Urban solid waste incineration hearth temperature control method and device, electronic equipment and storage medium

Abstract

The invention provides an urban solid waste incineration hearth temperature control method and device, electronic equipment and a storage medium, and relates to the technical field of temperature control, the method comprises the steps that combustion control parameters of a target hearth are collected, and the combustion control parameters comprise at least one of the primary air volume, the secondary air volume, the fire grate speed and the material pusher speed; inputting the temperature control error data of the target hearth into a controller model by taking the combustion control parameter as an operation variable of the pre-constructed controller model, and generating a control law of the target hearth at the next moment; wherein the control law is the variable quantity of the operation variable of the target hearth at the next moment relative to the current moment; and determining that the control law meets an updating condition, and updating the operation variable at the current moment according to the control law. The control law can be determined based on the combustion control parameters, and the operation variables are adjusted according to the control law, so that the urban solid waste is fully combusted, and the combustion efficiency of the urban solid waste is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology, and in particular to a method, device, electronic equipment, and storage medium for controlling the temperature of an urban solid waste incinerator. Background Technology

[0002] With economic development and urban expansion, the amount of municipal solid waste (MSW) in my country is also rapidly increasing, leading to environmental degradation in many cities. How to properly dispose of MSW has become one of the most important issues facing urban development and environmental protection in China. Incineration of municipal solid waste has gradually become the main method of municipal solid waste treatment in my country due to its advantages of harmlessness, volume reduction, resource recovery, and economic practicality. Furnace temperature (850-1200℃) is crucial for achieving complete combustion of solid waste and suppressing the generation of high-concentration pollutants, and it is also key to the efficient and stable operation of the municipal solid waste combustion process. Fluctuations in furnace temperature are a significant factor. These fluctuations can adversely affect the burnout rate of solid waste, causing its loss on ignition to exceed standard requirements. This not only increases energy consumption and operating costs but may also reduce economic benefits due to decreased steam production efficiency, as the amount of steam required for production is reduced. Therefore, to maintain good operational performance and economic benefits, it is essential to closely monitor the furnace temperature and take corresponding control measures to maintain a stable temperature level to ensure that solid waste is fully combusted.

[0003] Existing methods for controlling the furnace temperature in urban solid waste incineration plants are insufficient to stably control the furnace combustion temperature, resulting in low incineration efficiency. Summary of the Invention

[0004] This invention provides a method, device, electronic equipment, and storage medium for controlling the furnace temperature of urban solid waste incineration, in order to solve the technical problem that it is difficult to stably control the furnace incineration temperature in the prior art, resulting in low incineration efficiency of urban solid waste.

[0005] This invention provides a method for controlling the temperature of a municipal solid waste incinerator furnace, comprising: The combustion control parameters of the target furnace are collected, including at least one of primary air volume, secondary air volume, grate speed and pusher speed. Using the combustion control parameters as the operating variables of a pre-built controller model, the temperature control error data of the target furnace is input into the controller model to generate the control law of the target furnace at the next moment; wherein, the temperature control error data includes the temperature control error, the first derivative of the temperature control error, and the second derivative of the temperature control error; the control law is the change of the operating variables of the target furnace at the next moment relative to the current moment; Once the control law is determined to satisfy the update condition, the operation variable at the current moment is updated according to the control law.

[0006] According to the present invention, a method for controlling the temperature of an urban solid waste incinerator furnace is provided, wherein the controller model includes at least one controller, and each controller includes at least one sub-network; The control law for generating the target furnace at the next moment includes: Based on temperature control error data and the antecedent parameters of the controller, the defuzzified output data of each fuzzy rule is determined. Based on the temperature control error data and the controller's subsequent parameters, determine the output data of each sub-network; Based on the defuzzified output data of each fuzzy rule and the output data of each sub-network, the control law for the target furnace at the next moment is generated.

[0007] According to the present invention, a method for controlling the temperature of a municipal solid waste incineration furnace includes determining the defuzzified output data of each fuzzy rule based on temperature control error data and the antecedent parameters of the controller, comprising: Based on temperature control error data and the antecedent parameters of the controller, the membership degree of each operated variable is determined; wherein, the antecedent parameters of the controller include the center and width of the membership function; Based on the membership degree, determine the output data for each fuzzy rule; The output data of each fuzzy rule is fuzzified to obtain the defuzzified output data corresponding to each fuzzy rule.

