CN102023574A - Optimal method for controlling mixed model of first-order reaction continuous stirred tank reactor (CSTR) - Google Patents
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- QQONPFPTGQHPMA-UHFFFAOYSA-N propylene Natural products CC=C QQONPFPTGQHPMA-UHFFFAOYSA-N 0.000 description 2
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Abstract
The invention discloses an optimal method for controlling a mixed model of a first-order reaction continuous stirred tank reactor (CSTR). The method can ensure that the first-order reaction CSTR can rapidly, accurately, stably operate under the following two conditions: a transient process that a system is started and then operates into a certain stable operating point, and a transient process that the system converts into the other stable working point from a certain stable operating point. Compared with the existing first-order reaction CSTR control method, the method provided by the invention has obvious advantages.
Description
Technical Field
The invention relates to an optimization control method for a hybrid system model framework, in particular to an optimization control method for a hybrid system model framework for a first-order reaction Continuous Stirred Tank Reactor (CSTR).
Background
The actual first-order reaction CSTR shows strong nonlinear characteristics. Although the nonlinear control technology has been developed greatly in recent years, a unified theory and method have not been formed so far, and the nonlinear control technology has not been applied well in industrial practice. The main reasons are as follows: firstly, the existing design method of the nonlinear controller often needs an accurate nonlinear model, but it is not easy to obtain the accurate nonlinear model; secondly, even if an accurate nonlinear model exists, the design of the controller based on the nonlinear model is complex; in addition, the nonlinear controller has a certain application range, and once the nonlinear controller exceeds the application range, the control performance is reduced or even fails. One of the effective approaches to solving the nonlinear control problem is a multi-model approach, whose basic idea is to divide the whole operation space of the system into several sub-regions, then build a simple local linear model/controller in each sub-region, and finally form a global model/controller from the local linear model/controller.
However, the scheduling among multiple models/controllers in the multi-model method is not scheduling under a unified framework, so that if the coordination among the local models/controllers is not good, the control performance of the whole system cannot be guaranteed, and output jitter and even instability of the whole system may be caused. If all dynamic characteristics of the subsystems can be described under a unified framework, and then a global controller is designed, the global controller is beneficial to eliminating jitter caused by model switching, reducing oscillation, and enhancing the robustness of the system, so that the control precision is improved. The recent hybrid system theory provides technical support for achieving such an object, for example, a MPC control method based on an MLD model proposed by scholars.
The global MPC controller designed on the basis of the MLD model can utilize the dynamic information of all subsystems to carry out dynamic optimization control on the system at each sampling moment so as to ensure that the system operates under the unified and integral performance index. However, the MLD model method is a hybrid system modeling method based on a logical relationship, and is therefore only suitable for a type of hybrid system switched according to states, and in addition, selecting different logical variables may cause the MLD model to be non-unique, thereby bringing difficulty to model verification.
Disclosure of Invention
The invention aims to provide a hybrid model optimization control method of a first-order reaction continuous stirred tank reactor aiming at the defects of the prior art, and the method is suitable for a first-order reaction process of controlling the concentration of raw materials by using the temperature of a cooling liquid in a CSTR.
In order to achieve the purpose, the invention adopts the following technical scheme: a hybrid system model framework optimization control method for a first-order reactive Continuous Stirred Tank Reactor (CSTR), comprising the steps of:
(1) and performing state space modeling on the first-order reaction CSTR device, and determining the working range of normal operation and the steady-state working point in each working range.
(2) Determining a hybrid system optimization model according to a state space model of a first-order reaction CSTR, which specifically comprises the following two substeps:
the state space model of the first-order reaction CSTR is linearized near the steady-state operating point for each range.
And (II) establishing a hybrid system optimization model according to the linear model of the first-order reaction CSTR.
(3) According to the optimization model of the hybrid system, solving the parameters of the control signal by using ILOG CPLEX software specifically comprises the following two substeps:
(I) converting the first-order reaction CSTR into a form processable by ILOG CPLEX software.
And (II) solving the optimization model of the hybrid system and determining control signal parameters.
(4) And synthesizing the control signal according to the control signal parameter to implement control.
The method has the beneficial effects that the method can ensure that the first-order reaction CSTR can rapidly, accurately and stably run under two conditions: a transition process from system starting to stable operation at a certain stable working point; a transition from one stable operating point to another stable operating point. The present process is particularly advantageous over existing first order reaction CSTR control processes.
