CN114967468A - Dynamic double-index control method and device for desulfurization system - Google Patents

Dynamic double-index control method and device for desulfurization system Download PDF

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CN114967468A
CN114967468A CN202210651399.4A CN202210651399A CN114967468A CN 114967468 A CN114967468 A CN 114967468A CN 202210651399 A CN202210651399 A CN 202210651399A CN 114967468 A CN114967468 A CN 114967468A
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吴晔
李益国
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Datang Environment Industry Group Co Ltd
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Abstract

The embodiment of the specification provides a desulfurization system dynamic dual-index control method and device based on an input and output state space prediction model, wherein the method comprises the following steps: acquiring a step response model of an object, acquiring and analyzing field operation data based on the step response model, and performing a step response test of variables for a main control loop; selecting sampling time T according to the field operation data and the data result of the step response test, establishing a mathematical model between input variables and output variables of the desulfurization process by adopting a process identification method, selecting a state variable vector, an input variable vector and an output variable vector, and deriving an input and output state space prediction model; and simultaneously controlling the limestone slurry amount, the rotating speed of the oxidation fan and the rotating speed of the slurry pump through the input and output state space prediction model, SO as to control the discharge of the outlet SO2, the pH value of the slurry and the ORP.

Description

Dynamic double-index control method and device for desulfurization system
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for controlling a desulfurization system dynamically based on an input/output state space prediction model.
Background
The flue gas desulfurization technology is an effective method for controlling SO2 emission of a coal-fired power plant, and the wet limestone-gypsum flue gas desulfurization technology is the most widely applied flue gas desulfurization technology in the current coal-fired power plant. Desulfurization techniques involving both absorption of the SO2 gas and subsequent oxidation of the sulfite are a continuous and complex process. Currently, desulfurization systems are simply acid-base controlled, i.e., pH controlled. Neglecting the control of the oxidation-reduction reaction brings a series of problems to the power plant, such as reduction of the quality of gypsum, increase of energy consumption of a desulfurization system, easy scaling of equipment and the like, and influences the safe and stable operation of the desulfurization island. ORP is introduced as a new control index, a pH and ORP dynamic dual-control strategy is implemented on the desulfurization system, the oxidation state of the desulfurization slurry is determined by monitoring the dynamic changes of pH and ORP in real time, the oxidation air volume is adjusted in time, the oxidation state of the slurry can be accurately controlled, and the operation effect of the whole system is guaranteed.
The desulfurization process is a complex controlled object with multivariable, strong coupling, nonlinearity and large delay characteristics, and relates to complex physical and chemical processes such as reaction, absorption, mass transfer, heat transfer, drying, separation, crystallization and the like, wherein the pH value control of circulating slurry is a typical nonlinear and large delay control problem, the coupling between loops is serious, and the interferences such as flue gas flow, flue gas SO2 concentration, limestone slurry concentration and the like are numerous. The conventional control scheme taking PID as a core is usually adopted for automatic control of the current desulfurization system, and for the system, the scheme cannot solve the contradiction between the stability of the control system and the quality of the control system, and inevitably causes the instability and oscillation of the control system, thereby causing the repeated fluctuation of the desulfurization efficiency and the pH value of the absorption liquid; furthermore, the conventional PID regulator is a linear regulator in nature, and its control effect on the desulfurization process with strong nonlinearity is very limited.
Currently, desulfurization systems are simply acid-base controlled, i.e., pH controlled. Neglecting the control of the oxidation-reduction reaction brings a series of problems to the power plant, such as reduction of the quality of gypsum, increase of energy consumption of a desulfurization system, easy scaling of equipment and the like, and influences the safe and stable operation of the desulfurization island. In addition, the automatic control of the desulfurization system usually adopts a conventional control scheme taking PID as a core, and the control performance is difficult to meet the requirement.
Disclosure of Invention
The invention aims to provide a dynamic dual-index control method and device of a desulfurization system based on an input and output state space prediction model, and aims to solve the problems in the prior art.
The invention provides a desulfurization system dynamic dual-index control method based on an input and output state space prediction model, which comprises the following steps:
acquiring a step response model of an object, acquiring and analyzing field operation data based on the step response model, and performing a step response test of variables for a main control loop;
selecting sampling time T according to the field operation data and the data result of the step response test, establishing a mathematical model between input variables and output variables of the desulfurization process by adopting a process identification method, selecting a state variable vector, an input variable vector and an output variable vector, and deriving an input and output state space prediction model;
through the input and output state space prediction model, the limestone slurry amount, the rotating speed of the oxidation fan and the rotating speed of the slurry pump are simultaneously controlled, SO that the outlet SO is controlled 2 Discharge and slurry pH and ORP.
