CN103365207B - A kind of control method of industrial process and equipment - Google Patents

Abstract

The invention discloses an industrial process control method and equipment. The method includes: setting a steady-state input variable weight for each input variable in a steady-state corresponding to a steady-state input variable, and setting a steady-state input variable target value for at least one steady-state input variable. Based on the objective function and the steady-state function relationship model between multiple steady-state output variables and multiple steady-state input variables, optimize the calculation to obtain the extreme value of the objective function under the condition of meeting the preset conditions. The value of the steady-state input variable at the next moment is used as the value of the steady-state input variable at the next moment. The value of the steady-state input variable at the next moment is transmitted to the basic control loop to control the controllable variable of the industrial equipment. The method provided by the invention can realize the industrial process control based on the target set point under the premise of taking into account the economic performance of the system.

Description

A method and device for controlling an industrial process

technical field

The invention relates to the field of industrial process control, in particular to an industrial process control method and equipment.

Background technique

Process control systems used in industrial processes typically have multiple input variables and multiple output variables that vary as those input variables change. The plurality of input variables are typically controllable variables of the industrial equipment performing the industrial process, while the plurality of output variables may be variables related to the outcome of the operation of the industrial process. The multivariable control of industrial process can be divided into two layers, the upper layer is steady-state optimization, and the lower layer is dynamic control. The actual industrial process is dynamic, so the state of the system is constantly changing. The steady state of an industrial process refers to the steady state of the system when the time tends to infinity. Steady-state optimization of industrial process is to obtain the steady-state operating point of the system that makes the production process performance better when the system is in a steady state under certain system performance and given constraints. The steady-state operating point may be represented by a steady-state value of the input variable and a steady-state value of the output variable. Furthermore, the controllable variable can be set according to the obtained steady-state value of the input variable in the steady-state operating point to implement industrial process control.

In order to obtain the steady-state operating point of the system, the steady-state optimization method of industrial process can be realized by using the objective function reflecting the economic performance of the production process. These economic properties can be the economic benefits generated by the production process or the costs consumed. Steady-state optimization for economic performance aims to obtain the steady-state operating point when the economic performance is optimal, and then use the input and output variable values of the steady-state operating point for process control to achieve better economic performance goals.

Usually, the existing steady-state optimization method of the industrial process is to find the working point at the next moment to optimize the economic performance of the production process on the basis of the current operating point of the system. The goal of this method is to obtain the new operating point of the system at the next moment based on the current operating point of the system. Although this method considers the economic performance of the production process, this method is only based on the operating point of the system at the current moment. Not tracking to an arbitrary steady state set target point.

For example, in a steady-state optimization method, the minimum value of the objective function that reflects economic performance is calculated when certain boundary conditions are met.

The objective function can be:

$\underset{\mathrm{<span\; class="notranslate">\; \&Delta;</span>}{\mathrm{<span\; class="notranslate">\; u</span>}}_{<span\; class="notranslate">\; \&infin;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; min</span>}}\mathrm{<span\; class="notranslate">\; J</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; C</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}\mathrm{<span\; class="notranslate">\; \&Delta;</span>}{\mathrm{<span\; class="notranslate">\; u</span>}}_{<span\; class="notranslate">\; \&infin;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span>$

Wherein, C ^T =[c ₁ c ₂ ...c _m ] is a set of cost coefficients.

Indicates the difference between a set of steady-state input variables at the next moment and the steady-state input variables at the current moment.

Steady-state optimization methods for industrial processes can not only achieve better process control to achieve better economic performance, but also achieve target point tracking with an arbitrary steady-state set point as the goal. Target point tracking refers to finding a new steady-state operating point at the next moment on the basis of the steady-state operating point at the current moment of the system after setting the steady-state set point, so that the new steady-state operating point is as close as possible to the desired point. Set the steady-state set point.

In a method for tracking the target set point, firstly, the target set point is set, which is obtained by real-time optimization by the nonlinear steady-state optimizer in the upper layer of the system,

Then select the quadratic objective function for calculation, the formula of the objective function is expressed as:

$\underset{\mathrm{<span\; class="notranslate">\; \&Delta;</span>}{\mathrm{<span\; class="notranslate">\; u</span>}}_{<span\; class="notranslate">\; \&infin;</span>}}{\mathrm{<span\; class="notranslate">\; min</span>}}\mathrm{<span\; class="notranslate">\; J</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{<span\; class="notranslate">\; \&infin;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>}_{\mathrm{<span\; class="notranslate">\; Q</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{<span\; class="notranslate">\; \&infin;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>}_{\mathrm{<span\; class="notranslate">\; R</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>$

The meaning of this objective function is to find a new steady-state operating point (U _∞ (k+1), Y _∞ (k+1)) at the next moment based on the steady-state operating point of the system at the current moment, so that the new The distance between the steady-state operating point and the given target set point ( _{UT, Y T )} is the shortest in the sense of least squares, that is, the closest to the target set point.