[0008] According to the present invention, a method for controlling the temperature of a municipal solid waste incineration furnace, before determining the defuzzified output data of each fuzzy rule based on temperature control error data and the antecedent parameters of the controller, further includes: Calculate the activation intensity of the neuron corresponding to each fuzzy rule; If the absolute value of the current temperature error is greater than the preset lower limit of the temperature error, and the activation intensity of the neuron is less than the preset first activation intensity, a new fuzzy rule is added. If the absolute value of the current temperature control error is greater than the preset upper limit of the temperature error, and the activation intensity of the neuron is greater than the preset second activation intensity, the current fuzzy rule is deleted.

[0009] According to the present invention, a method for controlling the temperature of a municipal solid waste incinerator furnace includes determining the temperature control error data, comprising: Set the target temperature value according to the actual needs of the target furnace; Collect the actual temperature value of the target furnace; Based on the target temperature value and the actual temperature value, temperature control error data is determined.

[0010] According to the present invention, a method for controlling the temperature of a municipal solid waste incinerator furnace, wherein determining that the control law satisfies the update condition includes: A temperature error vector is determined based on the temperature control error data. When the absolute value of an element in the temperature error vector is greater than or equal to a preset threshold, the control law is determined to meet the update condition.

[0011] The present invention also provides a temperature control device for an urban solid waste incineration furnace, comprising: The combustion control parameter acquisition module is used to acquire the combustion control parameters of the target furnace, wherein the combustion control parameters include at least one of primary air volume, secondary air volume, grate speed and pusher speed; The control law output module is used to input the temperature control error data of the target furnace into the controller model, using the combustion control parameters as the operating variables of the pre-built controller model, to generate the control law of the target furnace at the next moment; wherein, the temperature control error data includes the temperature control error, the first derivative of the temperature control error, and the second derivative of the temperature control error; the control law at the current moment is the change of the operating variables of the target furnace at the next moment relative to the current moment; The operation variable update module is used to determine whether the control law meets the update conditions, and update the operation variables at the current time according to the control law.

[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the urban solid waste incineration furnace temperature control method as described above.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the urban solid waste incineration furnace temperature control method as described above.

[0014] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the urban solid waste incineration furnace temperature control method as described above.

[0015] This invention provides a method, apparatus, electronic device, and storage medium for controlling the temperature of a municipal solid waste incinerator furnace. By using at least one of the primary air volume, secondary air volume, grate speed, and pusher speed as the operating variables of the controller, and using temperature control error data as the input data of the controller, the influence of various combustion control parameters on the temperature control error data is fully considered. Thus, the controller can output the control law for the next moment, and adjust the operating variables according to the control law for the next moment to stabilize and control the furnace temperature, thereby fully burning municipal solid waste and effectively improving the combustion efficiency of municipal solid waste.

[0016] Furthermore, this invention determines the relevant data of temperature control error by the difference between the real-time temperature of the target furnace and the target temperature, and inputs the relevant data of temperature error into the controller model as input data. Multiple relevant data of temperature error can ensure the reliability of the finally determined control law. Then, according to the control law, the furnace temperature is stably controlled, the error between the actual temperature and the target temperature is reduced, and the solid waste can be fully burned. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of a method for controlling the temperature of an urban solid waste incinerator furnace provided by the present invention. Figure 2 This is a schematic diagram of the structure of a self-organizing TS fuzzy neural network provided by the present invention; Figure 3 This is a schematic diagram illustrating the test effect of a controller under varying setpoints provided by the present invention; Figure 4 This is a schematic diagram of the test error of a controller under a variable setpoint provided by the present invention; Figure 5 This is a schematic diagram illustrating the change in the number of neurons in a controller under varying setpoints, provided by the present invention. Figure 6 This is a schematic diagram comparing the number of control variable updates before and after the introduction of an event triggering mechanism, provided by the present invention. Figure 7 This is a schematic diagram of the structure of a temperature control device for an urban solid waste incineration furnace provided by the present invention; Figure 8 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0020] Figure 1 This is a schematic flowchart of the urban solid waste incineration furnace temperature control method provided by the present invention, which includes the following: S1. Collect combustion control parameters of the target furnace, wherein the combustion control parameters include at least one of primary air volume, secondary air volume, grate speed and pusher speed; In this embodiment of the invention, the target furnace is a furnace that requires temperature control. The target furnace is located in a municipal solid waste incineration plant.

[0021] In this embodiment of the invention, appropriate sensors, data acquisition systems, or automated control systems can be used to collect combustion control parameters of the target furnace. For example, an air volume sensor can be used to collect the primary and secondary air volumes of the target furnace; a speed sensor, such as a photoelectric sensor or encoder, can be used to collect the grate speed and pusher speed of the target furnace.

