A temperature control method for a decomposition furnace under waste co-processing

Abstract

The application discloses a kind of decomposition furnace temperature control methods under garbage collaborative disposal, its steps include:1, establish the MISOHammerstein model of garbage collaborative disposal under decomposition furnace data driving;2, based on the outlet temperature control of decomposition furnace under garbage collaborative disposal of MISOHammerstein model.The application considers the multivariable, nonlinearity and pure time delay of decomposition furnace system under garbage collaborative disposal, and under the premise of guaranteeing the control effect of decomposition furnace outlet temperature, the degree of smoothness of control and the mixing ratio of garbage can be adjusted, so as to ensure the quality and yield of cement, and maximize the mixing ratio of garbage, achieve the purpose of garbage recycling and energy saving and emission reduction of cement industry.

Description

Technical Field

[0001] This invention relates to the field of decomposition furnace temperature modeling and control, and in particular to a method for controlling the temperature of a decomposition furnace in a cement kiln co-processing waste. Background Technology

[0002] The energy-intensive cement industry consumes a large amount of coal resources annually. To conserve coal and rationally dispose of the large amounts of urban waste, the co-processing of waste in cement kilns has attracted widespread interest. By using waste as a partial alternative fuel, burning it along with pulverized coal in a decomposition furnace, not only can fuel and raw materials be saved for the cement industry, but it can also process large quantities of municipal solid waste. Furthermore, the decomposition furnace provides a high-temperature, alkaline environment that ensures the complete decomposition of heavy metal ions from waste without residue emissions and the complete decomposition of toxic gases such as dioxins.

[0003] For decomposers using waste-to-calcium co-processing, outlet temperature is a crucial process parameter, affecting the system's operational stability as well as the quality and yield of cement clinker. The use of waste as fuel directly impacts the stability of the decomposer's outlet temperature, increasing the difficulty of control. This is due to the significant differences between waste and pulverized coal in terms of moisture content, fineness, and calorific value. Therefore, the models and control strategies for waste-to-calcium co-processing in decomposers require further in-depth research.

[0004] Currently, due to the characteristics of co-processing waste decomposition furnace systems, such as pure time delay, nonlinearity, and multiple variables, some cement companies rely on manual experience or traditional PID control methods to control the outlet temperature, resulting in high labor costs and poor control effectiveness. Other companies treat waste as merely a disturbance and adjust the coal consumption of the decomposition furnace in real time based on changes in the outlet temperature. This method struggles to ensure a stable waste input, and the outlet temperature control is also unsatisfactory. Poor outlet temperature control primarily affects the stable operation of the decomposition furnace, leading to a decrease in raw material decomposition rate, which in turn results in deterioration of cement clinker quality and reduced output. Summary of the Invention

[0005] The present invention addresses the shortcomings of the prior art by proposing a method for controlling the temperature of a decomposition furnace under co-processing of waste. This method aims to stably and accurately control the outlet temperature of the decomposition furnace when waste is added, thereby ensuring the stable operation of the decomposition furnace system and ultimately guaranteeing the quality and yield of cement clinker.

[0006] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0007] The method for controlling the temperature of a decomposer in co-processing waste according to the present invention is characterized by the following steps:

[0008] Step 1: Real-time acquisition of on-site data {T(k), F} from the decomposition furnace under waste co-processing. c (k), F R (k)|k=1,...,N};where T(k) represents the original value of the decomposition furnace outlet temperature at time k, F c (k) represents the initial value of the coal feed rate of the decomposer at time k, F R (k) represents the original value of the waste flow rate in the decomposer at time k;

[0009] Step 2: Use the moving average filtering method to filter the field data {T(k), F} c (k), F R Preprocessing is performed on the data from the decomposer furnace (k)|k=1,...,N} to obtain filtered data. in, This represents the filtered outlet temperature value of the decomposition furnace at time k. This represents the filtered value of the coal feed rate in the decomposer at time k. This represents the filtered waste flow rate of the decomposer at time k;

[0010] Step 3: Based on the filtered precalciner field data, establish the MISO Hammerstein model:

[0011] Step 3.1: Construct a MISO Hammerstein model consisting of a static nonlinear element and a dynamic linear element connected in series. Use the coal feed rate u1(k) and the waste flow rate u2(k) at time k as the two inputs of the MISO Hammerstein model, and use the outlet temperature y(k) at time k as the output of the MISO Hammerstein model.