Although this method can make the new steady-state operating point (U _∞ (k+1), Y _∞ (k+1)) close to (U _T , Y _T ) to a certain extent, this target set point tracking The method is only aimed at the closeness to the target setting, without considering the economic performance, and has poor practicability for industrial process control.

Contents of the invention

The technical problem to be solved by the embodiment of the present invention is to realize the control of the industrial process by setting the target set point under the premise of taking certain economic performance into consideration.

In order to solve the above technical problem, according to one aspect of the present invention, an embodiment of the present invention provides a method for controlling an industrial process, the industrial process has multiple input variables and multiple output variables, the plurality of input variables are controllable variables of the industrial equipment performing the industrial process, the plurality of output variables are variables related to the operation results of the industrial process, the plurality of input variables and The plurality of steady-state input variables and the plurality of steady-state output variables corresponding to the plurality of output variables in a steady state need to meet preset conditions,

It is characterized in that the method comprises:

Step 1. Set the steady-state input variable weight for each steady-state input variable, and set the steady-state input variable target value for at least one steady-state input variable;

Step 2. Carry out optimization calculation based on the objective function and the steady-state functional relationship model between the multiple steady-state output variables and the multiple steady-state input variables, so as to obtain Next, make the objective function obtain the value of the steady-state input variable at the next moment of extreme value as the value of the steady-state input variable at the next moment, the objective function is based on the weight of the steady-state input variable, the steady-state The target value of the input variable is a parameter, and the steady-state input variable at the next moment is a function of the variable;

Step 3. Sending the value of the steady-state input variable at the next moment to the basic control loop to control the controllable variable of the industrial equipment.

Preferably, the objective function is a linear function or a quadratic function of the product of the difference between the steady-state input variable at the next moment and the target value of the steady-state input variable and the weight of the steady-state input variable.

Preferably, the weight of the steady-state input variable is a cost value involved in a unit change in the value of the steady-state input variable.

Preferably, the steady-state functional relationship model between the plurality of steady-state input variables and the plurality of steady-state output variables is that the steady-state output increment is a linear combination and correction of the steady-state input increment and the disturbance input increment The sum of the errors, the coefficients of the linear combination are determined according to the steady-state model of the object,

The steady-state output increment is the difference between the steady-state output variable at the next moment and the steady-state output variable at the current moment,

The steady-state input increment is the difference between the steady-state input variable at the next moment and the steady-state input variable at the current moment,

The disturbance input increment is the difference between the disturbance input value at the current moment and the disturbance input value at the previous moment.

Preferably, the control method also includes:

setting a steady-state input variable target range for the at least one steady-state input variable, the steady-state input variable target value being within the steady-state input variable target range;

Step 2 and Step 3 are repeatedly executed, so that the finally obtained steady-state input variable value of the at least one steady-state input variable at the next moment is within the target range of the steady-state input variable.

Preferably, it also includes:

setting a steady-state output variable target range for the at least one steady-state output variable,

Wherein, by repeatedly executing the steps 2 and 3, the next value of the at least one steady-state output variable obtained through the steady-state functional relationship model is also made according to the value of the steady-state input variable at the next moment. The value of the steady-state output variable at any moment is within the target range of the steady-state output variable.

Preferably, part of the steady-state input variables among the plurality of steady-state input variables are first steady-state input variables without a target set point, and part of the steady-state input variables are second steady-state input variables with a target set point set variable, the target setpoint includes a steady-state input variable target value,

The objective function is:

The linear function of the product of the difference between the first steady-state input variable at the next moment and the first steady-state input variable at the current moment and the weight of the first steady-state input variable, plus the second steady-state input variable and the steady-state input variable at the next moment a linear function of the product of the difference between the target value and the weight of the second steady-state input variable, or

The quadratic function of the product of the difference between the first steady-state input variable at the next moment and the first steady-state input variable at the current moment and the weight of the first steady-state input variable, plus the second steady-state input variable at the next moment and the steady-state input A quadratic function of the product of the difference between the variable's target value and the weight of the second steady-state input variable.