Drawings
Fig. 1 is a schematic diagram of a first order reaction CSTR of an application scenario of the present invention, in which,C Afthe feed concentration of substance A,T 0Is the feed temperature,qThe feed rate,C AIs the concentration of the substance A in the chemical reaction,TIs the reactor temperature,q cIs the flow rate of the cooling liquid,T cfIs the coolant flow.
Fig. 2 is a schematic diagram of the discretization method used in the present invention.
FIG. 3 is a simulation diagram of the multi-model soft handoff control method and the method of the present invention for controlling the first-order reaction CSTR respectively (transition between steady-state operating points), in which [ Y ]*,U*]For the curves obtained by the method described in the present patent, [ Y ]sf,Usf]The curve is obtained by the multi-model soft switching control method.
FIG. 4 is a simulation diagram of the multi-model soft handoff control method and the method of the present invention for controlling the first-order reaction CSTR (transition process of starting the CSTR), respectively, in which [ Y ]*,U*]For the curves obtained by the method described in the present patent, [ Y ]sf,Usf]The curve is obtained by the multi-model soft switching control method.
Detailed Description
In order to overcome the defects of the traditional multi-model control and MLD model control, the invention provides another nonlinear system optimization control method based on a hybrid model framework. On one hand, the invention keeps the advantage of synthesizing a plurality of linear subsystems of the nonlinear system by adopting a hybrid model framework, so that all dynamic characteristics of the subsystems are described under a unified framework, and the system is ensured to operate under unified and integral performance indexes. On the other hand, the invention adopts a full-space discretization method, not only can be applied to a hybrid system switched according to the state, but also can be applied to a hybrid system switched according to the time. Firstly, synthesizing a plurality of linear subsystems obtained by linearizing a nonlinear system at different operating points by using a hybrid model frame, and establishing an optimization control proposition under a unified frame; then, introducing binary variables, discretizing the obtained hybrid model in the full space, and converting the obtained optimization control proposition into MINLP for solving. In full-space discretization, finite element orthogonal configuration method is adopted to reduce the dimension of the obtained MINLP so as to reduce the complexity of solution. And finally, introducing auxiliary variables to replace the product of continuous variables and binary variables in the MINLP constraint condition, and converting the MINLP into MIQP for solving. In addition, the invention adopts a rolling optimization strategy to make up for model mismatch brought by discretization.
The invention is applied to the transition process of switching between the starting transition process and the steady-state working point of the first-order reaction CSTR, and uses an optimization control method based on a hybrid model frame. The principle of the hybrid model framework optimization control is as follows: linearizing the state space model of the CSTR at each steady-state working point of the CSTR as the mode of the hybrid system, and further establishing the hybrid system model of the CSTR; determining the performance index of the normal work of the CSTR, and establishing an optimization control proposition of a hybrid model frame of the CSTR by combining a hybrid system model of the CSTR; converting an optimization control proposition into a mixed integer quadratic form plan by using a discretization method based on finite element configuration; and after the control signal is obtained, the CSTR is controlled by using a rolling optimization strategy, so that the CSTR operates according to a preset scheme.
The invention is applied to the transition process of switching between the starting transition process and the steady-state working point of the first-order reaction CSTR. The first-order reaction CSTR is a process of carrying out first-order reaction in the CSTR, namely, adding a certain substance A into the CSTR, and after the substance A enters the CSTR, carrying out chemical reaction to generate a substance B and achieving chemical equilibrium. Such as a styrene bulk thermal polymerization process, the first two steps of a propylene polymerization reaction (three steps in total, the first two steps are carried out in a CSTR and are a first-order reaction), and the like. For such reactions, after the CSTR device is selected, the form of the state space model is fixed, and the value of the parameter and the value range of the variable to be decided in the model are given by chemical mechanical engineers and chemical process engineers.
The invention relates to a hybrid system model framework optimization control method for a first-order reaction CSTR, which comprises the following steps:
1. the first-order reaction CSTR apparatus shown in FIG. 1 was modeled as a state space and determined for its normal operating ranges and steady state operating points within each operating range.