[0012] The static nonlinear element is characterized using equations (1) and (2):

[0013]

[0014]

[0015] In equation (1), v1(k) is the intermediate variable at time k between u1(k) and y(k), and r i Let L1 be the coefficient of the i-th term of u1(k), and L1 be the highest degree of u1(k).

[0016] In equation (2), v2(k) is the intermediate variable at time k between u2(k) and y(k), and s i Let L2 be the coefficient of the i-th term of u2(k), and L2 be the highest degree of u2(k).

[0017] The dynamic linear element is characterized using the ARMAX model shown in equation (3):

[0018] A(z -1 )y(k)=z -τ D(z -1 )v(k)+ε(k) (3)

[0019] In equation (3), z -1 For delay operators, it means lag by 1 step; This represents the output coefficient polynomial of the linear element. These are the output coefficient polynomials from 1 to n. a The coefficient of the term, n a Indicates the output order; This represents the input coefficient polynomial of the linear element. The input coefficient polynomials are 1 to n respectively. d The coefficient of the term, n d Indicates the input order; v(k) = [v1(k), v2(k)] T Let z denote the intermediate vector at time k, and T denote the transpose; ε(k) denote the noise term at time k; -τ This indicates a lag of τ steps, where τ is the number of lag steps.

[0020] Step 3.2: Identify the parameters of the MISO Hammerstein model;

[0021] Initialize the structural parameters of the MISO Hammerstein model, including: output order n. a Input order n d The lag steps τ, the highest number of u1(k) L1 and the highest number of u2(k) L2, and L1 and L2 are odd numbers;

[0022] The filtered decomposition furnace field data is input into the MISO Hammerstein model, and the remaining parameters of the model are identified by recursive least squares method and parameter separation method, including the coefficients of the output polynomial of the linear element. The coefficients of the input coefficient polynomial in the linear circuit The coefficients of the nonlinear element u1(k) The coefficients of the nonlinear element u2(k)

[0023] Step 4: Control of the decomposer outlet temperature at time k under waste co-processing based on the MISO Hammerstein model;

[0024] Step 4.1: For the dynamic linear element, solve for the intermediate vector v(k) at time k using the generalized predictive control algorithm:

[0025] Step 4.2: For nonlinear components, solve for the control quantity using the intermediate vector:

[0026] Step 4.2.1: Substitute the intermediate vector obtained by equation (9) into equations (1) and (2), and obtain two sets of roots by solving two univariate higher-order equations;

[0027] Step 4.2.2: Remove the virtual roots from the roots obtained in Step 4.2.1, and keep only the real roots;

[0028] If each group of roots ultimately retains only one real root, then the real root in the corresponding group of roots is assigned to the coal feeding amount u1(k) at time k and the garbage flow rate u2(k) at time k.

[0029] If each group of roots retains two or more real roots, then select the corresponding real root that is closest to the coal feed amount u1(k-1) and the waste flow rate u2(k-1) at time k-1, and assign them to the coal feed amount u1(k) and the waste flow rate u2(k) at time k.

[0030] Step 4.3: Based on the upper and lower limits of coal feed, the upper and lower limits of coal feed increment, the upper and lower limits of waste flow, and the upper and lower limits of waste flow increment, process the coal feed u1(k) and waste flow u2(k), and then send the constrained coal feed u1(k) and waste flow u2(k) to the industrial field decomposition furnace control system to control the operation of the decomposition furnace at time k.

[0031] Step 5: After assigning k+1 to k, return to step 4 to execute the control at the next moment.