[0022] S2. Using the combustion control parameters as the operating variables of the pre-built controller model, the temperature control error data of the target furnace is input into the controller model to generate the control law of the target furnace at the next moment; wherein, the temperature control error data includes the temperature control error, the first derivative of the temperature control error, and the second derivative of the temperature control error; the control law is the change of the operating variables of the target furnace at the next moment relative to the current moment; In this embodiment of the invention, combustion control parameters can be used as operating variables of the controller model through expert experience or analysis of historical data. Specifically, when the fermentation of municipal solid waste is good and the amount of municipal solid waste incinerated is stable, the primary air volume, secondary air volume, grate speed, and pusher speed have a significant impact on the furnace temperature. Therefore, these parameters can be considered key factors for furnace temperature control and used as operating variables of the controller model. The operating variables of the controller model are crucial for controlling the furnace temperature. Precise control of these variables can effectively optimize the combustion efficiency of the target furnace during municipal solid waste incineration, reduce energy consumption, and ensure emissions meet environmental standards.

[0023] In this embodiment of the invention, the air temperature information of the target furnace also affects the temperature change during the furnace combustion process. The air temperature information of the target furnace, such as the primary air temperature and the secondary air temperature, can be collected and used as an uncertainty factor (i.e., disturbance) to be input into the controller model. This allows the influence of the disturbance on the furnace temperature control to be fully considered during the generation of the control law for the next moment, thereby further accurately determining the control law and improving the accuracy and reliability of the furnace temperature control.

[0024] In this embodiment of the invention, a self-organizing TS fuzzy neural network can be designed, and a controller model can be built on the basis of the fuzzy neural network.

[0025] Please see Figure 2 This is a schematic diagram of a self-organizing TS fuzzy neural network provided in an embodiment of the present invention. The self-organizing TS fuzzy neural network includes a predecessor network and a successor network. The predecessor network includes an input layer, an asymmetric membership layer, a rule layer, a predecessor output layer, and an output layer connected in sequence. The input layer of the predecessor network receives the input temperature control error data, and its output layer outputs the control law corresponding to each variable. The successor network includes an input layer and an output layer. Its input layer receives the input temperature control error data, and its output layer inputs the relevant data of the temperature control error data to the predecessor output layer of the predecessor network.

[0026] In this embodiment of the invention, the temperature control error data can be determined based on the real-time temperature of the target furnace and the preset target temperature.

[0027] S3. Determine that the control law satisfies the update condition, and update the operation variable at the current moment according to the control law.

[0028] In this embodiment of the invention, an event triggering mechanism can be designed in the controller model. After the control law is determined, if the control law meets the update condition, the update of the operation variable is triggered to adjust the update frequency of the operation variable, thereby adjusting the control frequency of the furnace temperature. This can ensure that urban solid waste is fully combusted while reducing the wear and tear on the actuator caused by repeated control operations, thus reducing its service life.

[0029] In this embodiment of the invention, the more combustion control parameters selected, the better the control effect of furnace temperature. For example, the four parameters of primary air volume, secondary air volume, grate speed and pusher speed can all be used as the operating variables of the controller. The influence of the four combustion control parameters on the furnace temperature is fully considered, and the control law at the next moment is accurately determined under the premise of reducing the temperature error in the furnace. Adjustments are made for each combustion control parameter to stabilize the furnace temperature, which can effectively improve the control effect of furnace temperature and the combustion efficiency of solid waste.

[0030] This invention uses at least one of the primary air volume, secondary air volume, grate speed, and pusher speed as the operating variables of the controller, and uses temperature control error data as the input data of the controller. It fully considers the influence of multiple combustion control parameters on temperature control error data, so that the controller can output the control law for the next moment, and adjust the operating variables according to the control law for the next moment to stabilize the furnace temperature, thereby fully burning municipal solid waste and effectively improving the combustion efficiency of municipal solid waste.

[0031] In one embodiment, determining the temperature control error data in step S1 includes: S11. Set the target temperature value according to the actual needs of the target furnace; In this embodiment of the invention, a target temperature value can be set based on factors such as the current calorific value of solid waste, fluctuations in main steam flow, and production capacity requirements. By setting a target temperature value, this embodiment of the invention ensures efficient incineration of solid waste and fully considers the efficiency of energy recovery. For example, if municipal solid waste incineration is used for heat recovery, a temperature that maximizes heat output needs to be set. If municipal solid waste incineration is used for power generation, a target temperature value that satisfies both the requirements of high temperature and power generation efficiency needs to be determined.

