CN116382371A - Decomposing furnace temperature control method under garbage co-treatment - Google Patents
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Abstract
The invention discloses a decomposing furnace temperature control method under the cooperative disposal of garbage, which comprises the following steps: 1. establishing a decomposing furnace data-driven MISOHOAMmerstein model under the garbage co-treatment; 2. decomposing furnace outlet temperature control under garbage co-treatment based on MISOHOAMmerstein model. The invention considers the multivariable, nonlinear and pure time delay of the decomposing furnace system under the cooperative treatment of the garbage, and can adjust the control stability and the garbage mixing proportion on the premise of ensuring the temperature control effect of the outlet of the decomposing furnace, thereby not only ensuring the quality and the yield of cement, but also maximizing the garbage mixing proportion and achieving the purposes of garbage recycling and energy conservation and emission reduction in the cement industry.
Description
Technical Field
The invention relates to the field of decomposing furnace temperature modeling and control, in particular to a decomposing furnace temperature control method under the condition that cement kiln co-handles garbage.
Background
The cement industry with high energy consumption consumes a large amount of coal resources each year, and in order to save the coal resources and reasonably dispose and utilize a large amount of garbage wastes generated by towns, cement kiln systems cooperatively dispose garbage, which is of great interest. The garbage is taken as partial alternative fuel and pulverized coal to be sent into the human decomposing furnace for combustion and heat release, so that fuel and partial raw materials can be saved for the cement industry, and a large amount of household garbage can be treated. Furthermore, the decomposing furnace has a high-temperature alkaline environment, so that no waste residue is discharged by heavy metal ions in the garbage, and toxic gases such as dioxin can be thoroughly decomposed.
For a decomposing furnace with co-disposal of waste, the outlet temperature is a very important process parameter which affects the operational stability of the decomposing furnace system and the quality and yield of cement clinker. The input of the garbage as the fuel can directly influence the stability of the outlet temperature of the decomposing furnace, and the control difficulty is increased, and the reason is that the garbage and the pulverized coal have great differences in the aspects of moisture content, fineness and heat value. Therefore, models and control strategies for the co-disposal of refuse by the decomposing furnace are under intense investigation.
At present, as the decomposing furnace system for cooperatively disposing garbage has the characteristics of pure hysteresis, nonlinearity, multiple variables and the like, some cement enterprises control the outlet temperature based on manual experience or a traditional PID control method, and the consumed labor cost is high and the control effect is poor. Some enterprises only consider the garbage as the interference amount, and the coal amount for the decomposing furnace is regulated in real time according to the change of the outlet temperature of the decomposing furnace, so that the method is difficult to ensure the stable input of the garbage, and the control effect of the outlet temperature is also difficult to be satisfactory. The poor control effect of the outlet temperature firstly affects the stable operation of the decomposing furnace, so that the decomposition rate of raw materials is reduced, and further, the quality degradation and the yield reduction of cement clinker are caused.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a decomposing furnace temperature control method under the cooperative treatment of garbage, so that the outlet temperature of the decomposing furnace can be stably and accurately controlled when garbage is added, the stable operation of a decomposing furnace system is ensured, and the quality and the yield of cement clinker are further ensured.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the invention relates to a decomposing furnace temperature control method under the cooperative disposal of garbage, which is characterized by comprising the following steps:
step 1, acquiring site data { T (k), F of a decomposing furnace under the cooperative disposal of garbage in real time c (k)，F R (k) I k=1, & N; wherein T (k) represents the original value of the outlet temperature of the decomposing furnace at time k, F c (k) Representing the original value of the coal feeding quantity of the decomposing furnace at the moment k, F R (k) The original value of the garbage flow of the decomposing furnace at the moment k is represented;