[0032] The method for controlling the temperature of the decomposer under waste co-processing described in this invention is also characterized in that step 4.1 is performed as follows:

[0033] Step 4.1.1: Construct a prediction model for the outlet temperature of the decomposition furnace using equation (4):

[0034]

[0035] In equation (4), j = N1, ..., N2 represents the number of advance prediction steps, and N1 = τ+1 and N2 = τ+N p N represents the start and end points of the prediction time domain, respectively. p To predict the step size; Let G represent the predicted value of the decomposer outlet temperature at time k+j; Δv(k+j-τ) represents the intermediate vector increment at time k+j-τ, and Δv(k-1) represents the intermediate vector increment at time k-1; j (z -1 ), F j (z -1 H j (z-1 ) are respectively about z -1 A polynomial of G, and the coefficients of each polynomial are obtained by solving the Diophantine equation; j (z -1 F represents the coefficient of Δv(k+j-τ) at time k+j. j (z -1 H represents the coefficient of y(k) at time k+j. j (z -1 ) represents the coefficient of Δv(k-1) at time k+j;

[0036] Step 4.1.2: When j = N1,...,N2, from N p The outlet temperature prediction model yields the predicted outlet temperature sequence of the decomposer as shown in equation (5).

[0037]

[0038] In equation (5), V = [△v(k),...,△v(k+N)] u -1)] T N represents the predicted sequence of intermediate vector increments. u G represents the control step size; G is the coefficient matrix of V; F is the coefficient vector of y(k); H is the coefficient vector of Δv(k-1);

[0039] Step 4.1.3: Use equation (6) to obtain the softened value y of the desired decomposition furnace outlet temperature at time k+j. r (k+j), thus when j=N1,...,N2, by N p The softening values ​​constitute the softening sequence Y of the desired decomposition furnace outlet temperature. r ;

[0040]

[0041] In equation (6), α represents the softening coefficient, and 0 < α < 1; y sp This represents the expected value of the decomposer outlet temperature;

[0042] Step 4.1.4: Construct the performance index of generalized predictive control using equation (7):

[0043]

[0044] In equation (7), Λ represents the control weighting matrix;

[0045] Step 4.1.5: Substitute equations (5) and (6) into equation (7) to obtain the predicted sequence V of the intermediate vector increment using equation (8):

[0046] V = (GT G+Λ) -1 G T [Y r -Fy(k)-H△v(k-1)] (8)

[0047] Step 4.1.6: Take the first two rows of V in equation (8) as the intermediate vector increment Δv(k) at time k;

[0048] Step 4.1.7: Use equation (9) to obtain the intermediate vector v(k) at time k:

[0049] v(k)=v(k-1)+△v(k) (9)

[0050] The present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the method, and the processor is configured to execute the program stored in the memory.

[0051] The present invention discloses a computer-readable storage medium on which a computer program is stored, wherein the computer program is executed by a processor to perform the steps of the method.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] 1. This invention collects on-site decomposition furnace data and uses data to drive theory to establish the MISO Hammerstein model. Compared with the traditional linear model, this model can better describe the pure time delay and nonlinearity of the decomposition furnace system and is closer to the real decomposition furnace system.

[0054] 2. This invention employs a two-step predictive control method based on the MISO Hammerstein model, enabling the decomposer outlet temperature to exhibit excellent tracking performance to the setpoint, with adjustable stability. Simultaneously, under stable conditions, the ratio of waste flow rate to coal feed rate can be adjusted, thereby achieving energy conservation and emission reduction. Attached Figure Description

[0055] Figure 1 This is a diagram showing the original data of the decomposer under the waste co-processing method in this embodiment;

[0056] Figure 2 This is a schematic diagram of the temperature control principle of the decomposition furnace under the co-processing of waste in this invention;

[0057] Figure 3 This diagram illustrates the temperature control effect of the decomposition furnace under the co-processing of waste in this embodiment. Detailed Implementation

[0058] In this embodiment, a method for controlling the temperature of a decomposition furnace under waste co-processing is performed according to the following steps:

[0059] Step 1: Real-time acquisition of on-site data {T(k), F} from the decomposition furnace under waste co-processing. c (k), F R (k)|k=1,...,N};where T(k) represents the original value of the decomposition furnace outlet temperature at time k, F c (k) represents the initial value of the coal feed rate of the decomposer at time k, F R (k) represents the original waste flow rate of the decomposition furnace at time k; this embodiment collected 800 sets of data from a 5000t / d cement clinker production line of a cement plant in Anhui Province on August 12, 2022, with a sampling period of 5 seconds, such as Figure 1 As shown;