According to a second aspect of the present invention, there is also provided a control device for an industrial process, the industrial process has a plurality of input variables and a plurality of output variables that vary with changes in the input variables, the plurality of The input variables are controllable variables of industrial equipment that executes the industrial process, the plurality of output variables are variables related to the operation results of the industrial process, and the plurality of input variables and the plurality of output variables are stable The corresponding multiple steady-state input variables and multiple steady-state output variables in the state need to meet the preset conditions,

It is characterized in that the control device includes:

A setting device is used to set the steady-state input variable weight for each steady-state input variable, and set the steady-state input variable target value for at least one steady-state input variable;

An optimization calculation device, configured to perform optimization calculations based on the objective function and the steady-state function relationship model between the plurality of steady-state output variables and the plurality of steady-state input variables, so as to obtain In the case of conditions, the objective function obtains the value of the steady-state input variable at the next moment of the extremum value as the steady-state input variable value at the next moment, and the objective function is based on the weight of the steady-state input variable, the The target value of the steady-state input variable is a parameter, and the steady-state input variable at the next moment is a function of the variable;

The industrial equipment control device is used to transmit the steady-state input variable value at the next moment to the basic control loop to control the controllable variables of the industrial equipment.

Preferably, the objective function is a linear function or a quadratic function of the product of the difference between the steady-state input variable at the next moment and the target value of the steady-state input variable and the weight of the steady-state input variable.

Preferably, the control device also includes:

target range setting means for setting a steady-state input variable target range for said at least one steady-state input variable, said steady-state input variable target value being within said steady-state input variable target range,

The optimization calculation means executes the optimization calculation multiple times, so that the finally obtained value of the steady-state input variable at the next moment of the at least one steady-state input variable is within the target range of the steady-state input variable.

Through the control method of the industrial process provided by the present invention, the optimization calculation is carried out based on the objective function and the steady-state function relationship model between the steady-state input variable and the steady-state output variable, and the objective function is based on the weight of the preset steady-state input variable The target value of the value and the steady-state input variable is a parameter, and the steady-state input variable at the next moment that makes the objective function obtain the extreme value is calculated as the steady-state input variable at the next moment. The setting of the weight value of the steady-state input variable and the acquisition of the extreme value of the objective function reflect the consideration of the economic performance of the system. Under the premise of tracking the target set point, the steady-state input variable obtained at the next moment is more practical, and then the controllable variable of the industrial equipment is set to the obtained steady-state input variable value at the next moment to achieve Better performance industrial process control.

Description of drawings

Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings.

The accompanying drawings, which constitute a part of this specification, illustrate the embodiments of the invention and together with the description serve to explain the principles of the invention.

The present invention can be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

Fig. 1 shows a schematic flow chart of an embodiment of a control method provided by the present invention;

Fig. 2 shows the schematic diagram of setting the steady-state input variable target range in the present invention;

Fig. 3 shows the schematic diagram of setting the target range of the steady-state output variable in the present invention;

FIG. 4 shows a schematic diagram of the trajectory of the steady-state input variable value at the next moment in the embodiment of the control method provided by the present invention;

Fig. 5 shows a schematic structural diagram of an embodiment of a control device provided by the present invention.

detailed description

Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of steps, numerical expressions and numerical values set forth in these examples do not limit the scope of the present invention unless specifically stated otherwise.

The following description of at least one exemplary embodiment is merely illustrative in nature and in no way taken as limiting the invention, its application or uses.

Techniques, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such techniques, methods and devices should be considered part of the Authorized Specification.

In all examples shown and discussed herein, any specific values should be construed as exemplary only, and not as limitations. Therefore, other examples of the exemplary embodiment may have different values.

It should be noted that like numerals and letters denote like items in the following figures, therefore, once an item is defined in one figure, it does not require further discussion in subsequent figures.

An industrial process has multiple control input variables and multiple output variables that vary as these multiple input variables change. Multiple input variables are controllable variables of industrial equipment that execute industrial processes, and multiple output variables are variables related to the operating results of industrial processes. For a linear multivariable process, the relationship between multiple input variables and multiple output variables The correspondence of can be described by a certain functional relationship model. The functional relational model is determined by the essential characteristics of the system, and the control output variable can be obtained according to the control input variable by using the functional relational model of the system, and vice versa.