The invention provides a desulfurization system dynamic dual-index control device based on an input and output state space prediction model, which comprises:
the step response testing module is used for acquiring a step response model of the object, acquiring and analyzing field operation data based on the step response model and carrying out variable step response test on the main control loop;
the input and output state space prediction model module is used for selecting sampling time T according to the field operation data and the data result of the step response test, establishing a mathematical model between input variables and output variables of the desulfurization process by adopting a process identification method, selecting a state variable vector, an input variable vector and an output variable vector, and deriving an input and output state space prediction model;
a control module for controlling the limestone slurry amount, the rotational speed of the oxidation fan and the rotational speed of the slurry pump simultaneously through the input and output state space prediction model SO as to control the outlet SO 2 Discharge and slurry pH and ORP.
By adopting the dynamic double-index control method of the desulfurization system based on the input-output state space prediction model, the desulfurization system is integrally regarded as a multivariable object with three inputs and three outputs through a pH and ORP dynamic double-index control strategy, and the input-output state space prediction model is designed, so that the limestone slurry amount, the rotating speed of the oxidation fan and the rotating speed of the slurry pump are simultaneously controlled, the running safety and the economical efficiency of the desulfurization system are improved, the problems of instability, oscillation, repeated fluctuation of desulfurization efficiency and the like of the traditional control system are solved, meanwhile, the oxidation air volume can be timely adjusted, and the slurry oxidation condition is improved.
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In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings that are needed in the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and that other drawings can be obtained by those skilled in the art without inventive exercise.
FIG. 1 is a flow chart of a dynamic dual-index control method of a desulfurization system based on an input-output state space prediction model according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a principle of a dual-index prediction control method for an input/output state space model desulfurization process according to an embodiment of the present invention;
FIG. 3 is a schematic illustration of the predictive control effect of a desulfurization system in accordance with an embodiment of the present invention;
FIG. 4 is a schematic diagram of a dynamic dual-index control device of a desulfurization system based on an input-output state space prediction model according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in one or more embodiments of the present disclosure, the technical solutions in one or more embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in one or more embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all embodiments. All other embodiments that can be derived by a person skilled in the art from one or more of the embodiments described herein without making any inventive step shall fall within the scope of protection of this document.
Method embodiment
According to an embodiment of the present invention, a dynamic dual-index control method for a desulfurization system based on an input/output state space prediction model is provided, fig. 1 is a flowchart of the dynamic dual-index control method for a desulfurization system based on an input/output state space prediction model according to an embodiment of the present invention, as shown in fig. 1, the dynamic dual-index control method for a desulfurization system based on an input/output state space prediction model according to an embodiment of the present invention specifically includes:
step 101, acquiring a step response model of an object, acquiring and analyzing field operation data based on the step response model, and performing a step response test of variables for a main control loop; specifically, a step response model of an object is obtained, field operation data are collected and analyzed based on the step response model, for a main control loop, an input variable is changed in a step mode, and changes of output parameters are recorded, wherein the input variable specifically comprises: limestone slurry amount, oxidation fan rotating speed and slurry circulating pump rotating speed, the output parameters specifically include: slurry pH, slurry ORP and outlet SO2 concentration
102, selecting sampling time T according to the field operation data and the data result of the step response test, establishing a mathematical model between input variables and output variables of the desulfurization process by adopting a process identification method, selecting a state variable vector, an input variable vector and an output variable vector, and deriving an input and output state space prediction model;
step 102 specifically includes: selecting sampling time T according to the field operation data and the dynamic characteristic test data result, and establishing a mathematical model between input variables and output variables of the desulfurization process by adopting a process identification method based on formula 1:
y(k+1)+F 1 y(k)+L+F n y(k-n+1)＝H 1 u(k)+H 2 u(k-1)+L+H n u (k-n +1) formula 1;
wherein y (k) ═ y 1 (k) T y 2 (k) T y 3 (k) T ] T ,y 1 (k) Is an outlet SO 2 Concentration, y 2 (k) Is the pH value of the slurry, y 3 (k) Is the ORP of the slurry; u (k) ═ u 1 (k) T u 2 (k) T u 3 (k) T ] T ，u 1 (k) Is the rotating speed of the slurry circulating pump u 2 (k) For oxidizing the rotational speed of the fan u 3 (k) The amount of limestone and stone slurry; f n And H n Identifying a corresponding parameter matrix under the obtained state space model;
selecting a state variable vector, an input variable vector and an output variable vector according to formulas 2-4:
x(k)＝[Δy(k) T Δy(k-1) T L Δy(k-n+1) T Δu(k-1) T Δu(k-2) T L Δu(k-n+1) T e(k) T ] T formula 2;
u(k)＝[u 1 (k) T u 2 (k) T u 3 (k) T ] T formula 3;
y(k)＝[y 1 (k) T y 2 (k) T y 3 (k) T ] T formula 4;
wherein Δ y (k) -y (k-1), Δ u (k) -u (k-1), and the output tracking error e (k) y (k) -r (k), and r (k) is the desired output;
an input-output state space model is derived according to equations 5-11:
x (k +1) ═ ax (k) + B Δ u (k) + C Δ r (k +1) formula 5;
Figure BDA0003686263930000051
Figure BDA0003686263930000052
Figure BDA0003686263930000053
Figure BDA0003686263930000054
Figure BDA0003686263930000055
C m ＝[I q O O L O O O O]equation 11;
where O is a m x 3 dimensional zero matrix and m is a non-minimum state space vector x m (k) Dimension of (d), m ═ 6n-3, I q And I p Is a 3-dimensional unit array.