The first-order reaction is a process of adding a certain substance A into the CSTR, and after the substance A enters the CSTR, the substance A is subjected to chemical reaction to generate a substance B and chemical equilibrium is achieved. Such as a styrene bulk thermal polymerization process, the first two steps of a propylene polymerization reaction (three steps in total, the first two steps are carried out in a CSTR and are a first-order reaction), and the like. As shown in figure 1. The state space model for the first-order reaction CSTR was established as follows:
wherein,is the concentration of substance AC AAnd is also an output variable;i.e. CSTR temperatureT；yIs an output variable; u is the coolant temperatureT cfIs a control variable; all variables are dimensionless parameters and take on values of
. The remaining parameters are all fixed parameters of the chemical reaction, and generally take the following values:
，
，
and an
。
The specific reactions, operating ranges and steady state operating points are given by the process engineer. The first-order reaction CSTR has 3 working ranges omega1=[0,0.35），Ω2=[0.35,0.78），Ω3=[0.78,1]Each operating range having a steady state operating point whenx 1∈[0.78,1]At a steady state operating point ofxs 1=[x 1 ,x 2] T =[0.856,0.886] T When is coming into contact withx 1∈[0.35,0.78]At a steady state operating point ofxs 2=[x 1 ,x 2] T =[0.5528,0.7517] T When is coming into contact withx 1∈[0,0.35]At a steady state operating point ofxs 3=[x 1 ,x 2] T =[0.2353,0.705] T 。
2. And determining a hybrid system optimization model according to a state space model of the first-order reaction CSTR.
The state space model of the first-order reaction CSTR is linearized near the steady-state operating point for each range.
Linearizing the state space model of the first-order reaction CSTR at 3 working points within 3 working ranges to obtain 3 linearized state space model sets, as follows:
the equation (2) has 3 continuous linear state equations, wheny∈Ω1When the 1 st continuous linear state equation is in a working state, the corresponding operating range is omega1=0, 0.35). The 3 continuous linear state equations can be considered as 3 modes of the hybrid system; when the content is less than or equal to 0y<At 0.35, the 1 st state equation is in working state, and the other state equations are in non-working state, theny<0.35 may be referred to as a discrete event of the promiscuous system, and the switching of the system operation modality is a result of the action of the discrete event;ythe value of (A) determines which continuous linear state equation is in a working state, and the influence of discrete events on a continuous system is shown, and the evolution of each continuous linear state equation isyCreates conditions that manifest as the impact of a continuous system on discrete events. Thus, the expression (2) can be referred to as an approximation of the first order reaction CSTR shown in the expression (1) under the framework of the hybrid system model, each successive linear stateThe equation may be referred to as a mode of the hybrid system model.
And (II) establishing a hybrid system optimization model according to the linear model of the first-order reaction CSTR.
And establishing a first-order reaction CSTR hybrid system optimization model. The control goal of a first order reaction CSTR is to make the CSTR transition from an initial state to a target state quickly, accurately and stably, and therefore, the following objective function is used:
wherein,Q 1=1，Q 2=0.95，Q 3= 0.85. The first-order reaction CSTR hybrid system optimization model is as follows:
(3) the control signal parameters were solved using ILOG CPLEX software according to the hybrid system optimization model.
(I) converting the first-order reaction CSTR into a form processable by ILOG CPLEX software.
And (3) converting the hybrid system optimization model (4) into an ILOG CPLEX processable form. The discretization method shown in the attached figure 2 is adopted to convert the hybrid system optimization control proposition of the original nonlinear system into the MIQP problem, and the specific method is as follows:
time domain [0,10 ] for operating first order reaction CSTR]Equally divided into 10 segments, each called a finite element, each finite element having a length of 1. Introducing 3 binary variables into each finite elementy 1，y 2，y 3Corresponding to the 3 modes of equation (2), respectively. Within each finite element, there is and only one mode in the active state. In order to improve the solving precision and avoid introducing a large number of decision variables and constraint conditions, three configuration points are inserted into each finite element, a Radau configuration method is used for processing a target function and a state equation, and a second-order Lagrange interpolation polynomial is used for approximating the state variables and the control variables on each finite element. In the process industry, state variables must be continuous in time, and therefore, a linear equality constraint must be added that ensures continuity between state variables at finite element boundaries. After finite element positive mating as shown in fig. 2, equation (4) can be converted to a Mixed Integer Quadratic Program (MIQP) expressed by equation (5): the formula (5) is as follows:
J ≈ ∑ ne i=1∑ 3 j=1 Coe j [Q 1 （x i,j -x e ）2+ Q 2 u i,j 2]
s.t. ∑ 3 k=1 l k （τ j ）x i,k - h∑ M m=1（A m z 1 i,m +B m z 2 i,m +d j y i,m ） =0
∑ M m=1 y i,m =1
l 1（1）x i,0+l 2（1）x i,1+l 3（1）x i,2-x i+ ,10=0
x i,j -θ 2 y i,1-θ 3 y i,2-…-θ M+1 y i,M ≤0
-x i,j +θ 1 y i,1+θ 2 y i,2+…+θ M y i,M ≤0
-θ M+1 y i,m +z 1 i,m ≤0
θ 1 y i,m -z 1 i,m ≤0
-x i,j -θ 1 y i,m +z 1 i,m ≤-θ 1
x i,j +θ M+1 y i,m -z 1 i,m ≤θ M+1
- cy i,m +z 2 i,m ≤0
b y i,m -z 2 i,m ≤0
-u i,j - b y i,m +z 2 i,m ≤- b
u i,j + c y i,m -z 2 i,m ≤c
θ 1≤x i,j ≤θ M+1, b≤u i,j ≤c
the meanings and values of the individual unknowns in formula (5) are as follows:
z 1 i,m = x i,j y i,m ，z 2 i,m =u i,j y i,m Q 1=1.0，Q 2=0.95，Q 3=0.85，Coe 1= 1.024972，Coe 2= 0.752806，Coe 3= 0.222222，n e =10，M=3，τ 1= 0.155051
，τ 2= 0.844949，τ 3=1，
，θ 1=0，θ 2=0.35，θ 3=0.78，θ 4=1，b=-2，c=2，B 1=[0,0.3]T，B 2=[0,0.3]T，B 3=[0,0.3]T，A 1=
，A 2=
，A 3=
。
the solution of MIQP represented by equation (5) after the conversion is the numerical solution of equation (4).