[0032] S12. Collect the actual temperature value of the target furnace; In this embodiment of the invention, the actual temperature value of the target furnace can be collected by setting up a sensor.

[0033] S13. Determine the temperature control error data based on the target temperature value and the actual temperature value.

[0034] In this embodiment of the invention, the temperature control error data can be calculated according to the following formula: in, The target temperature value set for the furnace. This represents the actual temperature value of the furnace. Due to temperature control error, The temperature control error at the current moment, This represents the temperature control error from the previous moment. The first derivative of the temperature control error, for Temperature control error at any time for Temperature control error at any time This is the second derivative of the temperature control error.

[0035] In this embodiment of the invention, the difference between the real-time temperature of the target furnace and the target temperature is used to determine the relevant data of temperature control error. The relevant data of temperature error is then input into the controller model. Multiple relevant data of temperature error can ensure the reliability of the final determined control law. Based on the control law, the furnace temperature is stably controlled, the error between the actual temperature and the target temperature is reduced, and the urban solid waste can be fully combusted.

[0036] In one embodiment, the controller model includes at least one controller, and each controller includes at least one sub-network; S2, the control law for generating the target furnace at the next moment includes: S21. Based on the temperature control error data and the antecedent parameters of the controller, determine the defuzzified output data for each fuzzy rule; In this embodiment of the invention, the expression for the defuzzified output data of each fuzzy rule can be as follows: in, For the first j The output data after defuzzification of the fuzzy rules. For the first j The calculation results of the fuzzy rule m To determine the number of fuzzy rules, It can be determined based on the antecedent parameters of the controller.

[0037] S22. Determine the output data of each sub-network based on the temperature control error data and the subsequent parameters of the controller; In this embodiment of the invention, the expression for the output data of the sub-network is as follows: in, For the first j The output data of each subnetwork , , , These are the consequent parameters of the controller. In this embodiment of the invention, the number of sub-networks corresponds one-to-one with the number of controllers and fuzzy rules.

[0038] S23. Based on the defuzzified output data of each fuzzy rule and the output data of each sub-network, generate the control law of the target furnace at the next moment.

[0039] In this embodiment of the invention, the expression for the control law at the next moment is as follows: in, For the control law of the next moment,i For the number of variables to be operated on, , For the first j The output data after defuzzification of the fuzzy rules. For the first j Output data of each subnetwork.

[0040] The output data of the network implemented in this invention is the data output by the sub-network based on the input temperature control error data. The output data of the sub-network is accumulated with the defuzzified output data of each fuzzy rule to obtain an accurate control law. This allows for dynamic adjustment of the control strategy based on the temperature error data to adapt to different combustion conditions and load changes, as well as to cope with external interference and uncertainties in system parameters, thereby effectively improving the stability of furnace temperature control.

[0041] In one embodiment, a parameter update mechanism can also be set within the controller model to update the antecedent and consequent parameters of the controller, thereby updating the calculated control law for the next moment to further improve control accuracy.

[0042] In this embodiment of the invention, the controller parameters can be updated using the gradient descent algorithm, specifically as follows: Based on the real-time temperature of the target furnace and the target temperature, an error cost function is established: in, Let be the error cost function.

[0043] The controller's antecedent parameter updates include: The central update formula for membership functions: The formula for updating the width of the membership function is as follows: The controller's antecedent parameters are updated as follows: in, , It is the learning rate of the controller.

[0044] In this embodiment of the invention, the minimum temperature control error can be used as the objective function, the gradient of the objective function with respect to each controller parameter can be calculated, and the controller parameters can be updated using the gradient descent algorithm.

[0045] The embodiments of the present invention use a gradient descent algorithm to update controller parameters, which can dynamically adjust controller parameters based on real-time data to minimize control errors and improve the real-time performance and accuracy of furnace temperature control; and by continuously updating controller parameters, the furnace can maintain stable operation in the face of uncertainties and external disturbances.

[0046] In one embodiment, step S21, determining the defuzzified output data for each fuzzy rule based on temperature control error data and the controller's antecedent parameters, includes: S211. Based on temperature control error data and the antecedent parameters of the controller, determine the membership degree of each operated variable; wherein, the antecedent parameters of the controller include the center and width of the membership function; In this embodiment of the invention, the expression of the membership function of the operand is as follows: in, For the first i The first operand of the _th operation variable j The membership degree of a fuzzy rule. For the first i The first operand of the _th operation variable j The center of a membership function For the first i The first operand of the _th operation variable j The width of each membership function, Let this be the temperature error vector. This is the preset threshold for asymmetric membership functions.