There are many kinds of functional relationship models between the input variables and output variables of the industrial process system. After obtaining the functional relationship model of the system, it is necessary to perform a steady-state treatment on the functional relationship model to obtain the steady-state functional relationship model between the steady-state input variables and the steady-state output variables when the system reaches a steady state. Input variables and steady-state output variables correspond to the representation of the input variables and output variables of the system in a steady state.

There can be many kinds of steady-state functional relationship models of multiple steady-state input variables and multiple steady-state output variables of the industrial process. For example, a steady-state functional relationship model can be expressed as that the steady-state output increment is the sum of the linear combination of the steady-state input increment and the disturbance input increment and the correction error, and the coefficient of the linear combination is determined according to the object steady-state model, where , the steady-state output increment is the difference between the steady-state output variable at the next moment and the steady-state output variable at the current moment, and the steady-state input increment is the difference between the steady-state input variable at the next moment and the steady-state input variable at the current moment Difference, the disturbance input increment is the difference between the disturbance input value at the current moment and the disturbance input value at the previous moment.

For the actual industrial production process, there are certain boundary constraints on the steady-state input variables and steady-state output variables respectively. Therefore, multiple steady-state input variables and multiple steady-state output variables need to meet certain preset conditions, so that the steady-state input variables at the next moment and the steady-state output variables at the next moment are located at the upper boundary values of their respective boundary constraints and the lower boundary value.

In addition, the steady-state input variables and steady-state output variables will also be restricted by the steady-state incremental constraints during the execution of optimization and control, and the steady-state input increments and steady-state output increments are located in their respective incremental constraint between the upper and lower bounds of . The steady-state input increment is the difference between the steady-state input variable at the next moment and the steady-state input variable at the current moment, and the steady-state output increment is the difference between the steady-state output variable at the next moment and the steady-state output variable at the current moment value,

Referring to FIG. 1 , which is a schematic flowchart of an embodiment of the industrial process control method of the present invention, the steps of the embodiment of the industrial process control method of the present invention will be described in detail below.

The industrial processes described in the various embodiments have multiple input variables and multiple output variables that vary as the multiple input variables change. The plurality of input variables are controllable variables of the industrial equipment performing the industrial process, and the plurality of output variables are variables related to the operation result of the industrial process. Multiple steady-state input variables and multiple steady-state output variables are representations of multiple input variables and multiple output variables of the system when the system is in a steady state. Multiple steady-state input variables and multiple steady-state output variables are related to multiple The input variables correspond to a plurality of output variables.

The plurality of steady-state input variables and the plurality of steady-state output variables satisfy preset conditions, namely the aforementioned boundary constraint conditions and incremental constraint conditions.

In step 101, a steady-state input variable weight is set for each steady-state input variable, and a steady-state input variable target value is set for at least one steady-state input variable.

Since there is a linear correlation between the steady-state input increment ΔU _∞ (k) and the steady-state output increment ΔY _∞ (k) represented by the above-mentioned steady-state functional relationship model, the two can be unified as the steady-state input increment ΔU _∞ (k) means.

After the steady-state input increment ΔU _∞ (k) is used to represent the steady-state output increment ΔY _∞ (k), the steady-state input variable weight is set for the steady-state input increment ΔU _∞ (k) for a specific industrial process. The method of setting the weight may be to standardize the benefit or cost generated by the unit increment of the steady-state input variable, and use the standardized parameter to represent the benefit or cost of each steady-state input variable. That is, the steady-state input variable weight is the cost value involved in the unit change of the steady-state input variable value. Costs and benefits can be distinguished using the ± symbol, + for costs and - for benefits. For example, the set of steady-state input variable weights for each steady-state input variable can be expressed as C ^T =[c ₁ c ₂ . . . c _m ], where m is the number of steady-state input variables.

Multiple steady-state input variables of an industrial process may have different characteristics, and some of the steady-state input variables need to set target set points. The steady-state operating point at the next moment is calculated by the target tracking method of steady-state optimization. In the case of optimal performance, make the steady-state operating point at the next moment as close as possible to the set target set point. However, for another part of the steady-state input variables, the target set point is not set, and the steady-state operating point at the next moment is only calculated by the target tracking method of steady-state optimization. When the economic performance is optimal, the next The steady-state operating point at the moment is as close as possible to the steady-state input variable value at the current moment.