[0060] Step 2: Due to the harsh industrial environment and numerous interference sources, errors inevitably occur in the data during acquisition and transmission. Therefore, the data from Step 1 needs to be filtered to reduce the impact of interference. The moving average filtering method is used to filter the field data {T(k), F...} c (k), F R Preprocessing is performed on the data from the decomposer furnace (k)|k=1,...,N} to obtain filtered data. in, This represents the filtered outlet temperature value of the decomposition furnace at time k. This represents the filtered value of the coal feed rate in the decomposer at time k. This represents the filtered waste flow rate of the decomposer at time k;

[0061] Step 3: Based on the filtered precalciner field data, establish the MISO Hammerstein model:

[0062] Step 3.1: Construct a MISO Hammerstein model consisting of a static nonlinear element and a dynamic linear element connected in series. Use the coal feed rate u1(k) and the waste flow rate u2(k) at time k as the two inputs of the MISO Hammerstein model, and use the outlet temperature y(k) at time k as the output of the MISO Hammerstein model.

[0063] Static nonlinear elements are characterized using equations (1) and (2):

[0064]

[0065]

[0066] In equation (1), v1(k) is the intermediate variable at time k between u1(k) and y(k), and ri Let L1 be the coefficient of the i-th term of u1(k), and L1 be the highest degree of u1(k).

[0067] In equation (2), v2(k) is the intermediate variable at time k between u2(k) and y(k), and s i Let L2 be the coefficient of the i-th term of u2(k), and L2 be the highest degree of u2(k).

[0068] The dynamic linear element is represented using the ARMAX model shown in equation (3):

[0069] A(z -1 )y(k)=z -τ D(z -1 )v(k)+ε(k) (3)

[0070] In equation (3), z -1 For delay operators, it means lag by 1 step; This represents the output coefficient polynomial of the linear element. These are the output coefficient polynomials from 1 to n. a The coefficient of the term, n a Indicates the output order; This represents the input coefficient polynomial of the linear element. The input coefficient polynomials are 1 to n respectively. d The coefficient of the term, n d Indicates the input order; v(k) = [v1(k), v2(k)] T Let z denote the intermediate vector at time k, and T denote the transpose; ε(k) denote the noise term at time k; -τ This indicates a lag of τ steps, where τ is the number of lag steps.

[0071] Step 3.2: Identify the parameters of the MISO Hammerstein model;

[0072] Step 3.2.1: Initialize the structural parameters of the MISO Hammerstein model, including: output order n a Input order n d The lag steps τ, the highest number of u1(k) L1, and the highest number of u2(k) L2 are defined, and L1 and L2 are odd numbers; in this embodiment, let L1 = 3, L2 = 3, and n a =2, n d =2, τ=2;

[0073] Step 3.2.2, let D(z) -1 In the coefficients of ), d1 = [b1,c1] and d2 = [0,c2]. Equation (3) is transformed into the form required by the least squares algorithm as shown in equation (4).

[0074]

[0075] In equation (4),

[0076]

[0077] θ=[a1,a2,b1r0,b1r1,b1r2,b1r3,c1s0,c1s1,c1s2,c1s3,c2s0,c2s1,c2s2,c2s3] T

[0078] Let θ be the input and output vectors, and θ be the parameter vector to be identified.

[0079] Step 3.2.3: Input the filtered decomposition furnace field data from step 2 into equation (4), and obtain the optimal estimate of θ by recursively using the least squares formula shown in equation (5);

[0080]

[0081] In equation (5), K(k), P(k) represents the gain matrix, parameter estimation matrix, and error covariance matrix, respectively; the initial parameter P(0) = α required for recursive calculation is calculated. 2 I, Where α is a large real number, δ is a small vector, and their dimensions are the same as θ; in this embodiment, α = 10. 3 δ=0.001×[1,...,1] T ;

[0082] The MISO Hammerstein model parameters a1 and a2 can be directly obtained from θ obtained in steps 3.2.4 and 3.2.3, but there is coupling between b and r, and between c and s. These are separated by singular value decomposition.