[0047] S212. Determine the output data for each fuzzy rule based on the membership degree. In this embodiment of the invention, the expression for the output data of the fuzzy rule is as follows: in, For the first j The output data of a fuzzy rule n This represents the number of controllers.

[0048] S213. Perform fuzzification processing on the output data of each fuzzy rule to obtain the defuzzified output data corresponding to each fuzzy rule.

[0049] In this embodiment of the invention, the expression for the defuzzified output data corresponding to the fuzzy rule is as follows: in, For the first j The output data after defuzzification of the fuzzy rules. mThe number of fuzzy rules.

[0050] This invention constructs membership functions and determines the output data of fuzzy rules. The output data of each fuzzy rule is then fuzzified to obtain the defuzzified output data corresponding to each fuzzy rule. This enables the controller to adapt to different combustion conditions and load changes, thereby improving the reliability of furnace temperature control.

[0051] In one embodiment, before step S21, which determines the defuzzified output data for each fuzzy rule based on temperature control error data and the controller's antecedent parameters, the method further includes: S221. Calculate the activation intensity of the neuron corresponding to each fuzzy rule; In this embodiment of the invention, the expression for the neuron activation intensity corresponding to the fuzzy rule is as follows: in, For the first j The activation intensity of neurons corresponding to a fuzzy rule. For the temperature error vector, the first... i Each element.

[0052] S222. When the absolute value of the current temperature error is greater than the preset lower limit of the temperature error and the activation intensity of the neuron is less than the preset first activation intensity, a new fuzzy rule is added. In this embodiment of the invention, a new fuzzy rule is added when the following conditions are met: in, It is the absolute value of the current temperature control error. This is the lower limit of temperature error. K This represents the maximum activation intensity of the neuron corresponding to the current fuzzy rule. r 0 It is the pre-set first activation strength.

[0053] In this embodiment of the invention, the expression for the newly added fuzzy rule is as follows: in, The center of the membership function of the new fuzzy rule, The width of the membership function of the newly added fuzzy rule. These are the back-end parameters of the controller.

[0054] S223. If the absolute value of the current temperature control error is greater than the preset upper limit of the temperature error, and the activation intensity of the neuron is greater than the preset second activation intensity, delete the current fuzzy rule.

[0055] In this embodiment of the invention, the current fuzzy rule is deleted when the following conditions are met: In an embodiment of the present invention, It is the absolute value of the current temperature control error. This is the upper limit of temperature error. Q This is another expression of the maximum activation intensity of the neuron corresponding to the current fuzzy rule. r 1 It is a pre-set second activation intensity.

[0056] In this embodiment of the invention, to ensure that the changes in the information stored in the network before and after deleting a fuzzy rule are not too drastic, the parameters of the neuron closest to the neuron of the deleted fuzzy rule in Euclidean distance will not be adjusted, as described in the following expression: in , and It is the fuzzy rule parameter of the neuron that is closest in Euclidean distance to the deleted neuron. , ,and These are the fuzzy rule parameters after the pruning operation has been performed on the aforementioned fuzzy rule parameters.

[0057] This invention, through adding and removing fuzzy rules, enables the controller to more accurately capture key features and trends in the furnace temperature control process, thereby improving control accuracy and reducing temperature deviation. Furthermore, by removing fuzzy rules, some neuron parameters are left unchanged, effectively preventing drastic changes in network stored information before and after the removal of fuzzy rules, thus improving information storage stability.

[0058] In one embodiment, step S3, determining that the control law satisfies the update condition, includes: A temperature error vector is determined based on the temperature control error data. When the absolute value of an element in the temperature error vector is greater than or equal to a preset threshold, the control law is determined to meet the update condition.

[0059] In this embodiment of the invention, the temperature error vector The expression is as follows: The update conditions in this embodiment of the invention are as follows: in, It is the first element in the absolute value of the furnace temperature error vector, i.e., the furnace temperature control error. It is the second element in the absolute value of the furnace temperature error vector, that is, the first derivative of the furnace temperature control error. M 1 and M 2 These are the first and second preset thresholds, which can be set according to the indicator requirements and operating costs.

[0060] In this embodiment of the invention, when it is determined that the control law meets the update conditions, the controller's operating variables can be updated in the following manner: in, For the updated controller's first i Given one operation variable, The controller at the current moment i Given one operation variable, This is a control law.

[0061] If the control law does not meet the update condition, the controller's operating variables retain their original values, as shown in the following expression: .