step 2, utilizing a sliding average filtering method to carry out on field data { T (k), F c (k)，F R (k) Pre-processing of i k=1,..n } to obtain filtered decomposing furnace field data
Wherein (1)>
Represents the outlet temperature filter value of the decomposing furnace at time k, < >>
Representing the filtering value of the coal feeding quantity of the decomposing furnace at the moment k, < >>
The garbage flow filtering value of the decomposing furnace at the moment k is represented;
step 3, based on the filtered decomposing furnace field data, establishing a MISO Hammerstein model:
step 3.1, constructing a MISO Hammerstein model formed by connecting a static nonlinear link and a dynamic linear link in series, and feeding coal quantity u at k moment 1 (k) And the garbage flow u at the moment k 2 (k) As two inputs of the MISO Hammerstein model, the outlet temperature y (k) at the time of k is taken as the output of the MISO Hammerstein model;
characterizing the static nonlinear element using formula (1) and formula (2):
in the formula (1), v 1 (k) Is u 1 (k) And y (k), r i Is u 1 (k) I-th order term coefficient of L 1 Is u 1 (k) Is the highest number of times;
in the formula (2), v 2 (k) Is u 2 (k) And an intermediate variable at time k between y (k), s i Is u 2 (k) I-th order term coefficient of L 2 Is u 2 (k) Is the highest number of times;
characterizing the dynamic linear element using an ARMAX model of formula (3):
A(z -1 )y(k)＝z -τ D(z -1 )v(k)+ε(k) (3)
in formula (3), z -1 For a delay operator, representing a lag of 1 step;
representing a linear element output coefficient polynomial, +.>
Respectively 1 to n of output coefficient polynomials a Sub-term coefficients, n a Representing the output order; />
Representing a linear element input coefficient polynomial, +.>
Respectively 1 to n of input coefficient polynomials d Sub-term coefficients, n d Representing an input order; v (k) = [ v 1 (k),v 2 (k)] T An intermediate vector at time k, T representing the transpose; epsilon (k) represents the noise term at time k; z -τ Indicating lag tau steps, wherein tau is the number of lag steps;
step 3.2, identifying parameters of the MISO Hammerstein model;
initializing structural parameters of the MISO Hammerstein model, including: output order n a Input order n d Number of steps by hysteresis τ, u 1 (k) The highest number L of times 1 And u 2 (k) The highest number L of times 2 And L is 1 And L 2 Is odd;
inputting the filtered decomposing furnace field data into the MISO Hammerstein model, and identifying the residual parameters of the model by a recursive least square method and a parameter separation method, wherein the method comprises the following steps of: coefficient of linear link output coefficient polynomial
Coefficients of a linear element input coefficient polynomial +.>
Nonlinear link u 1 (k) Coefficient of->
Nonlinear link u 2 (k) Coefficient of->
Step 4, controlling the outlet temperature of the decomposing furnace at the moment k under the cooperative disposal of garbage based on the MISO Hammerstein model;
step 4.1, solving an intermediate vector v (k) at the moment k by a generalized predictive control algorithm aiming at a dynamic linear link:
step 4.2, solving the control quantity by the intermediate vector aiming at the nonlinear link:
step 4.2.1, substituting the intermediate vector obtained in the formula (9) into the formulas (1) and (2), and obtaining two groups of roots by solving two unitary high-order equations;
step 4.2.2, removing the virtual roots in the roots solved in the step 4.2.1, and only retaining the real roots;
if each group of roots finally only retains one real root, the real root in the corresponding group of rootsGiving the coal feeding quantity u at the moment k 1 (k) And the garbage flow u at the moment k 2 (k)；
If each group of roots keeps two or more real roots, selecting the coal feeding quantity u closest to k-1 moment 1 Garbage flow u at times (k-1) and (k-1) 2 Corresponding solid root of (k-1) and giving the coal feeding quantity u at the moment k 1 (k) And the garbage flow u at the moment k 2 (k)；
Step 4.3, feeding coal quantity u according to the upper and lower limit constraints of the coal feeding quantity, the upper and lower limit constraints of the increment of the coal feeding quantity, the upper and lower limit constraints of the garbage flow quantity and the upper and lower limit constraints of the increment of the garbage flow quantity 1 (k) And garbage flow u 2 (k) Processing to obtain constrained coal feeding quantity u 1 (k) And garbage flow u 2 (k) Issuing to an industrial field decomposing furnace control system to control the operation of the decomposing furnace at the moment k;
and 5, after k+1 is assigned to k, returning to the step 4 to execute the next time control.