103, simultaneously controlling the limestone slurry amount, the rotating speed of the oxidation fan and the rotating speed of the slurry pump through the input and output state space prediction model SO as to control the outlet SO 2 Discharge and slurry pH and ORP. Step 103 specifically comprises:
step 1, setting and selecting relevant parameters of a controller, wherein the relevant parameters of the controller specifically comprise: a maximum prediction step length P, a control step length M, a weighting symmetric matrix Q, an input increment weighting matrix L and a softening coefficient mu; predicting main dynamic characteristics of the time domain P covering system, controlling the time domain M not to be larger than the predicted time domain P, and taking a softening coefficient of 0<μ<1; input incremental weighting array L ═ diag { L ═ d } 1 L 2 L L M In the formula, L j More than or equal to 0 is a weighting coefficient for controlling input increment, and j is more than or equal to 1 and less than or equal to M; weighted symmetric matrix Q ═ diag { Q ═ Q 1 Q 2 L Q P In the formula Q j ＝diag{q j,y1 L q j,y3n q j,u1 L q j,u3(n-1) q j,e1 L q j,e3 J is more than or equal to 1 and less than or equal to P, Q j The diagonal element value in the control system corresponds to the weight given to each output increment item, input increment item and error item in the control system.
Step 2, initializing the state of the controller, namely detecting the measured value of the output variable at the current moment under a certain steady-state working condition, and taking the measured value as the initial value of the state of the controller;
step 3, obtaining real-time data of the pH and ORP output variables of the slurry through online monitoring, calculating and updating X (k) and delta R, and predicting a future state vector X by adopting a prediction model shown in formulas 12-16:
x ═ fx (k) + Φ Δ U + S Δ R formula 12;
X＝[x(k+1) T x(k+2) T L x(k+P) T ] T formula 13;
ΔU＝[Δu(k) T Δu(k+1) T L Δu(k+M+1) T ] T equation 14;
F＝[A A 2 L A P ] T equation 15;
Figure BDA0003686263930000061
Figure BDA0003686263930000071
step 4, setting the reference trajectory to r (k) y (k), and r (k + i) μ i y(k)+(1-μ i )y s I-1, 2, …, P, wherein y s For the set point vector, the reference vector increment is calculated according to equation 18:
Figure BDA0003686263930000072
step 5, constructing a performance index function according to a formula 19 to solve the optimal control increment delta U:
J＝X T QX+ΔU T l Δ U equation 19;
step 6, substituting equation 12 into equation 19, and calculating the control vector increment Δ u (k) ═ k at time k according to the extremum requirement S x(k)-k R Δ R, wherein k S 、k R Are respectively a matrix K S 、K R P is the dimension of the input vector, K S ＝(Φ T QΦ+L) -1 Φ T QF，K R ＝(Φ T QΦ+L) -1 Φ T QS；
Step 7, calculating an input controlled variable u (k) ═ u (k-1) + Δ u (k);
step 8, if u i (k)>u max Then let u i (k)＝u max ，Δu i (k)＝u max -u i (k-1), i ═ 1,2, 3; if u is i (k)<u min Then order u i (k)＝u min ，Δu i (k)＝u min -u i (k-1), i ═ 1,2, 3; wherein u is max And u min Respectively an upper limit value and a lower limit value of the control quantity;
and 9, outputting u (k), and repeatedly executing the steps 3 to 9 in each sampling period.
The technical solutions of the embodiments of the present invention are described below by way of example with reference to the accompanying drawings.
The embodiment of the invention provides a dynamic double-index control method of a desulfurization system based on an input and output state space prediction model, as shown in figure 2, the method adopts a pH and ORP dynamic double-index control strategy, the desulfurization system is integrally regarded as a multivariable object with three inputs and three outputs, and the input and output state space prediction model is designed to realize the simultaneous control of the limestone slurry amount, the rotating speed of an oxidation fan and the rotating speed of a slurry pump, so that the safety and the economy of the operation of the desulfurization system are improved.