[0062] In this embodiment of the invention, after determining the control law, if the control law meets the update conditions, the update of the control variables is triggered to adjust the update frequency of the operation variables, thereby adjusting the control frequency of the furnace temperature. This can ensure the complete combustion of urban solid waste while reducing the wear and tear on the actuator caused by repeated control operations, thus reducing its service life and effectively improving the service life of the controller.

[0063] In one embodiment, after updating the operating variables at the current time according to the current time control rate, the method further includes: The current temperature of the target furnace is obtained. Based on the current temperature and the target temperature value, multiple control performance indicators are determined. When at least one control performance indicator is less than the corresponding preset indicator threshold, the temperature control result of the target furnace is determined to be passed. The control performance indicators include at least one of temperature integral square error, temperature integral absolute error, temperature integral time error absolute value, and temperature error maximum error.

[0064] In this embodiment of the invention, the calculation expressions for each performance index are as follows: in, ISE It is the integral squared error of temperature. IAE It is the absolute error of the temperature integral. ITAE It is the absolute value of the temperature integral time error. Dev Max This represents the maximum temperature error. The smaller the values ​​of the aforementioned performance indicators, the better the temperature control effect of the furnace. In this embodiment of the invention, a corresponding preset threshold value can be set for each performance indicator. When at least one control performance indicator is less than the corresponding preset threshold value, the temperature control result of the target furnace is determined to be satisfactory, meaning the combustion requirements for solid waste incineration are met, thereby ensuring that the solid waste can be fully combusted.

[0065] In one embodiment, actual data from a municipal solid waste incineration plant in a certain region are collected to verify the urban solid waste incineration furnace temperature control method provided by this embodiment of the invention.

[0066] After collecting relevant data on the incineration process at the incineration plant, obvious abnormal data were removed, resulting in 13,000 sets of 8-dimensional experimental data. Based on the obtained data samples and expert experience, feature selection was performed, choosing four variables with high correlation to furnace temperature—primary air volume, secondary air volume, grate speed, and pusher speed—as the controller's operating variables, with temperature control error data used as the controller's input.

[0067] Of the 13,000 sets of data after preprocessing, 9,000 sets were used to build the controller model, and the remaining 4,000 sets were used for model performance testing. In this embodiment of the invention, the controller setting value can be set to a step change from 1130°C to 1140°C to 1120°C, and an event-triggered self-organizing TS fuzzy neural network is used.

[0068] Please see Figure 3-5 This is a schematic diagram illustrating the test of multiple variable setpoint control effects provided in an embodiment of the present invention. Figure 3 To test the controller's performance under varying setpoints, Figure 4 To measure the controller's test error under varying setpoints, Figure 5 The number of neurons in the controller under varying setpoints. Figure 3 , Figure 4 , Figure 5The X-axis represents the number of training samples, in units of samples. Figure 3 , Figure 4 The Y-axis represents the furnace temperature value, in °C. Figure 5 The Y-axis represents the number of neurons in the membership layer, with the unit being one. Figure 6 It compares the number of times the control variables are updated before and after the introduction of the event triggering mechanism. The X-axis represents the number of training samples, in units of one sample, and the Y-axis represents the number of times the control law is updated, in units of times.

[0069] Please refer to Table 1, which is a performance indicator diagram. According to the performance indicator data in Table 1, it can be determined that the urban solid waste incineration furnace temperature control method provided in this embodiment of the invention can stably control the furnace temperature, resulting in better furnace incineration effect.

[0070] Table 1: Performance Indicators Performance indicators ET-AMF-SOTSFNNController ISE 5.5588 IAE 0.9023 ITAE 1.350e+03 DevMax 27.945 Event trigger count 2209 Implementing the embodiments of the present invention has the following beneficial effects: This invention uses at least one of the primary air volume, secondary air volume, grate speed, and pusher speed as the operating variables of the controller, and uses temperature control error data as the input data of the controller. It fully considers the influence of multiple combustion control parameters on temperature control error data, so that the controller can output the control law for the next moment, and adjust the operating variables according to the control law for the next moment to stabilize the furnace temperature, thereby fully burning municipal solid waste and effectively improving the combustion efficiency of municipal solid waste.

[0071] Furthermore, in this embodiment of the invention, the difference between the real-time temperature of the target furnace and the target temperature is used to determine the relevant data of temperature control error. The relevant data of temperature error is then input into the controller model. Multiple relevant data of temperature error can ensure the reliability of the finally determined control law. Based on the control law, the furnace temperature is stably controlled, the error between the actual temperature and the target temperature is reduced, and the urban solid waste can be fully combusted.

