring to screen the optimal blending ratio; Intelligent closed-loop combustion control module: Construct a closed-loop feedback control system, and collect temperature and flame stability parameters in real time and input them into the closed-loop feedback system to achieve intelligent regulation of the combustion process; Emission monitoring and feedback optimization module: Construct a dynamic emission risk index, and combine variable regression and dynamic adjustment algorithms to achieve closed-loop optimal control of pollutant emissions.

[0033] It should be noted that the gasification slag mixed biomass fuel co-firing control system provided in the above embodiment and the gasification slag mixed biomass fuel co-firing control method provided in the above embodiment belong to the same concept. The specific ways in which each module and unit perform operations have been described in detail in the method embodiment, and will not be repeated here. In practical applications, the gasification slag mixed biomass fuel co-firing control system provided in the above embodiment can, according to needs, allocate the above functions to different functional modules, that is, divide the internal structure of the system into different functional modules to complete all or part of the functions described above. This is not limited here either.

[0034] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that includes one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0035] It should be understood that the term "and / or" in this text is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this text generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context before and after.

[0036] In this application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following items" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0037] It should be understood that in various embodiments of this application, the magnitude of the serial numbers of the above processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this application.

[0038] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this text can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0039] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated here.

[0040] In several embodiments provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0041] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0042] In addition, each functional unit in various embodiments of the present application may be integrated in a processing unit, may exist physically separately for each unit, or two or more units may be integrated in one unit.

[0043] If the described function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0044] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claimed rights.

Claims

1. A method for controlling the co - firing of gasification slag and biomass fuel, characterized in that, Including: Initialize and calibrate the sensor, and collect sensor data for data fusion to preprocess the raw data; Construct an environmental combustion coordination index based on the preprocessed data to conduct quality determination and intelligent classification of raw materials; Construct an optimal blending ratio model based on the classification results, and use dynamic game scoring to screen the optimal blending ratio; Construct a closed-loop feedback control system, and collect temperature and flame stability parameters in real time and input them into the closed-loop feedback system to achieve intelligent regulation of the combustion process; Construct a dynamic emission risk index, and combine variable regression and dynamic adjustment algorithms to achieve closed-loop optimal control of pollutant emissions.

2. The method for controlling the co-firing of gasification slag and mixed biomass fuel according to claim 1, wherein Install multimodal sensors to collect the physical and chemical parameters of raw materials, upload the collected raw data to the cloud for cleaning and denoising, and use the weighted average method to fuse sensor data to obtain a preprocessed data set: , where is the preprocessed data set, is the number of sensors, is the sensor weight, is the sensor output; The preprocessed data set obtained by data fusion: , where is the mercury content of the raw material, is the chlorine content of the raw material, is the potassium content of the raw material, is the sodium content of the raw material, is the sulfur content of the raw material, is the ash melting point of the raw material, is the moisture content of the raw material, is the volatile matter of the raw material, is the residual carbon content of the raw material, is the higher calorific value of the raw material.

3. A method for controlling the co-firing of gasification slag and biomass fuel according to claim 1, characterized in that, Calculation logic of the environmental combustion coordination index: , is the environmental combustion coordination index; Classification rules: , where and are preset thresholds.

4. The method for controlling the co-firing of gasification slag and biomass fuel according to claim 1, characterized in that, The method includes: Construct candidate blending ratios for the generated preprocessed fuel; Screen the candidate blending ratios according to the dynamic game scoring to obtain the optimal blending ratio; Candidate blending ratio construction logic: , where is the candidate blending ratio; is the environmental constraint, is the calorific value constraint, is the sulfur constraint.

5. A method for controlling the co-firing of gasification slag and biomass fuel according to claim 4, characterized in that, The environmental constraint calculation logic is as follows: , where is the environmental combustion synergy index of the raw material, is the mass screening threshold; The calorific value constraint calculation logic: , where is the calorific value of the raw material, is the minimum calorific value requirement, is the calorific value of the clean fuel; The sulfur constraint calculation logic: , where is the sulfur content of the raw material, is the sulfur emission limit, is the sulfur content of the clean fuel.

6. The method for controlling the co-firing of gasification slag and mixed biomass fuel according to claim 5, wherein Dynamic game scoring calculation logic: , where is the dynamic game score, is the candidate blending ratio, is the total number of parameters, is the total number of out-of-specification parameters, is the parameter actual value, is the safety threshold of the parameter, is the parameter weight, is the sensitivity, is the global penalty coefficient, is the penalty function, , where is the actual value obtained as the parameter changes with the blending ratio, is the reference value, is the penalty coefficient, is the penalty term; Optimal blending ratio calculation logic: , is the optimal blending ratio.

7. A method for controlling the co-firing of gasification slag and mixed biomass fuel according to claim 6, characterized in that, Real-time monitoring of the combustion state to construct a combustion state set: , where is the combustion temperature, is the flame stability; Fuel injection rate calculation logic: , where is the injection rate, is the predicted injection rate at the next moment, and are adjustment coefficients, is the set target temperature, is the set target flame stability; Fuel injection rate limit constraint: , is the maximum fuel injection rate allowed by the system. The injection rate at the next moment is dynamically adjusted according to the current combustion state to ensure that the combustion process always remains within the set temperature and stability range.

8. A method for controlling the co-firing of gasification slag and mixed biomass fuel according to claim 1, characterized in that, Construction logic of dynamic emission risk index: , where is the dynamic emission risk index, is the nitrogen oxide concentration, is the sulfur oxide concentration, is the particulate matter concentration, is the reference value of nitrogen oxide concentration, is the reference value of sulfur oxide concentration, is the reference value of particulate matter concentration, is the weight coefficient, is the dynamic change rate of nitrogen oxide concentration; if the dynamic emission risk index is greater than the set threshold, it triggers the optimization of the blending ratio of the control algorithm.

9. A method for controlling the co - firing of gasification slag and mixed biomass fuel according to claim 8, characterized in that, Optimize the calculation logic of the blending ratio: , where is the adjustment amount of the optimized blending ratio, is the proportional term, is the target emission risk threshold, is the integral term; The adjusted blending ratio is: .

10. A control system for co-firing gasification slag and biomass fuel, which is used to implement the co-firing control method of gasification slag and biomass fuel according to any one of claims 1-9, characterized in that, Including: Data acquisition and fusion module: Initialize and calibrate the sensor, and collect sensor data for data fusion to preprocess the raw data; Raw material intelligent classification module: Construct an environmental combustion coordination index based on the preprocessed data to conduct quality determination and intelligent classification of raw materials; Blending ratio optimization module: Construct an optimal blending ratio model based on the classification results, and use dynamic game scoring to screen the optimal blending ratio; Intelligent closed-loop combustion control module: Construct a closed-loop feedback control system, and collect temperature and flame stability parameters in real time and input them into the closed-loop feedback system to achieve intelligent regulation of the combustion process; Emission monitoring and feedback optimization module: Construct a dynamic emission risk index, and combine variable regression and dynamic adjustment algorithms to achieve closed-loop optimal control of pollutant emissions.

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