The decomposing furnace temperature control method under the garbage cooperative treatment is also characterized in that the step 4.1 is carried out according to the following steps:
step 4.1.1, constructing an outlet temperature prediction model of the decomposing furnace by using the formula (4):
in formula (4), j=n 1 ,...,N 2 Indicating the number of steps of the advance prediction, N 1 =τ+1 and N 2 ＝τ+N p Respectively a starting point and an ending point of a prediction time domain, N p Is the prediction step length;
the predicted value of the outlet temperature of the decomposing furnace at the moment k+j is shown; deltav (k+j- τ) represents the intermediate vector increment at time k+j- τ, deltav (k-1) represents the intermediate vector increment at time k-1; g j (z -1 )、F j (z -1 )、H j (z -1 ) Respectively about z -1 And the coefficients of each polynomial are represented by the Dipsilon equationSolving to obtain; g j (z -1 ) A coefficient representing Deltav (k+j- τ) at time k+j, F j (z -1 ) A coefficient indicating y (k) at time k+j, H j (z -1 ) A coefficient representing Deltav (k-1) at time k+j;
step 4.1.2, when j=n 1 ,...,N 2 When by N p Obtaining an outlet temperature prediction sequence of the decomposing furnace shown in the formula (5) by using the group outlet temperature prediction model
In formula (5), v= [ Δv (k),. The term,. Δv (k+n) u -1)] T Prediction sequence representing intermediate vector increment, N u Representing a control step size; g is a coefficient matrix of V; f is a coefficient vector of y (k); h is a coefficient vector of Deltav (k-1);
step 4.1.3, obtaining a softening value y of the expected value of the outlet temperature of the decomposing furnace at the moment k+j by using the step (6) r (k+j) such that at j=n 1 ,...,N 2 When by N p The softening values form a softening sequence Y of expected values of the outlet temperature of the decomposing furnace r ；
In formula (6), α represents a softening coefficient, and 0<α<1；y sp Indicating the expected value of the outlet temperature of the decomposing furnace;
step 4.1.4, constructing a performance index of generalized predictive control by using a formula (7):
in the formula (7), Λ represents a control weight matrix;
step 4.1.5, substituting the formula (5) and the formula (6) into the formula (7), thereby obtaining a predicted sequence V of intermediate vector increments by using the formula (8):
V＝(G T G+Λ) -1 G T [Y r -Fy(k)-H△v(k-1)] (8)
step 4.1.6, taking the first two rows of V in the formula (8) as the intermediate vector increment delta V (k) at the moment k;
step 4.1.7, obtaining an intermediate vector v (k) at the time of k by using the method (9):
v(k)＝v(k-1)+△v(k) (9)
the invention provides an electronic device comprising a memory and a processor, characterized in that the memory is arranged to store a program supporting the processor to execute the method, the processor being arranged to execute the program stored in the memory.
The invention relates to a computer-readable storage medium, on which a computer program is stored, characterized in that the computer program is executed by a processor to perform the steps of the method.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, on-site decomposing furnace data are collected, a MISO Hammerstein model is established according to a data driving theory, and compared with a traditional linear model, the model can well describe the pure time lag and nonlinearity of a decomposing furnace system, and is closer to a real decomposing furnace system;
2. the invention adopts a two-step prediction control method based on the MISO Hammerstein model, so that the outlet temperature of the decomposing furnace has good tracking performance for a set value, and the tracking stability degree can be adjusted. Meanwhile, on the premise of stability, the proportion of the garbage flow and the coal feeding amount can be regulated, so that the purposes of energy conservation and emission reduction are achieved.
Drawings
Fig. 1 is a diagram of raw data of a decomposing furnace under the co-disposal of garbage according to the embodiment;
FIG. 2 is a schematic diagram of the temperature control of the decomposing furnace under the co-disposal of the garbage according to the present invention;
fig. 3 is a graph showing the effect of controlling the temperature of the decomposing furnace under the co-disposal of the garbage according to the present embodiment.