Step 1, selecting a sampling period T which is 15s, and acquiring a state space and a corresponding discretization mathematical model between input variables and output variables in the desulfurization process:
Figure BDA0003686263930000081
Figure BDA0003686263930000082
step 2, calculating the parameters of the input and output state space model
Figure BDA0003686263930000083
Figure BDA0003686263930000084
Step 3, setting relevant parameters of the controller, taking the maximum prediction step length P as 10, taking the control step length M as 1, taking the softening coefficient mu as 0.8, and taking the weighting array Q as diag { Q ═ 1 Q 2 L Q 20 }， Q j 1 ≦ j ≦ P, { 0L 00L 01L 1 }; inputting an incremental weighting array L ═ diag { 0.50.010.05 };
step 4, calculating the parameters of the prediction model:
Figure BDA0003686263930000091
Figure BDA0003686263930000092
step 5, calculating controller parameters:
Figure BDA0003686263930000093
Figure BDA0003686263930000094
step 6, initializing the state of the controller;
step 7, obtaining the current measured values y (k) of each output variable, then calculating and updating X (k) and Δ R, establishing a prediction model X ═ fx (k) + Φ Δ U + S Δ R, and predicting the outputs of P times in the future;
step 8, calculating the control vector increment Δ u (k) ═ -k S x(k)-k R ΔR；
Step 9, calculating an input control amount u (k) ═ u (k-1) + Δ u (k);
step 10, if u i (k)>100, then order u i (k)＝100，Δu i (k)＝u max -u i (k-1)，i＝1，2，3; if u is i (k)<0, then let u i (k)＝0，Δu i (k)＝u min -u i (k-1)，i＝1，2，3；
Step 11, outputting the control action u (k), and then, repeatedly executing the step 7 to the step 11 in each sampling period
FIG. 3 shows the step increase of the concentration value of SO2 at the outlet by 1mg/m 3 Under the condition, the invention is adopted to predict the control effect curve of the desulfurization system. It can be seen that under the condition that the concentration of SO2 deviates from a set value, the invention can quickly and stably recover the concentration of SO2 to the set value by coordinately controlling the limestone slurry amount, the rotating speed of an oxidation fan and the rotating speed of a slurry circulating pump and combining an input and output state space prediction model, ensure that the SO2 reaches the standard and is discharged, simultaneously ensure that the pH value and the ORP of the slurry are both controlled in an ideal interval, realize the accurate control of the slurry oxidation state, and effectively improve the operation efficiency and the economical efficiency of a desulfurization system compared with the traditional control strategy and control model.
In conclusion, the embodiment of the invention determines the oxidation state of the desulfurization slurry by monitoring the dynamic changes of pH and ORP in real time, adjusts the oxidation air quantity in time, ensures that the SO2 at the outlet is discharged after reaching the standard, accurately controls the oxidation state of the slurry, and realizes energy conservation and consumption reduction; in addition, by adopting the input and output state space prediction model, the strong coupling, nonlinearity and large delay characteristics of the desulfurization process are effectively processed, the dynamic performance and tracking performance of the system and the robustness under the model mismatch condition are improved, the control quality of the system is improved, and the efficient, safe, stable and economic operation of the desulfurization system is ensured.
Device embodiment
According to an embodiment of the present invention, there is provided a desulfurization system dynamic dual-index control device based on an input/output state space prediction model, fig. 4 is a schematic diagram of a desulfurization system dynamic dual-index control device based on an input/output state space prediction model according to an embodiment of the present invention, as shown in fig. 4, the desulfurization system dynamic dual-index control device based on an input/output state space prediction model according to an embodiment of the present invention specifically includes:
the step response testing module 40 is used for acquiring a step response model of the object, acquiring and analyzing field operation data based on the step response model, and performing a step response test of variables for the main control loop; the step response testing module 40 is specifically configured to:
acquiring a step response model of an object, acquiring and analyzing field operation data based on the step response model, respectively changing input variables in a step mode aiming at a main control loop, and recording changes of output parameters, wherein the input variables specifically comprise: limestone slurry amount, oxidation fan rotating speed and slurry circulating pump rotating speed, the output parameters specifically include: slurry pH, slurry ORP, and outlet SO2 concentration;
an input and output state space prediction model module 42, configured to select a sampling time T according to the field operation data and the data result of the step response test, establish a mathematical model between an input variable and an output variable of a desulfurization process by using a process identification method, select a state variable vector, an input variable vector, and an output variable vector, and derive an input and output state space prediction model; the input-output state space prediction model module 42 is specifically configured to:
selecting sampling time T according to the field operation data and the dynamic characteristic test data result, and establishing a mathematical model between input variables and output variables of the desulfurization process by adopting a process identification method based on formula 1:
y(k+1)+F 1 y(k)+L+F n y(k-n+1)＝H 1 u(k)+H 2 u(k-1)+L+H n u (k-n +1) formula 1;
wherein y (k) ═ y 1 (k) T y 2 (k) T y 3 (k) T ] T ,y 1 (k) Is an outlet SO 2 Concentration, y 2 (k) Is the pH value of the slurry, y 3 (k) Is the ORP of the slurry; u (k) ═ u 1 (k) T u 2 (k) T u 3 (k) T ] T ，u 1 (k) Is the rotating speed of the slurry circulating pump u 2 (k) For oxidizing the rotational speed of the fan u 3 (k) The amount of limestone and stone slurry; f n And H n For pairs under the state space model obtained by identificationApplying a parameter matrix;
selecting a state variable vector, an input variable vector and an output variable vector according to formulas 2-4:
x(k)＝[Δy(k) T Δy(k-1) T L Δy(k-n+1) T Δu(k-1) T Δu(k-2) T L Δu(k-n+1) T e(k) T ] T formula 2;
u(k)＝[u 1 (k) T u 2 (k) T u 3 (k) T ] T formula 3;
y(k)＝[y 1 (k) T y 2 (k) T y 3 (k) T ] T formula 4;
wherein Δ y (k) -y (k-1), Δ u (k) -u (k-1), and the output tracking error e (k) y (k) -r (k), and r (k) is the desired output;
an input-output state space model is derived according to equations 5-11:
x (k +1) ═ ax (k) + B Δ u (k) + C Δ r (k +1) formula 5;
Figure BDA0003686263930000111
Figure BDA0003686263930000121
Figure BDA0003686263930000122
Figure BDA0003686263930000123
Figure BDA0003686263930000124
C m ＝[I q O O L O O O O]equation 11;
where O is a m x 3 dimensional zero matrix and m is a non-minimum state space vector x m (k) Dimension of (d), m ═ 6n-3, I q And I p Is a 3-dimensional unit array.
A control module 44, configured to control the amount of limestone slurry, the rotational speed of the oxidation fan, and the rotational speed of the slurry pump simultaneously through the input/output state space prediction model, SO as to control the outlet SO 2 Discharge and slurry pH and ORP. The control module 44 specifically includes:
the setting submodule is used for setting and selecting relevant parameters of the controller, and the relevant parameters of the controller specifically comprise: maximum prediction step length P, control step length M, weighting symmetric matrix Q, input increment weighting matrix L and softening coefficient mu; predicting main dynamic characteristics of the time domain P covering system, controlling the time domain M not to be larger than the predicted time domain P, and taking the softening coefficient to be 0<μ<1; weighted symmetric matrix Q ═ diag { Q ═ Q 1 Q 2 L Q P In the formula Q j ＝diag{q j,y1 L q j,y3n q j,u1 L q j,u3(n-1) q j,e1 L q j,e3 J is more than or equal to 1 and less than or equal to P, Q j The diagonal element value in the control system corresponds to the weight value given to each output increment item, input increment item and error item in the control system; input incremental weighting array L ═ diag { L ═ d } 1 L 2 L L M In the formula, L j More than or equal to 0 is the weighting coefficient of the control input increment, and j is more than or equal to 1 and less than or equal to M.
The initialization submodule is used for initializing the state of the controller, namely detecting a measured value of an output variable at the current moment under a certain steady-state working condition and taking the measured value as an initial value of the state of the controller;
and the online monitoring submodule is used for acquiring real-time data of the pH and ORP output variables of the slurry through online monitoring, calculating and updating X (k) and delta R, and predicting a future state vector X by adopting a prediction model shown in the formula 12-16:
x ═ fx (k) + Φ Δ U + S Δ R formula 12;
X＝[x(k+1) T x(k+2) T L x(k+P) T ] T equation 13;
ΔU＝[Δu(k) T Δu(k+1) T L Δu(k+M+1) T ] T equation 14;
F＝[A A 2 L A P ] T equation 15;
Figure BDA0003686263930000131
Figure BDA0003686263930000132
a setting submodule for setting the reference trajectory to r (k) y (k), r (k + i) μ i y(k)+(1-μ i )y s I-1, 2, …, P, wherein y s For the set point vector, the reference vector increment is calculated according to equation 18:
Figure BDA0003686263930000133
and the optimal control increment calculation submodule is used for constructing a performance index function according to a formula 19 and solving the optimal control increment delta U:
J＝X T QX+ΔU T l Δ U equation 19;
a first calculation submodule for substituting the formula 12 into the formula 19, and calculating a control vector increment Δ u (k) -k at the time k by an extremum requirement S x(k)-k R Δ R, wherein k S 、k R Are respectively a matrix K S 、K R P is the dimension of the input vector, K S ＝(Φ T QΦ+L) -1 Φ T QF， K R ＝(Φ T QΦ+L) -1 Φ T QS；
A second calculation submodule for calculating an input control amount u (k) ═ u (k-1) + Δ u (k);
a judgment submodule for if u i (k)>u max Then order u i (k)＝u max ，Δu i (k)＝u max -u i (k-1)， i＝12, 3; if u is i (k)<u min Then let u i (k)＝u min ，Δu i (k)＝u min -u i (k-1), i ═ 1,2, 3; wherein u is max And u min Respectively an upper limit value and a lower limit value of the control quantity;
and the output calling submodule is used for outputting u (k), and repeatedly calling the online monitoring submodule, the setting submodule, the optimal control increment calculating submodule, the first calculating submodule, the second calculating submodule, the judging submodule and the output calling submodule in each sampling period.