For the steady-state input variable that needs to set the target set point, in addition to setting the weight of the steady-state input variable, it is also necessary to set the target value U _T of the steady-state input variable.

The target set point is the expected steady-state operating point of the system, and the target set point includes a steady-state input variable target value U _T and a steady-state output variable target value Y _T . Due to the linear correlation between the steady-state input variable target value U _T and the steady-state output variable target value Y _T , by setting the steady-state input variable target value U _T and the linear correlation between them, the steady-state State output variable target value Y _T .

The target set point can be the result of optimization calculation by the optimizer on the upper layer of the system, or it can be an ideal value given by the process personnel based on experience.

In step 102, based on the objective function and the steady-state functional relationship model between multiple steady-state output variables and multiple steady-state input variables, optimization calculations are performed to obtain the objective The function obtains the value of the steady-state input variable at the next moment of the extremum, and uses it as the value of the steady-state input variable at the next moment. The objective function is a function that takes the weight of the steady-state input variable, the target value of the steady-state input variable as parameters, and the steady-state input variable at the next moment as a variable.

In order to reflect the economic performance of the industrial process system, the objective function can be selected as a function that takes the steady-state input variable weight and the steady-state input variable target value as parameters, and the next-time steady-state input variable as a variable. Since the weight of the steady-state input variable is related to a certain economic index of the steady-state input variable, the value of the steady-state input variable at the next moment when the objective function obtains the extreme value is used as The value of the steady-state input variable at the next moment can realize the extreme value of this economic index, and by setting the target value of the steady-state input variable as the target set point for tracking, it can realize the realization of the economic performance of the system under the premise of taking into account the economic performance of the system. Tracking of target set points.

The objective function may be a linear function or a quadratic function of the product of the difference between the steady-state input variable and the target value of the steady-state input variable at the next moment and the weight of the steady-state input variable.

In step 103, the value of the steady-state input variable at the next moment is transmitted to the basic control loop to control the controllable variables of the industrial equipment. The controllable variable of the industrial equipment is set to the steady-state input variable value obtained in step 102 at the next moment, so that industrial process control closer to the set steady-state input variable target value and taking into account economy can be realized.

In the process of optimizing the calculation to obtain the value of the steady-state input variable at the next moment when the objective function obtains the extreme value, multiple steady-state output variables can pass between multiple steady-state input variables and multiple steady-state output variables obtained from the steady-state functional relationship model. Therefore, it is also possible to _{obtain the next stable working state (U ∞ (} k+1), _{Y ∞} (k+1)).

The next stable working state (U _∞ (k+1), Y _∞ (k+1)) obtained based on the objective function not only satisfies the above preset conditions, that is, in another embodiment, it can be further Add the soft constraints on (U _∞ (k+1), Y _∞ (k+1)), which means setting the steady-state input variable target range (U _Tmin , U _Tmax ) for the steady-state input variable, and the input variable target value U _T is in the target range of the steady-state input variable, and set the target range of the steady-state output variable (Y _Tmin , Y _Tmax ) for the steady-state output variable, satisfying the following infinitives:

U _Tmin ≤ U _∞ (k+1) ≤ U _Tmax

Y _Tmin ≤ Y _∞ (k+1) ≤ Y _Tmax

in, and are the lower bound and upper bound of the target range of the set steady-state input variable, respectively. Usually, the soft constraints are stricter conditions than the aforementioned preset conditions. Therefore, the values of U _Tmin and U _Tmax are different from U _min and U _max . Generally speaking, UT _min ≥ U _min , U _Tmax ≤ U _max .

Referring to FIG. 2 , the value range of ( _UTmin , U _Tmax ) is smaller than that of (U _min , U _max ), and U _{T_range} in the figure indicates the range of possible values of U _T.

Referring to FIG. 3 , similarly, the value range of (Y _Tmin , Y _Tmax ) is smaller than that of (Y _min , Y _max ). In FIG. 3 , Y _{T_range} represents the range in which Y _T can take values.

In the process of using the objective function for optimization calculation to realize the tracking of the target set point, the result of an optimization calculation (U _∞ (k+1), Y _∞ (k+1)) may not satisfy the soft constraints. Steady-state input and steady-state output variable target range, so the iterative method can be used to perform optimization calculations multiple times, so that the final steady-state input variable U _∞ (k+1) at the next moment is within the steady-state input variable target range (U _Tmin , U _Tmax ), and the output variable steady state Y _∞ (k+1) at the next moment of at least one steady-state output variable obtained through the steady-state functional relationship model is within the output variable target range (Y _Tmin , Y _Tmax ).