Detailed Description
In this embodiment, a method for controlling the temperature of a decomposing furnace under the cooperative disposal of garbage is performed according to the following steps:
step 1, acquiring site data { T (k), F of a decomposing furnace under the cooperative disposal of garbage in real time c (k)，F R (k) I k=1, & N; wherein T (k) represents the original value of the outlet temperature of the decomposing furnace at time k, F c (k) Representing the original value of the coal feeding quantity of the decomposing furnace at the moment k, F R (k) The original value of the garbage flow of the decomposing furnace at the moment k is represented; in the embodiment, 800 groups of data of a 5000t/d cement clinker production line 2022 of a cement plant of Anhui province on the same day of 8 months and 12 days are collected, and the sampling period is 5 seconds, as shown in fig. 1;
and 2, because the industrial field environment is bad and the interference sources are more, errors are inevitably generated in the data acquisition and transmission process, so that the data in the step 1 are subjected to filtering processing, and the influence of interference information is reduced. On-site data { T (k), F using sliding mean filtering c (k)，F R (k) Pre-processing of i k=1,..n } to obtain filtered decomposing furnace field data
Wherein (1)>
Represents the outlet temperature filter value of the decomposing furnace at time k, < >>
Representing the filtering value of the coal feeding quantity of the decomposing furnace at the moment k, < >>
The garbage flow filtering value of the decomposing furnace at the moment k is represented;
step 3, based on the filtered decomposing furnace field data, establishing a MISO Hammerstein model:
step 3.1, constructing a MISO Hammerstein model formed by connecting a static nonlinear link and a dynamic linear link in series,and the coal feeding quantity u at k moment 1 (k) And the garbage flow u at the moment k 2 (k) As two inputs of the MISO Hammerstein model, the outlet temperature y (k) at the time of k is taken as the output of the MISO Hammerstein model;
the static nonlinear link is characterized by using the formula (1) and the formula (2):
in the formula (1), v 1 (k) Is u 1 (k) And y (k), r i Is u 1 (k) I-th order term coefficient of L 1 Is u 1 (k) Is the highest number of times;
in the formula (2), v 2 (k) Is u 2 (k) And an intermediate variable at time k between y (k), s i Is u 2 (k) I-th order term coefficient of L 2 Is u 2 (k) Is the highest number of times;
and (3) representing a dynamic linear link by using an ARMAX model shown in the formula (3):
A(z -1 )y(k)＝z -τ D(z -1 )v(k)+ε(k) (3)
in formula (3), z -1 For a delay operator, representing a lag of 1 step;
representing a linear element output coefficient polynomial, +.>
Respectively 1 to n of output coefficient polynomials a Sub-term coefficients, n a Representing the output order;
representing a linear element input coefficient polynomial, +.>
Respectively 1 to n of input coefficient polynomials d Sub-term coefficients, n d Representing an input order; v (k) = [ v 1 (k),v 2 (k)] T An intermediate vector at time k, T representing the transpose; epsilon (k) represents the noise term at time k; z -τ Indicating lag tau steps, wherein tau is the number of lag steps;
step 3.2, identifying parameters of the MISO Hammerstein model;
step 3.2.1, initializing structural parameters of the MISO Hammerstein model, including: output order n a Input order n d Number of steps by hysteresis τ, u 1 (k) The highest number L of times 1 And u 2 (k) The highest number L of times 2 And L is 1 And L 2 Is odd; in the present embodiment, let L 1 ＝3、L 2 ＝3、n a ＝2、n d ＝2、τ＝2；
Step 3.2.2, let D (z -1 ) D in the coefficient of (2) 1 ＝[b 1 ,c 1 ]，d 2 ＝[0,c 2 ]The formula (3) is converted into a form required by a least squares algorithm as shown in the formula (4);
in the formula (4), the amino acid sequence of the compound,
θ＝[a 1 ,a 2 ,b 1 r 0 ,b 1 r 1 ,b 1 r 2 ,b 1 r 3 ,c 1 s 0 ,c 1 s 1 ,c 1 s 2 ,c 1 s 3 ,c 2 s 0 ,c 2 s 1 ,c 2 s 2 ,c 2 s 3 ] T
step 3.2.3, inputting the decomposing furnace field data filtered in the step 2 into a formula (4), and obtaining an optimal estimated value of theta through a recursive least square formula shown as a formula (5);
in the formula (5), K (K),
P (k) is a gain matrix, a parameter estimation matrix and a covariance matrix of errors respectively; initial parameter P (0) =α required for recursive computation 2 I、/>
Wherein alpha is a larger real number, delta is a smaller vector, and the dimension is the same as theta; in this embodiment, α=10 3 ，δ＝0.001×[1,...,1] T ；
The MISO Hammerstein model parameter a can be directly obtained from the theta obtained in the steps 3.2.4 and 3.2.3 1 、a 2 But there is a coupling between b and r, and between c and s, which are separated by singular value decomposition.