The embodiment of the present invention is an apparatus embodiment corresponding to the above method embodiment, and specific operations of each module may be understood with reference to the description of the method embodiment, which is not described herein again.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A desulfurization system dynamic dual-index control method based on an input and output state space prediction model is characterized by comprising the following steps:
acquiring a step response model of an object, acquiring and analyzing field operation data based on the step response model, and performing a step response test of variables for a main control loop;
selecting sampling time T according to the field operation data and the data result of the step response test, establishing a mathematical model between input variables and output variables of the desulfurization process by adopting a process identification method, selecting a state variable vector, an input variable vector and an output variable vector, and deriving an input and output state space prediction model;
through the input and output state space prediction model, the limestone slurry amount, the rotating speed of the oxidation fan and the rotating speed of the slurry pump are simultaneously controlled, SO that the outlet SO is controlled 2 Discharge and slurry pH and ORP.
2. The method of claim 1, wherein a step response model of the subject is obtained, field operational data is collected and analyzed based on the step response model, and performing a step response test of the variables for the primary control loop specifically comprises:
acquiring a step response model of an object, acquiring and analyzing field operation data based on the step response model, respectively changing input variables in a step mode aiming at a main control loop, and recording changes of output parameters, wherein the input variables specifically comprise: limestone slurry amount, oxidation fan rotating speed and slurry circulating pump rotating speed, the output parameters specifically include: slurry pH, slurry ORP, and outlet SO2 concentration.
3. The method of claim 1, wherein selecting a sampling time T based on the field operating data and the data results of the step response test, establishing a mathematical model between input variables and output variables of the desulfurization process using a process identification method, selecting a state variable vector, an input variable vector, and an output variable vector, and deriving an input-output state space prediction model specifically comprises:
selecting sampling time T according to the field operation data and the dynamic characteristic test data result, and establishing a mathematical model between input variables and output variables of the desulfurization process by adopting a process identification method based on formula 1:
y(k+1)+F 1 y(k)+L+F n y(k-n+1)＝H 1 u(k)+H 2 u(k-1)+L+H n u (k-n +1) formula 1;
wherein y (k) ═ y 1 (k) T y 2 (k) T y 3 (k) T ] T ,y 1 (k) Is an outlet SO 2 Concentration, y 2 (k) Is the pH value of the slurry, y 3 (k) Is the ORP of the slurry;u(k)＝[u 1 (k) T u 2 (k) T u 3 (k) T ] T ，u 1 (k) is the rotating speed of the slurry circulating pump u 2 (k) For oxidizing the rotational speed of the fan u 3 (k) The amount of limestone and stone slurry; f n And H n Identifying a corresponding parameter matrix under the obtained state space model;
selecting a state variable vector, an input variable vector and an output variable vector according to formulas 2-4:
x(k)＝[Δy(k) T Δy(k-1) T L Δy(k-n+1) T Δu(k-1) T Δu(k-2) T L Δu(k-n+1) T e(k) T ] T
formula 2;
u(k)＝[u 1 (k) T u 2 (k) T u 3 (k) T ] T formula 3;
y(k)＝[y 1 (k) T y 2 (k) T y 3 (k) T ] T formula 4;
wherein Δ y (k) -y (k-1), Δ u (k) -u (k-1), and the output tracking error e (k) y (k) -r (k), and r (k) is the desired output;
an input-output state space model is derived according to equations 5-11:
x (k +1) ═ ax (k) + B Δ u (k) + C Δ r (k +1) formula 5;
Figure FDA0003686263920000021
Figure FDA0003686263920000022
Figure FDA0003686263920000023
Figure FDA0003686263920000024
Figure FDA0003686263920000025
C m ＝[I q O O L O O O O]equation 11;
where O is a m x 3 dimensional zero matrix and m is a non-minimum state space vector x m (k) Dimension of (d), m ═ 6n-3, I q And I p Is a 3-dimensional unit array.