[0083] In this embodiment, the remaining parameters of the MISO Hammerstein model were finally identified as follows: a1 = -1.9751, a2 = 0.9816; d1 = [1, 0.7074], d2 = [0, 0.7068]; r0 = 1.8673, r1 = 0.0308, r2 = -0.0038, r3 = 1.518 × 10⁻⁶. -4 ;s0=2.6407, s1=0.0294, s2=-0.0023, s3=5.043×10 -5 .

[0084] Step 3.2.5: Substitute the parameters initialized in step 3.2.1 and the parameters identified in step 3.2.4 into equations (1) to (3) to obtain the MISO Hammerstein model. Perform a curve fitting test on the model. If the test requirements are met, it means that the final MISO Hammerstein model has been obtained and can be used as the decomposition furnace outlet temperature model under waste co-processing. In this embodiment, the fitting coefficient is 0.9993, which meets the test requirements.

[0085] Step 4: Control of the decomposer outlet temperature at time k under waste co-processing based on the MISO Hammerstein model;

[0086] The control principle of this invention is as follows: Figure 2 As shown, the predicted sequence of the outlet temperature is obtained through the prediction model, and the softened sequence of the outlet temperature setpoint is obtained through the reference trajectory. The difference between the two and the weighted average of the intermediate vector are used to construct a quadratic performance index. The intermediate vector is obtained through the generalized predictive control algorithm. The intermediate vector is then substituted back into the nonlinear model to solve for the coal feed rate and waste flow rate. The calculated coal feed rate and waste flow rate are processed by upper and lower limit constraints and incremental constraints, and then sent to the industrial field control system to control the outlet temperature of the decomposition furnace.

[0087] Step 4.1: For the dynamic linear element, solve for the intermediate vector v(k) at time k using the generalized predictive control algorithm:

[0088] Step 4.1.1: Construct a prediction model for the outlet temperature of the decomposition furnace using equation (6):

[0089]

[0090] In equation (6), j = N1, ..., N2 represents the number of advance prediction steps, and N1 = τ+1 and N2 = τ+N p N represents the start and end points of the prediction time domain, respectively. p To predict the step size; Let G represent the predicted value of the decomposer outlet temperature at time k+j; Δv(k+j-τ) represents the intermediate vector increment at time k+j-τ, and Δv(k-1) represents the intermediate vector increment at time k-1; j (z -1 ), F j (z -1 H j (z -1 ) are respectively about z -1 A polynomial of G, and the coefficients of each polynomial are obtained by solving the Diophantine equation; j (z -1 F represents the coefficient of Δv(k+j-τ) at time k+j. j (z-1 H represents the coefficient of y(k) at time k+j. j (z -1 ) represents the coefficient of Δv(k-1) at time k+j;

[0091] Step 4.1.2: When j = N1,...,N2, from N p The outlet temperature prediction model yields the predicted outlet temperature sequence of the decomposer as shown in equation (7).

[0092]

[0093] In equation (7), V = [△v(k),...,△v(k+N)] u -1)] T N represents the predicted sequence of intermediate vector increments. u G represents the control step size; G is the coefficient matrix of V; F is the coefficient vector of y(k); H is the coefficient vector of Δv(k-1);

[0094] Step 4.1.3: Use equation (8) to obtain the softened value y of the desired decomposition furnace outlet temperature at time k+j. r (k+j), thus when j=N1,...,N2, by N p The softening values ​​constitute the softening sequence Y of the desired decomposition furnace outlet temperature. r ;

[0095]

[0096] In equation (8), α represents the softening coefficient, and 0 < α < 1; y sp This represents the expected value of the decomposer outlet temperature;

[0097] Step 4.1.4: Construct the performance index of generalized predictive control using equation (9):

[0098]

[0099] In equation (9), Λ represents the control weighting matrix;

[0100] Step 4.1.5: Substitute equations (7) and (8) into equation (9) to obtain the predicted sequence V of the intermediate vector increment using equation (10):

[0101] V = (G T G+Λ) -1 G T [Y r -Fy(k)-H△v(k-1)] (10)