In this embodiment, the residual parameter a of the MISO Hammerstein model is finally identified 1 ＝-1.9751、a 2 ＝0.9816；d 1 ＝[1,0.7074]、d 2 ＝[0,0.7068]；r 0 ＝1.8673、r 1 ＝0.0308、r 2 ＝-0.0038、r 3 ＝1.518×10 -4 ；s 0 ＝2.6407、s 1 ＝0.0294、s 2 ＝-0.0023、s 3 ＝5.043×10 -5 。
And 3.2.5, substituting the initialized parameters in the step 3.2.1 and the parameters identified in the step 3.2.4 into the formulas (1) - (3) to obtain a MISO Hammerstein model, performing curve fitting degree test on the model, and if the test requirement is met, obtaining a final MISO Hammerstein model and using the final MISO Hammerstein model as a decomposing furnace outlet temperature model under garbage cooperative treatment. In the embodiment, the fitting coefficient is 0.9993, and the inspection requirement is met;
step 4, controlling the outlet temperature of the decomposing furnace at the moment k under the cooperative disposal of garbage based on the MISO Hammerstein model;
the control principle of the invention is shown in figure 2, a predicted sequence of the outlet temperature is obtained through a predicted model, a softened sequence of an outlet temperature set value is obtained through a reference track, a quadratic performance index is formed according to the difference between the two sequences and the weighting of an intermediate vector, and the intermediate vector is obtained through a generalized predictive control algorithm; replacing the intermediate vector back to the nonlinear model, and solving the coal feeding quantity and the garbage flow; the obtained coal feeding quantity and garbage flow are processed by upper and lower limit constraint and increment constraint and then sent to an industrial field control system, so that the outlet temperature of the decomposing furnace is controlled.