4. The method of claim 3, wherein the amount of limestone slurry, the rotational speed of the oxidation fan, and the rotational speed of the slurry pump are simultaneously controlled by the input-output state space prediction model to control the outlet SO 2 The discharge and slurry pH and ORP specifically include:
step 1, setting and selecting relevant parameters of a controller, wherein the relevant parameters of the controller specifically comprise: maximum prediction step length P, control step length M, weighting symmetric matrix Q, input increment weighting matrix L and softening coefficient mu;
step 2, initializing the state of the controller, namely detecting the output variable measured value at the current moment under a certain steady-state working condition, and taking the output variable measured value as the initial value of the state of the controller;
step 3, obtaining real-time data of the pH and ORP output variables of the slurry through online monitoring, calculating and updating X (k) and delta R, and predicting a future state vector X by adopting a prediction model shown as a formula 12-16:
x ═ fx (k) + Φ Δ U + S Δ R formula 12;
X＝[x(k+1) T x(k+2) T L x(k+P) T ] T equation 13;
ΔU＝[Δu(k) T Δu(k+1) T L Δu(k+M+1) T ] T equation 14;
F＝[A A 2 L A P ] T equation 15;
Figure FDA0003686263920000031
Figure FDA0003686263920000032
step 4, setting the reference trajectory to r (k) y (k), and r (k + i) μ i y(k)+(1-μ i )y s I-1, 2, …, P, wherein y s For the set point vector, the reference vector increment is calculated according to equation 18:
Figure FDA0003686263920000041
step 5, constructing a performance index function according to a formula 19 to solve the optimal control increment delta U:
J＝X T QX+ΔU T l Δ U equation 19;
step 6, substituting the formula 12 into the formula 19, and calculating the control vector increment Δ u (k) -k at the time k according to the extremum requirement S x(k)-k R Δ R, wherein k S 、k R Are respectively a matrix K S 、K R P is the input vector dimension, K S ＝(Φ T QΦ+L) -1 Φ T QF，K R ＝(Φ T QΦ+L) -1 Φ T QS；
Step 7, calculating an input control amount u (k) ═ u (k-1) + Δ u (k);
step 8, if u i (k)＞u max Then order u i (k)＝u max ，Δu i (k)＝u max -u i (k-1), i ═ 1,2, 3; if u is i (k)＜u min Then order u i (k)＝u min ，Δu i (k)＝u min -u i (k-1), i ═ 1,2, 3; wherein u is max And u min Respectively an upper limit value and a lower limit value of the control quantity;
and 9, outputting u (k), and repeatedly executing the steps 3 to 9 in each sampling period.
5. The method according to claim 4, characterized in that the main dynamics of the time domain P covering system are predicted, the control time domain M is not greater than the prediction time domain P, the softening coefficient is taken to be 0 < μ < 1; input incremental weighting array L ═ diag { L ═ d } 1 L 2 L L M }, wherein L j More than or equal to 0 is a weighting coefficient for controlling input increment, and j is more than or equal to 1 and less than or equal to M; weighted symmetry matrix Q ═ diag { Q } 1 Q 2 L Q P In the formula Q j ＝diag{q j,y1 L q j,y3n q j,u1 L q j,u3(n-1) q j,e1 L q j,e3 J is more than or equal to 1 and less than or equal to P, Q j The diagonal element value in the control system corresponds to the weight given to each output increment item, input increment item and error item in the control system.
6. A dynamic dual-index control device of a desulfurization system based on an input and output state space prediction model is characterized by comprising the following components:
the step response testing module is used for acquiring a step response model of the object, acquiring and analyzing field operation data based on the step response model and carrying out variable step response test on the main control loop;
the input and output state space prediction model module is used for selecting sampling time T according to the field operation data and the data result of the step response test, establishing a mathematical model between input variables and output variables of the desulfurization process by adopting a process identification method, selecting a state variable vector, an input variable vector and an output variable vector, and deriving an input and output state space prediction model;
a control module for controlling the limestone slurry amount, the rotational speed of the oxidation fan and the rotational speed of the slurry pump simultaneously through the input and output state space prediction model SO as to control the outlet SO 2 Discharge and slurry pH and ORP.
7. The apparatus of claim 6, wherein the step response testing module is specifically configured to:
acquiring a step response model of an object, acquiring and analyzing field operation data based on the step response model, respectively changing input variables in a step mode aiming at a main control loop, and recording changes of output parameters, wherein the input variables specifically comprise: limestone slurry amount, oxidation fan rotating speed and slurry circulating pump rotating speed, the output parameters specifically include: slurry pH, slurry ORP, and outlet SO2 concentration.