[0102] Step 4.1.6: Take the first two rows of V in equation (10) as the intermediate vector increment Δv(k) at time k;

[0103] Step 4.1.7: Use equation (11) to obtain the intermediate vector v(k) at time k:

[0104] v(k)=v(k-1)+△v(k) (11)

[0105] Step 4.2: For nonlinear components, solve for the control quantity using the intermediate vector:

[0106] Step 4.2.1: Substitute the intermediate vector obtained by equation (11) into equations (1) and (2), and obtain two sets of roots by solving two univariate higher-order equations;

[0107] Step 4.2.2: For the roots obtained in Step 4.2.1, remove the virtual roots and keep only the real roots;

[0108] Step 4.2.3: If only one real root is retained in each group in step 4.2.2, then this real root is assigned to the coal feed amount u1(k) and the waste flow rate u2(k) at time k; if two or more real roots are retained in each group in step 4.2.2, then the corresponding real root that is closest to the coal feed amount u1(k-1) and the waste flow rate u2(k-1) at time k-1 is selected and assigned to the coal feed amount u1(k) and the waste flow rate u2(k) at time k.

[0109] Step 4.3: Based on the upper and lower limits of coal feed, the upper and lower limits of coal feed increment, the upper and lower limits of waste flow, and the upper and lower limits of waste flow increment, process the coal feed u1(k) and waste flow u2(k), and then send the constrained coal feed u1(k) and waste flow u2(k) to the industrial field decomposition furnace control system to control the operation of the decomposition furnace at time k.

[0110] Step 5: After assigning k+1 to k, return to step 4 to execute the control at the next moment.

[0111] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0112] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

[0113] In this embodiment, the desired temperature of the decomposition furnace is set to 885℃ in step 51 and 878℃ ​​in step 251. The control effect and the adjustment of coal feed rate and waste flow rate are as follows. Figure 3 As shown. During the control process, adjusting the softening coefficient can regulate the stability of the control. Specifically, increasing the softening coefficient will make the outlet temperature of the decomposition furnace track the set value more stably, but the time required for the outlet temperature to reach a stable value will also be longer. Adjusting the control weighting matrix can regulate the proportion of waste mixed in. Specifically, increasing the weighting coefficient of the coal feed amount in the control weighting matrix will limit the increase of the coal feed amount, thereby increasing the proportion of waste mixed in, ultimately achieving the goals of waste recycling and energy conservation and emission reduction in the cement industry.