Step 4.1, solving an intermediate vector v (k) at the moment k by a generalized predictive control algorithm aiming at a dynamic linear link:
step 4.1.1, constructing an outlet temperature prediction model of the decomposing furnace by using the formula (6):
in formula (6), j=n 1 ,...,N 2 Indicating the number of steps of the advance prediction, N 1 =τ+1 and N 2 ＝τ+N p Respectively a starting point and an ending point of a prediction time domain, N p Is the prediction step length;
the predicted value of the outlet temperature of the decomposing furnace at the moment k+j is shown; deltav (k+j- τ) represents the intermediate vector increment at time k+j- τ, deltav (k-1) represents the intermediate vector increment at time k-1; g j (z -1 )、F j (z -1 )、H j (z -1 ) Respectively about z -1 And coefficients of each polynomial are solved by a Dipsilon diagram equation; g j (z -1 ) Representation ofCoefficients of Deltav (k+j- τ) at time k+j, F j (z -1 ) A coefficient indicating y (k) at time k+j, H j (z -1 ) A coefficient representing Deltav (k-1) at time k+j;
step 4.1.2, when j=n 1 ,...,N 2 When by N p Obtaining an outlet temperature prediction sequence of the decomposing furnace shown in the formula (7) by using the group outlet temperature prediction model
In formula (7), v= [ Δv (k),. The term,. Δv (k+n) u -1)] T Prediction sequence representing intermediate vector increment, N u Representing a control step size; g is a coefficient matrix of V; f is a coefficient vector of y (k); h is a coefficient vector of Deltav (k-1);
step 4.1.3, obtaining a softening value y of the expected value of the outlet temperature of the decomposing furnace at the moment k+j by using the step (8) r (k+j) such that at j=n 1 ,...,N 2 When by N p The softening values form a softening sequence Y of expected values of the outlet temperature of the decomposing furnace r ；
In the formula (8), α represents a softening coefficient, and 0<α<1；y sp Indicating the expected value of the outlet temperature of the decomposing furnace;
step 4.1.4, constructing a performance index of generalized predictive control by using a formula (9):
in the formula (9), Λ represents a control weight matrix;
step 4.1.5, substituting the formula (7) and the formula (8) into the formula (9), thereby obtaining a predicted sequence V of intermediate vector increment by using the formula (10):
V＝(G T G+Λ) -1 G T [Y r -Fy(k)-H△v(k-1)] (10)
step 4.1.6, taking the first two rows of V in the formula (10) as the increment Deltav (k) of the intermediate vector at the moment k;
step 4.1.7, obtaining an intermediate vector v (k) at the time of k by using the method (11):
v(k)＝v(k-1)+△v(k) (11)
step 4.2, solving the control quantity by the intermediate vector aiming at the nonlinear link:
step 4.2.1, substituting the intermediate vector obtained in the formula (11) into the formulas (1) and (2), and obtaining two groups of roots by solving two unitary high-order equations;
step 4.2.2, removing the virtual root from the root solved in the step 4.2.1, and only retaining the real root;
step 4.2.3, if each group in step 4.2.2 finally only keeps one real root, the real root is assigned to the coal feeding quantity u at the moment k 1 (k) And the garbage flow u at the moment k 2 (k) The method comprises the steps of carrying out a first treatment on the surface of the If each group in the step 4.2.2 reserves two or more solid roots, selecting the coal feeding quantity u closest to the k-1 moment 1 Garbage flow u at times (k-1) and (k-1) 2 Corresponding solid roots of (k-1) are given to the coal feeding quantity u at the moment k 1 (k) And the garbage flow u at the moment k 2 (k)；
Step 4.3, feeding coal quantity u according to the upper and lower limit constraints of the coal feeding quantity, the upper and lower limit constraints of the increment of the coal feeding quantity, the upper and lower limit constraints of the garbage flow quantity and the upper and lower limit constraints of the increment of the garbage flow quantity 1 (k) And garbage flow u 2 (k) Processing to obtain constrained coal feeding quantity u 1 (k) And garbage flow u 2 (k) Issuing to an industrial field decomposing furnace control system to control the operation of the decomposing furnace at the moment k;
and 5, after k+1 is assigned to k, returning to the step 4 to execute the next time control.
In this embodiment, an electronic device includes a memory for storing a program supporting the processor to execute the above method, and a processor configured to execute the program stored in the memory.
In this embodiment, a computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method described above.
In this example, the expected temperature of the decomposing furnace was set to 885℃in step 51, and 878℃in step 251, and the control effect, the coal feeding amount, and the garbage flow rate were adjusted as shown in FIG. 3. In the control process, the stability of the control can be adjusted by adjusting the softening coefficient, specifically, the softening coefficient is increased, so that the outlet temperature of the decomposing furnace can track the set value more stably, and the time required for the outlet temperature to reach the stable value can be prolonged correspondingly; the mixing proportion of the garbage can be adjusted by adjusting the control weighting matrix, specifically, the increase of the coal feeding amount can be limited by increasing the weighting coefficient of the coal feeding amount in the control weighting matrix, so that the mixing proportion of the garbage is increased, and finally, the purposes of garbage recycling and energy conservation and emission reduction in the cement industry are achieved.