[0072] The temperature control device for urban solid waste incineration furnace provided by the present invention is described below. The temperature control device for urban solid waste incineration furnace described below can be referred to in correspondence with the temperature control method for urban solid waste incineration furnace described above.

[0073] Please see Figure 7 The present invention provides a temperature control device for an urban solid waste incineration furnace, comprising: Combustion control parameter acquisition module 10 is used to acquire combustion control parameters of the target furnace, wherein the combustion control parameters include at least one of primary air volume, secondary air volume, grate speed and pusher speed; The control law output module 20 is used to input the temperature control error data of the target furnace into the controller model, using the combustion control parameters as the operating variables of the pre-built controller model, to generate the control law of the target furnace at the next moment; wherein, the temperature control error data includes the temperature control error, the first derivative of the temperature control error, and the second derivative of the temperature control error; the control law at the current moment is the change of the operating variables of the target furnace at the next moment relative to the current moment; The operation variable update module 30 is used to determine that the control law meets the update conditions, and update the operation variable at the current time according to the control law.

[0074] In one embodiment, the controller model includes at least one controller, and each controller includes at least one sub-network; The control law for generating the target furnace at the next moment includes: Based on temperature control error data and the antecedent parameters of the controller, the defuzzified output data of each fuzzy rule is determined. Based on the temperature control error data and the controller's subsequent parameters, determine the output data of each sub-network; Based on the defuzzified output data of each fuzzy rule and the output data of each sub-network, the control law for the target furnace at the next moment is generated.

[0075] In one embodiment, determining the defuzzified output data for each fuzzy rule based on temperature control error data and the controller's antecedent parameters includes: Based on temperature control error data and the antecedent parameters of the controller, the membership degree of each operated variable is determined; wherein, the antecedent parameters of the controller include the center and width of the membership function; Based on the membership degree, determine the output data for each fuzzy rule; The output data of each fuzzy rule is fuzzified to obtain the defuzzified output data corresponding to each fuzzy rule.

[0076] In one embodiment, before determining the defuzzified output data for each fuzzy rule based on temperature control error data and the controller's antecedent parameters, the method further includes: Calculate the activation intensity of the neuron corresponding to each fuzzy rule; If the absolute value of the current temperature error is greater than the preset lower limit of the temperature error, and the activation intensity of the neuron is less than the preset first activation intensity, a new fuzzy rule is added. If the absolute value of the current temperature control error is greater than the preset upper limit of the temperature error, and the activation intensity of the neuron is greater than the preset second activation intensity, the current fuzzy rule is deleted.

[0077] In one embodiment, determining the temperature control error data includes: Set the target temperature value according to the actual needs of the target furnace; Collect the actual temperature value of the target furnace; Based on the target temperature value and the actual temperature value, temperature control error data is determined.

[0078] In one embodiment, determining that the control law satisfies the update condition includes: A temperature error vector is determined based on the temperature control error data. When the absolute value of an element in the temperature error vector is greater than or equal to a preset threshold, the control law is determined to meet the update condition.

[0079] Figure 8 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 8 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute a method for controlling the temperature of a municipal solid waste incinerator furnace, the method including: The combustion control parameters of the target furnace are collected, including at least one of primary air volume, secondary air volume, grate speed and pusher speed. Using the combustion control parameters as the operating variables of a pre-built controller model, the temperature control error data of the target furnace is input into the controller model to generate the control law of the target furnace at the next moment; wherein, the temperature control error data includes the temperature control error, the first derivative of the temperature control error, and the second derivative of the temperature control error; the control law is the change of the operating variables of the target furnace at the next moment relative to the current moment; Once the control law is determined to satisfy the update condition, the operation variable at the current moment is updated according to the control law.

[0080] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the 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 cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0081] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program that can be stored on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, the computer is able to execute the urban solid waste incineration furnace temperature control method provided by the above methods, the method comprising: The combustion control parameters of the target furnace are collected, including at least one of primary air volume, secondary air volume, grate speed and pusher speed. Using the combustion control parameters as the operating variables of a pre-built controller model, the temperature control error data of the target furnace is input into the controller model to generate the control law of the target furnace at the next moment; wherein, the temperature control error data includes the temperature control error, the first derivative of the temperature control error, and the second derivative of the temperature control error; the control law is the change of the operating variables of the target furnace at the next moment relative to the current moment; Once the control law is determined to satisfy the update condition, the operation variable at the current moment is updated according to the control law.