Claims

1. A method for temperature control of a decomposition furnace under waste co-processing, characterized in that, The procedure is as follows: Step 1: Real-time acquisition of on-site data from the decomposition furnace under the co-processing of waste. ;in, This represents the initial outlet temperature of the decomposition furnace at time k. This represents the initial value of the coal feed rate to the decomposer at time k. This represents the original waste flow rate of the decomposer at time k; Step 2: Use the moving average filtering method to process the field data. Preprocessing is performed to obtain filtered data from the decomposition furnace. ;in, This represents the filtered outlet temperature value of the decomposition furnace at time k. This represents the filtered value of the coal feed rate in the decomposer at time k. This represents the filtered waste flow rate of the decomposer at time k; Step 3: Based on the filtered precalciner field data, establish the MISO Hammerstein model: Step 3.1: Construct a MISO Hammerstein model consisting of a static nonlinear element and a dynamic linear element connected in series, and use the coal feed rate at time k. Garbage flow at time k As two inputs to the MISO Hammerstein model, the outlet temperature at time k As the output of the MISO Hammerstein model; The static nonlinear element is characterized using equations (1) and (2): (1) (2) In equation (1), for and The intermediate variable at time k, for of coefficient of the secondary term, for The highest number of times; In equation (2), for and The intermediate variable at time k, for of coefficient of the secondary term, for The highest number of times; The dynamic linear element is characterized using the ARMAX model shown in equation (3): (3) In equation (3), For delay operators, it means lag by 1 step; This represents the output coefficient polynomial of the linear element. These are the output coefficient polynomials. coefficient of the secondary term, Indicates the output order; This represents the input coefficient polynomial of the linear element. The input coefficient polynomials are respectively coefficient of the secondary term, Indicates the input order; Let represent the intermediate vector at time k, and T represent the transpose; Represents the noise term at time k; Indicates lag step, The number of lag steps; Step 3.2: Identify the parameters of the MISO Hammerstein model; Initialize the structural parameters of the MISO Hammerstein model, including: output order. Input order Lag steps , The highest number and The highest number ,and and It is an odd number; The filtered decomposition furnace field data is input into the MISO Hammerstein model, and the remaining parameters of the model are identified by recursive least squares method and parameter separation method, including the coefficients of the output polynomial of the linear element. The coefficients of the input polynomial in the linear process Nonlinear elements coefficient Nonlinear elements coefficient ; Step 4: Control of the decomposer outlet temperature at time k under waste co-processing based on the MISO Hammerstein model; Step 4.1: For the dynamic linear element, solve for the intermediate vector at time k using the generalized predictive control algorithm. : Step 4.2: For nonlinear components, solve for the control quantity using the intermediate vector: Step 4.2.1: Substitute the intermediate vector obtained in step 4.1 into equations (1) and (2), and obtain two sets of roots by solving two univariate higher-order equations; Step 4.2.2: Remove the virtual roots from the roots obtained in Step 4.2.1, and keep only the real roots; If each set of roots ultimately retains only one real root, then the real root from the corresponding set of roots is assigned to... Coal feeding amount at all times and Garbage flow at all times ; If each root group retains two or more real roots, then the closest one is selected. Coal feeding amount at all times and Garbage flow at all times The corresponding real roots are assigned to Coal feeding amount at all times and Garbage flow at all times ; Step 4.3: Based on the upper and lower limits of coal feed rate, the upper and lower limits of coal feed rate increment, the upper and lower limits of waste flow rate, and the upper and lower limits of waste flow rate increment, adjust the coal feed rate... and garbage flow This process is performed to control the amount of coal fed. and garbage flow The command is sent to the industrial site decomposition furnace control system to control the operation of the decomposition furnace at time k. Step 5: After assigning k+1 to k, return to step 4 to execute the control at the next moment.

2. The method for controlling the temperature of a decomposer in co-processing waste according to claim 1, characterized in that, Step 4.1 is performed as follows: Step 4.1.1: Construct a prediction model for the outlet temperature of the decomposition furnace using equation (4): (4) In equation (4), This indicates the number of steps in the advance prediction. and These represent the start and end points of the prediction time domain, respectively. To predict the step size; express Predicted value of decomposition furnace outlet temperature at any time; express The increment of the intermediate vector at time step, express The increment of the intermediate vector at time step; They are respectively about The polynomials are given by the Diophantine equation, and the coefficients of each polynomial are obtained by solving the Diophantine equation. express time coefficient, express time coefficient, express time The coefficient; Step 4.1.2, when At that time, by The outlet temperature prediction model yields the predicted outlet temperature sequence of the decomposer as shown in equation (5). : (5) In equation (5), The predicted sequence representing the intermediate vector increment. This represents the control step size; G is the coefficient matrix of V; for The coefficient vector; H is The coefficient vector; Step 4.1.3, using equation (6) to obtain The softening value of the expected value of the decomposition furnace outlet temperature at any time Thus in At that time, by The softening values ​​constitute a softening sequence for the desired decomposition furnace outlet temperature. ; (6) In equation (6), This represents the softening coefficient, and ; This represents the expected value of the decomposer outlet temperature; Step 4.1.4: Construct the performance index of generalized predictive control using equation (7): (7) In equation (7), Represents the control weighting matrix; Step 4.1.5: Substitute equations (5) and (6) into equation (7) to obtain the predicted sequence of intermediate vector increments using equation (8). : (8) Step 4.1.6, take the formula (8) The first two lines as Increment of intermediate vector at time step ; Step 4.1.7: Obtain the result using equation (9). Intermediate vector at time : (9)。 3. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports a processor in executing the method of claim 1 or 2, the processor being configured to execute the program stored in the memory.

4. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program is executed by the processor to perform the steps of the method of claim 1 or 2.

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