Claims (4)
1. The decomposing furnace temperature control method under the cooperative disposal of the garbage is characterized by comprising the following steps:
step 1, acquiring site data { T (k), F of a decomposing furnace under the cooperative disposal of garbage in real time c (k)，F R (k) I k=1, & N; wherein T (k) represents the original value of the outlet temperature of the decomposing furnace at time k, F c (k) Representing the original value of the coal feeding quantity of the decomposing furnace at the moment k, F R (k) The original value of the garbage flow of the decomposing furnace at the moment k is represented;
step 2, utilizing a sliding average filtering method to carry out on field data { T (k), F c (k)，F R (k) Pre-processing of i k=1,..n } to obtain filtered decomposing furnace field data
Wherein (1)>
Indicating the time kFiltering value of outlet temperature of decomposing furnace, +.>
Representing the filtering value of the coal feeding quantity of the decomposing furnace at the moment k, < >>
The garbage flow filtering value of the decomposing furnace at the moment k is represented;
step 3, based on the filtered decomposing furnace field data, establishing a MISO Hammerstein model:
step 3.1, constructing a MISO Hammerstein model formed by connecting a static nonlinear link and a dynamic linear link in series, and feeding coal quantity u at k moment 1 (k) And the garbage flow u at the moment k 2 (k) As two inputs of the MISO Hammerstein model, the outlet temperature y (k) at the time of k is taken as the output of the MISO Hammerstein model;
characterizing the static nonlinear element using formula (1) and formula (2):
in the formula (1), v 1 (k) Is u 1 (k) And y (k), r i Is u 1 (k) I-th order term coefficient of L 1 Is u 1 (k) Is the highest number of times;
in the formula (2), v 2 (k) Is u 2 (k) And an intermediate variable at time k between y (k), s i Is u 2 (k) I-th order term coefficient of L 2 Is u 2 (k) Is the highest number of times;
characterizing the dynamic linear element using an ARMAX model of formula (3):
A(z -1 )y(k)＝z -τ D(z -1 )v(k)+ε(k) (3)
in formula (3), z -1 For a delay operator, representing a lag of 1 step;
representing a linear element output coefficient polynomial, +.>
Respectively 1 to n of output coefficient polynomials a Sub-term coefficients, n a Representing the output order;
representing a linear element input coefficient polynomial, +.>
Respectively 1 to n of input coefficient polynomials d Sub-term coefficients, n d Representing an input order; v (k) = [ v 1 (k),v 2 (k)] T An intermediate vector at time k, T representing the transpose; epsilon (k) represents the noise term at time k; z -τ Indicating lag tau steps, wherein tau is the number of lag steps;
step 3.2, identifying parameters of the MISO Hammerstein model;
initializing structural parameters of the MISO Hammerstein model, including: output order n a Input order n d Number of steps by hysteresis τ, u 1 (k) The highest number L of times 1 And u 2 (k) The highest number L of times 2 And L is 1 And L 2 Is odd;
inputting the filtered decomposing furnace field data into the MISO Hammerstein model, and identifying the residual parameters of the model by a recursive least square method and a parameter separation method, wherein the method comprises the following steps of: coefficient of linear link output coefficient polynomial
Coefficients of a linear element input coefficient polynomial +.>
Nonlinear link u 1 (k) Coefficient of->
Nonlinear link u 2 (k) Coefficient of->
Step 4, controlling the outlet temperature of the decomposing furnace at the moment k under the cooperative disposal of garbage based on the MISO Hammerstein model;
step 4.1, solving an intermediate vector v (k) at the moment k by a generalized predictive control algorithm aiming at a dynamic linear link:
step 4.2, solving the control quantity by the intermediate vector aiming at the nonlinear link:
step 4.2.1, substituting the intermediate vector obtained in the formula (9) into the formulas (1) and (2), and obtaining two groups of roots by solving two unitary high-order equations;
step 4.2.2, removing the virtual roots in the roots solved in the step 4.2.1, and only retaining the real roots;
if each group of roots finally only keeps one real root, the real root in the corresponding group of roots is given to the coal feeding quantity u at the moment k 1 (k) And the garbage flow u at the moment k 2 (k)；
If each group of roots keeps two or more real roots, selecting the coal feeding quantity u closest to k-1 moment 1 Garbage flow u at times (k-1) and (k-1) 2 Corresponding solid root of (k-1) and giving the coal feeding quantity u at the moment k 1 (k) And the garbage flow u at the moment k 2 (k)；
Step 4.3, feeding coal quantity u according to the upper and lower limit constraints of the coal feeding quantity, the upper and lower limit constraints of the increment of the coal feeding quantity, the upper and lower limit constraints of the garbage flow quantity and the upper and lower limit constraints of the increment of the garbage flow quantity 1 (k) And garbage flow u 2 (k) Processing to obtain constrained coal feeding quantity u 1 (k) And garbage flow u 2 (k) Issuing to an industrial field decomposing furnace control system to control the operation of the decomposing furnace at the moment k;
and 5, after k+1 is assigned to k, returning to the step 4 to execute the next time control.