[0082] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the urban solid waste incineration furnace temperature control method provided by the above methods, the method comprising: The combustion control parameters of the target furnace are collected, including at least one of primary air volume, secondary air volume, grate speed and pusher speed. Using the combustion control parameters as the operating variables of a pre-built controller model, the temperature control error data of the target furnace is input into the controller model to generate the control law of the target furnace at the next moment; wherein, the temperature control error data includes the temperature control error, the first derivative of the temperature control error, and the second derivative of the temperature control error; the control law is the change of the operating variables of the target furnace at the next moment relative to the current moment; Once the control law is determined to satisfy the update condition, the operation variable at the current moment is updated according to the control law.

[0083] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0084] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling the temperature of an urban solid waste incinerator furnace, characterized in that, include: The combustion control parameters of the target furnace are collected, and the combustion control parameters include at least one of primary air volume, secondary air volume, grate speed and pusher speed; Using the combustion control parameters as the operating variables of a pre-built controller model, the temperature control error data of the target furnace is input into the controller model to generate the control law of the target furnace at the next moment; wherein, the temperature control error data includes the temperature control error, the first derivative of the temperature control error, and the second derivative of the temperature control error; the control law is the change of the operating variables of the target furnace at the next moment relative to the current moment; Once the control law is determined to satisfy the update condition, the operation variable at the current moment is updated according to the control law.

2. The method for controlling the temperature of an urban solid waste incinerator furnace as described in claim 1, characterized in that, The controller model includes at least one controller, and each controller includes at least one sub-network; The control law for generating the target furnace at the next moment includes: Based on temperature control error data and the antecedent parameters of the controller, the defuzzified output data of each fuzzy rule is determined. Based on the temperature control error data and the controller's subsequent parameters, determine the output data of each sub-network; Based on the defuzzified output data of each fuzzy rule and the output data of each sub-network, the control law for the target furnace at the next moment is generated.

3. The method for controlling the temperature of an urban solid waste incinerator furnace as described in claim 2, characterized in that, The process of determining the defuzzified output data for each fuzzy rule based on temperature control error data and the controller's antecedent parameters includes: Based on temperature control error data and the antecedent parameters of the controller, the membership degree of each operated variable is determined; wherein, the antecedent parameters of the controller include the center and width of the membership function; Based on the membership degree, determine the output data for each fuzzy rule; The output data of each fuzzy rule is fuzzified to obtain the defuzzified output data corresponding to each fuzzy rule.

4. The method for controlling the temperature of an urban solid waste incinerator furnace as described in claim 2, characterized in that, Before determining the defuzzified output data for each fuzzy rule based on temperature control error data and the controller's antecedent parameters, the following steps are also included: Calculate the activation intensity of the neuron corresponding to each fuzzy rule; If the absolute value of the current temperature error is greater than the preset lower limit of the temperature error, and the activation intensity of the neuron is less than the preset first activation intensity, a new fuzzy rule is added. If the absolute value of the current temperature control error is greater than the preset upper limit of the temperature error, and the activation intensity of the neuron is greater than the preset second activation intensity, the current fuzzy rule is deleted.

5. The method for controlling the temperature of an urban solid waste incinerator furnace as described in claim 1, characterized in that, The determination of the temperature control error data includes: Set the target temperature value according to the actual needs of the target furnace; Collect the actual temperature value of the target furnace; Based on the target temperature value and the actual temperature value, temperature control error data is determined.

6. The method for controlling the temperature of an urban solid waste incinerator furnace as described in any one of claims 1-5, characterized in that, Determining that the control law satisfies the update condition includes: A temperature error vector is determined based on the temperature control error data. When the absolute value of an element in the temperature error vector is greater than or equal to a preset threshold, the control law is determined to meet the update condition.

7. A temperature control device for an urban solid waste incinerator furnace, characterized in that, include: The combustion control parameter acquisition module is used to acquire the combustion control parameters of the target furnace, wherein the combustion control parameters include at least one of primary air volume, secondary air volume, grate speed and pusher speed; The control law output module is used to input the temperature control error data of the target furnace into the controller model, using the combustion control parameters as the operating variables of the pre-built controller model, to generate the control law of the target furnace at the next moment; wherein, the temperature control error data includes the temperature control error, the first derivative of the temperature control error, and the second derivative of the temperature control error; the control law at the current moment is the change of the operating variables of the target furnace at the next moment relative to the current moment; The operation variable update module is used to determine whether the control law meets the update conditions, and update the operation variables at the current time according to the control law.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the urban solid waste incineration furnace temperature control method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the urban solid waste incineration furnace temperature control method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the urban solid waste incineration furnace temperature control method as described in any one of claims 1 to 6.

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