8. The apparatus of claim 6, wherein the input-output state space prediction model module is specifically configured to:
selecting sampling time T according to the field operation data and the dynamic characteristic test data result, and establishing a mathematical model between input variables and output variables of the desulfurization process by adopting a process identification method based on formula 1:
y(k+1)+F 1 y(k)+L+F n y(k-n+1)＝H 1 u(k)+H 2 u(k-1)+L+H n u (k-n +1) formula 1;
wherein y (k) ═ y 1 (k) T y 2 (k) T y 3 (k) T ] T ,y 1 (k) Is an outlet SO 2 Concentration, y 2 (k) Is the pH value of the slurry, y 3 (k) Is the ORP of the slurry; u (k) ═ u 1 (k) T u 2 (k) T u 3 (k) T ] T ，u 1 (k) Is the rotating speed of the slurry circulating pump u 2 (k) For oxidizing the rotational speed of the fan u 3 (k) The amount of limestone and stone slurry; f n And H n Identifying a corresponding parameter matrix under the obtained state space model;
selecting a state variable vector, an input variable vector and an output variable vector according to formulas 2-4:
x(k)＝[Δy(k) T Δy(k-1) T L Δy(k-n+1) T Δu(k-1) T Δu(k-2) T L Δu(k-n+1) T e(k) T ] T
formula 2;
u(k)＝[u 1 (k) T u 2 (k) T u 3 (k) T ] T formula 3;
y(k)＝[y 1 (k) T y 2 (k) T y 3 (k) T ] T formula 4;
wherein Δ y (k) -y (k-1), Δ u (k) -u (k-1), and the output tracking error e (k) y (k) -r (k), and r (k) is the desired output;
an input-output state space model is derived according to equations 5-11:
x (k +1) ═ ax (k) + B Δ u (k) + C Δ r (k +1) formula 5;
Figure FDA0003686263920000061
Figure FDA0003686263920000062
Figure FDA0003686263920000063
Figure FDA0003686263920000064
Figure FDA0003686263920000065
C m ＝[I q O O L O O O O]equation 11;
where O is a m x 3 dimensional zero matrix and m is a non-minimum state space vector x m (k) Dimension of (d), m ═ 6n-3, I q And I p Is a 3-dimensional unit array.
9. The apparatus according to claim 8, wherein the control module specifically comprises:
the setting submodule is used for setting and selecting relevant parameters of the controller, and the relevant parameters of the controller specifically comprise: maximum prediction step length P, control step length M, weighting symmetric matrix Q, input increment weighting matrix L and softening coefficient mu;
the initialization submodule is used for initializing the state of the controller, namely detecting a measured value of an output variable at the current moment under a certain steady-state working condition and taking the measured value as an initial value of the state of the controller;
and the online monitoring submodule is used for acquiring real-time data of the pH and ORP output variables of the slurry through online monitoring, calculating and updating X (k) and delta R, and predicting a future state vector X by adopting a prediction model shown in the formula 12-16:
x ═ fx (k) + Φ Δ U + S Δ R formula 12;
X＝[x(k+1) T x(k+2) T L x(k+P) T ] T equation 13;
ΔU＝[Δu(k) T Δu(k+1) T L Δu(k+M+1) T ] T equation 14;
F＝[A A 2 L A P ] T equation 15;
Figure FDA0003686263920000071
Figure FDA0003686263920000072
a setting submodule for setting the reference trajectory to r (k) y (k), r (k + i) μ i y(k)+(1-μ i )y s I-1, 2, …, P, wherein y s For the set point vector, the reference vector increment is calculated according to equation 18:
Figure FDA0003686263920000073
and the optimal control increment calculation submodule is used for constructing a performance index function according to a formula 19 and solving the optimal control increment delta U:
J＝X T QX+ΔU T l Δ U equation 19;
a first calculating submodule for substituting the equation 12 into the equation 19, and calculating the control vector increment Δ u (k) ═ k at the time k by the extreme value requirement S x(k)-k R Δ R, wherein k S 、k R Are respectively a matrix K S 、K R P is the input vector dimension, K S ＝(Φ T QΦ+L) -1 Φ T QF，K R ＝(Φ T QΦ+L) -1 Φ T QS；
A second calculation submodule for calculating an input control amount u (k) ═ u (k-1) + Δ u (k);
a judgment submodule for if u i (k)＞u max Then order u i (k)＝u max ，Δu i (k)＝u max -u i (k-1), i ═ 1,2, 3; if u is i (k)＜u min Then order u i (k)＝u min ，Δu i (k)＝u min -u i (k-1), i ═ 1,2, 3; wherein u is max And u min Respectively an upper limit value and a lower limit value of the control quantity;
and the output calling submodule is used for outputting u (k), and repeatedly calling the online monitoring submodule, the setting submodule, the optimal control increment calculating submodule, the first calculating submodule, the second calculating submodule, the judging submodule and the output calling submodule in each sampling period.
10. The apparatus of claim 9, wherein the dominant dynamics of the time domain P overlay system are predicted, the control time domain M is not greater than the prediction time domain P, and the softening coefficient is 0 < μ < 1; input incremental weighting array L ═ diag { L ═ d } 1 L 2 L L M In the formula, L j More than or equal to 0 is a weighting coefficient for controlling input increment, and j is more than or equal to 1 and less than or equal to M; weighted symmetric matrix Q ═ diag { Q ═ Q 1 Q 2 L Q P In the formula Q j ＝diag{q j,y1 L q j,y3n q j,u1 L q j,u3(n-1) q j,e1 L q j,e3 J is more than or equal to 1 and less than or equal to P, Q j The diagonal element value in the control system corresponds to the weight given to each output increment item, input increment item and error item in the control system.
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