And (II) solving the optimization model of the hybrid system and determining control signal parameters.
Solving the formula (5) by cplexmiqp in the software according to the use specification of ILOG CPLEX software, and obtaining the variables to be decided of the formula (5) as follows:
the state variable parameters are as follows:x ,11，x ,12，x ,13，x ,21……，x ,101， x ,102， x ,103
controlling signal parameters:u ,11，u ,12，u ,13，u ,21……，u ,101，u ,102， u ,103
binary variables:y ,11，y ,12，y ,13，y ,21……，y ,101， y ,102， y ,103
the state variable parameters, binary variables and auxiliary variables have no practical significance for implementing the control method, and the auxiliary variables are not listed here.
4. And synthesizing the control signal according to the control signal parameter to implement control.
Synthesizing a control signal using the obtained control signal parameters by:
abandonu ,21，u ,22，u ,23， u ,31……，u ,101，u ,102， u ,103Use ofu ,11，u ,12，u ,13The control signal is synthesized as follows:
wherein,t 1= 0.155051，t 2= 0.844949，t 3and = 1. In the time domain [0,1]Above, the control signal isu（t）。
Will time domain [0,1]Control signal onu（t) The signal is output by a computer through a D/A converter and converted into an industrial standard signal (4-20 mA), the industrial standard signal is applied to a hot water valve in the attached figure 1, the temperature of cooling liquid is changed by adjusting the flow of hot water, and the purpose of controlling the concentration of the substance A is achieved.
When time istWhen =1, control signalu（t) It is not effective. To be provided withtConcentration of substance A and reactor temperature at =1TIn the initial state, repeating the step (II) and the step (4) in the step (3) to obtain a time domain [0,1 ]]Control signal onu ’ （t) But this timet=1, therefore, the control signalu ’ （t) The time domain of action is [1,2 ]]. When time istWhen =2, control signalu ’ （t) Losing the effect and repeating the steps. Thus, by continuously repeating step (II) and step (4) in step (3), the first-order reaction CSTR can be operated according to a predetermined schedule. The method of applying control in this way is called a roll optimization method. The rolling optimization is a finite period optimization, and the relative form of each optimized performance index is the same, but the absolute form is different, namely the contained time domain is advanced. Through repeated online optimization, the purpose of compensating model mismatch can be achieved, which cannot be achieved by the traditional multi-model control method.
As shown in FIGS. 3 and 4, [ Y ]*,U*]For the curve obtained in the present patent, [ Y ]sf,Usf]Is a curve obtained by the traditional multi-model control method, [ Y*,U*]Relative to [ Y ] of the control signalsf,Usf]The variation is slow, which is more beneficial for the actuator to accurately track the control signal, and conversely, if the control signal oscillates too violently, the control signal is not beneficial for the tracking of the actuator.
The rolling optimization is a finite period optimization, and the relative form of each optimized performance index is the same, but the absolute form is different, namely the contained time domain is advanced. Through repeated online optimization, the purpose of compensating model mismatch can be achieved, which cannot be achieved by the traditional multi-model control method.
Claims (4)
1. A hybrid system model framework optimization control method for a first-order reactive Continuous Stirred Tank Reactor (CSTR), comprising the steps of:
(1) performing state space modeling on the first-order reaction CSTR device, and determining the working range of normal work and the steady-state working point in each working range;
(2) determining a hybrid system optimization model according to a state space model of the first-order reaction CSTR;
(3) solving control signal parameters according to the hybrid system optimization model;
(4) and synthesizing the control signal according to the control signal parameter to implement control.