2. The method for controlling the temperature of a decomposing furnace under the cooperative disposal of garbage as claimed in claim 1, wherein the step 4.1 is performed as follows:
step 4.1.1, constructing an outlet temperature prediction model of the decomposing furnace by using the formula (4):
in formula (4), j=n 1 ,...,N 2 Indicating the number of steps of the advance prediction, N 1 =τ+1 and N 2 ＝τ+N p Respectively a starting point and an ending point of a prediction time domain, N p Is the prediction step length;
the predicted value of the outlet temperature of the decomposing furnace at the moment k+j is shown; deltav (k+j- τ) represents the intermediate vector increment at time k+j- τ, deltav (k-1) represents the intermediate vector increment at time k-1; g j (z -1 )、F j (z -1 )、H j (z -1 ) Respectively about z -1 And coefficients of each polynomial are solved by a Dipsilon diagram equation; g j (z -1 ) A coefficient representing Deltav (k+j- τ) at time k+j, F j (z -1 ) A coefficient indicating y (k) at time k+j, H j (z -1 ) A coefficient representing Deltav (k-1) at time k+j;
step 4.1.2, when j=n 1 ,...,N 2 When by N p Obtaining an outlet temperature prediction sequence of the decomposing furnace shown in the formula (5) by using the group outlet temperature prediction model
In formula (5), v= [ Δv (k),. The term,. Δv (k+n) u -1)] T Prediction sequence representing intermediate vector increment, N u Representing a control step size; g is a coefficient matrix of V; f is a coefficient vector of y (k); h is a coefficient vector of Deltav (k-1);
step 4.1.3, obtaining a softening value y of the expected value of the outlet temperature of the decomposing furnace at the moment k+j by using the step (6) r (k+j) such that at j=n 1 ,...,N 2 When by N p The softening values form a softening sequence Y of expected values of the outlet temperature of the decomposing furnace r ；
In formula (6), α represents a softening coefficient, and 0<α<1；y sp Indicating the expected value of the outlet temperature of the decomposing furnace;
step 4.1.4, constructing a performance index of generalized predictive control by using a formula (7):
in the formula (7), Λ represents a control weight matrix;
step 4.1.5, substituting the formula (5) and the formula (6) into the formula (7), thereby obtaining a predicted sequence V of intermediate vector increments by using the formula (8):
V＝(G T G+Λ) -1 G T [Y r -Fy(k)-H△v(k-1)] (8)
step 4.1.6, taking the first two rows of V in the formula (8) as the intermediate vector increment delta V (k) at the moment k;
step 4.1.7, obtaining an intermediate vector v (k) at the time of k by using the method (9):
v(k)＝v(k-1)+△v(k) (9)
3. an electronic device comprising a memory and a processor, wherein the memory is configured to store a program that supports the processor to perform 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 having a computer program stored thereon, characterized in that the computer program when run by a processor performs the steps of the method according to claim 1 or 2.
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