Referring to Figure 4, this figure is an example of the steady-state value of the steady-state input variable at the next moment, showing that the steady-state input variable value U _∞ (k+1) at the next moment reaches the steady-state input variable target after multiple iterative calculations Trajectory diagram within the range ( _UTmin , U _Tmax ). In Figure 4, ΔU _∞ ′(k) represents the difference between the steady-state input variable U _∞ (k+1) and the target value U _T of the steady-state input variable at the next moment, and ΔU _∞ (k) represents the steady-state input variable at the next moment The difference between the variable U _∞ (k+1) and the steady-state input variable U _∞ (k) at the current moment.

Further, after obtaining the steady-state input variable U _∞ (k+1) at the next moment, U _∞ (k+1) can be transmitted to the basic control loop to control the controllable variables of the industrial equipment.

After U _∞ (k+1) is transmitted to the basic control loop for control, the system is controlled by the basic control loop, and the state will change, so that step 102 in the above embodiment can be executed cyclically at the next moment, both according to the steady state Input the variable target value, and perform optimization calculation based on the objective function and the steady-state function relationship model to obtain a new steady-state input variable value at the next moment and a new steady-state output variable value at the next moment, and then execute step 103 for control, That is, step 102 and step 103 are repeatedly executed, so that the value of the steady-state input variable at the next moment of the finally obtained at least one steady-state input variable is within the target range of the steady-state input variable, and according to the value of the steady-state input variable at the next moment The value of the steady-state output variable at the next moment of at least one steady-state output variable obtained through the steady-state functional relationship model is within the target range of the steady-state output variable.

In the process of loop optimization calculation, the target value of the input variable can usually not be changed after being set.

In addition, as an optional implementation, the steady-state input variable target range ( _UTmin , U _Tmax ) can also be set only for the steady-state input variable, and the steady-state input variable target value is within the input variable target range. Step 102 and step 103 are repeatedly executed, so that the final steady-state input variable value U _∞ (k+1) at the next moment of the steady-state input variable is within the steady-state input variable target range ( _UTmin , U _Tmax ) Inside. In this embodiment, the steady-state output variable Y _∞ (k+1) at the next moment only needs to meet the constraints preset by the system.

As mentioned above, according to the fact that multiple input variables may have different characteristics, in the calculation process of an industrial process control, for multiple steady-state input variables corresponding to multiple input variables, it may be necessary to set some steady-state input variables Set the target set point. Under the condition of achieving the best economic performance, optimize the calculation by setting the target set point method so that the steady-state operating point at the next moment is as close as possible to the set target set point. For another part of the steady-state input variables, the target set point is not set, and the steady-state operating point at the next moment is calculated to be as close as possible to the steady-state operating point at the current moment when the economic performance is optimal.

Specifically, part of the steady-state input variables among the plurality of steady-state input variables are first steady-state input variables without a target set point, and part of the steady-state input variables are second steady-state input variables with a target set point set, The target set point consists of a steady-state input variable target value, and the target function can be:

The linear function of the product of the difference between the first steady-state input variable at the next moment and the first steady-state input variable at the current moment and the weight of the first steady-state input variable, plus the second steady-state input variable and the steady-state input variable at the next moment a linear function of the product of the difference between the target value and the weight of the second steady-state input variable, or

The quadratic function of the product of the difference between the first steady-state input variable at the next moment and the first steady-state input variable at the current moment and the weight of the first steady-state input variable, plus the second steady-state input variable at the next moment and the steady-state input A quadratic function of the product of the difference between the variable's target value and the weight of the second steady-state input variable.

Referring to Fig. 5, the present invention also provides a control device of an industrial process corresponding to the control method of the present invention, which is a schematic structural diagram of an embodiment of the control device.

The industrial process has multiple input variables and multiple output variables that change with the change of the multiple input variables. The multiple input variables are controllable variables of the industrial equipment that executes the industrial process, and the multiple output variables are the operating results of the industrial process. Relevant variables, a plurality of input variables and a plurality of output variables corresponding to a plurality of steady-state input variables and a plurality of steady-state output variables in a steady state need to meet preset conditions, and the control device 500 includes a setting device 501, an optimization A computing device 502 and an industrial equipment control device 503 .