2. The hybrid model optimization control method of the first-order reaction continuous stirred tank reactor according to claim 1, wherein the state space model of the first-order reaction CSTR established in the step (1) is represented by the following formula:
wherein,
is the concentration of substance AC AAnd is also an output variable;
i.e. CSTR temperatureT；yIs an output variable; u is the coolant temperatureT cfIs a control variable; all variables are dimensionless parameters and take on values of(ii) a The remaining parameters are all fixed parameters of the chemical reaction, and generally take the following values:
，
，
and an
；
The first-order reaction CSTR has 3 working ranges omega1=[0,0.35），Ω2=[0.35,0.78），Ω3=[0.78,1]Each operating range having a steady state operating point whenx 1∈[0.78,1]At a steady state operating point ofxs 1=[x 1 ,x 2] T =[0.856,0.886] T When is coming into contact withx 1∈[0.35,0.78]At a steady state operating point ofxs 2=[x 1 ,x 2] T =[0.5528,0.7517] T When is coming into contact withx 1∈[0,0.35]At a steady state operating point ofxs 3=[x 1 ,x 2] T =[0.2353,0.705] T 。
3. The hybrid model-optimized control method for a first-order stirred-tank reactor according to claim 1, wherein the step (2) comprises the following two substeps:
(A) linearizing the state space model of the first-order reaction CSTR near the steady-state operating point in each range;
(B) and establishing a hybrid system optimization model according to a linearization model of the first-order reaction CSTR.
4. The hybrid model optimization control method of the first-order reaction continuous stirred tank reactor according to claim 1, wherein the step (3) comprises the following two substeps:
(a) converting the first-order reaction CSTR into a processable form of ILOG CPLEX software;
(b) and solving the hybrid system optimization model to determine control signal parameters.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103309234A (en) * | 2013-06-08 | 2013-09-18 | 浙江大学 | Orthogonal collocation optimization based batch reactor control system |
| CN103324086A (en) * | 2013-06-08 | 2013-09-25 | 浙江大学 | Batch reactor control system based on accurate punishment optimization |
| CN103995469A (en) * | 2014-05-30 | 2014-08-20 | 河海大学 | Method for designing controller of non-minimum-phase constant-temperature continuous stirred tank reactor |
| CN104035336A (en) * | 2014-05-30 | 2014-09-10 | 河海大学 | Method for designing non-linear controller of MIMO non-minimum phase CSTR |
| CN104950670A (en) * | 2015-06-10 | 2015-09-30 | 浙江大学 | Integrated multi-model method for controlling CSTRs (continuous stirred tank reactors) |
| CN105893654A (en) * | 2016-03-11 | 2016-08-24 | 中南大学 | Robust predictive control method for first-order continuous stirred tank reactor (CSTR) |
| CN106126784A (en) * | 2016-06-16 | 2016-11-16 | 上海电机学院 | A kind of Optimized model of continuous stirred tank reactor |
| CN109870908A (en) * | 2019-02-27 | 2019-06-11 | 南京理工大学 | A kind of sampling control method for continuous stirred tank reactor system |
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2004062786A1 (en) * | 2003-01-14 | 2004-07-29 | The Universtiy Court Of The University Of St Andrews | Catalysed reaction system using supercritical fluid |
| CN101626825A (en) * | 2007-03-08 | 2010-01-13 | 伊士曼化工公司 | Polyester production system employing an unagitated esterification reactor |
-
2010
- 2010-12-31 CN CN2010106169566A patent/CN102023574B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2004062786A1 (en) * | 2003-01-14 | 2004-07-29 | The Universtiy Court Of The University Of St Andrews | Catalysed reaction system using supercritical fluid |
| CN101626825A (en) * | 2007-03-08 | 2010-01-13 | 伊士曼化工公司 | Polyester production system employing an unagitated esterification reactor |
Non-Patent Citations (3)
| Title |
|---|
| 《Chinese Journal of Chemical Engineering》 20070815 杜静静等 基于MLD模型的CSTR建模和控制(英文) 说明书第2部分-第6部分 1,3,4 , 第04期 2 * |
| 《信息与控制》 19920430 陈申等 CSTR的非线性自适应控制 全文 1-4 第21卷, 第2期 2 * |
| 《系统仿真学报》 20090430 李晓理等 《连续搅拌釜的模糊多模型自适应控制》 全文 1-4 第21卷, 第7期 2 * |
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