The setting means 501 is used for setting the weight value of the steady-state input variable for each steady-state input variable, and setting the target value of the steady-state input variable for at least one steady-state input variable.

The optimization calculation device 502 is used to perform optimization calculations based on the objective function and the steady-state functional relationship model between multiple steady-state output variables and multiple steady-state input variables, so as to obtain The objective function obtains the value of the steady-state input variable at the next moment of extreme value as the value of the steady-state input variable at the next moment. The objective function takes the steady-state input variable weight and the steady-state input variable target value as parameters, and the next moment The steady-state input variable is a function of the variable.

The industrial equipment control device 503 is used to transmit the steady-state input variable value at the next moment to the basic control loop to control the controllable variables of the industrial equipment.

In another embodiment, the objective function is a linear function or a quadratic function of the product of the difference between the steady-state input variable and the target value of the steady-state input variable at the next moment and the weight of the steady-state input variable.

In another embodiment, the control device further comprises target range setting means.

The target range setting device is used for setting the target range of the steady-state input variable for at least one steady-state input variable, the target value of the steady-state input variable is within the target range of the steady-state input variable,

The optimization calculation device executes the optimization calculation multiple times, so that the finally obtained steady-state input variable value of at least one steady-state input variable at the next moment is within the target range of the steady-state input variable.

So far, an industrial process control method and device according to the present invention have been described in detail. Certain details well known in the art have not been described in order to avoid obscuring the inventive concept. Based on the above description, those skilled in the art can fully understand how to implement the technical solutions disclosed herein.

In addition, the connection relationship between the constituent devices of the device in the embodiment of the present invention is only an example of the information flow relationship based on the present invention, and is not limited to a physical connection relationship, and it is not necessarily necessary or limited to realize the embodiment of the present invention. of.

It is possible to implement the method and apparatus of the invention in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware or any combination of software, hardware, and firmware. The above sequence of steps used in the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present invention can also be implemented as programs recorded in recording media including machine-readable instructions for realizing the method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

Although some specific embodiments of the present invention have been described in detail through examples, those skilled in the art should understand that the above examples are for illustration only, rather than limiting the scope of the present invention. Those skilled in the art will appreciate that modifications can be made to the above embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.

Claims (10)

1. A method for controlling an industrial process, comprising,

the industrial process has a plurality of input variables which are controllable variables of an industrial device executing the industrial process and a plurality of output variables which are variables related to an operation result of the industrial process, and a plurality of steady-state input variables and a plurality of steady-state output variables corresponding to the plurality of input variables and the plurality of output variables in a steady state need to satisfy a preset condition,

characterized in that the method comprises:

step 1, setting a steady-state input variable weight value for each steady-state input variable, and setting a steady-state input variable target value for at least one steady-state input variable;

step 2, performing optimization calculation based on an objective function and a steady-state function relationship model between the steady-state output variables and the steady-state input variables to obtain a value of a steady-state input variable at the next moment, which makes the objective function obtain an extreme value under the condition of meeting the preset condition, as a steady-state input variable value at the next moment, wherein the objective function is a function which takes the weight value of the steady-state input variable, the target value of the steady-state input variable as parameters, and the steady-state input variable at the next moment as a variable;

and 3, transmitting the steady-state input variable value at the next moment to a basic control loop to control the controllable variable of the industrial equipment.

2. The control method according to claim 1, wherein the objective function is a first order function or a second order function of a product of a difference between the steady-state input variable and the steady-state input variable target value at the next time and the steady-state input variable weight.

3. The control method according to claim 1, wherein the steady-state input variable weight is a cost value related to a unit change in the steady-state input variable value.

4. The control method of claim 1, wherein the steady state functional relationship model between the plurality of steady state input variables and the plurality of steady state output variables is that a steady state output increment is a sum of a correction error and a linear combination of a steady state input increment and a disturbance input increment, coefficients of the linear combination being determined according to a subject steady state model,

the steady state output increment is a difference between a steady state output variable at a next time and a steady state output variable at a current time,

the steady state input increment is the difference between the steady state input variable at the next time and the steady state input variable at the current time,

the disturbance input increment is the difference between the disturbance input value at the current moment and the disturbance input value at the last moment.

5. The control method according to claim 4, characterized by further comprising:

setting a steady-state input variable target range for the at least one steady-state input variable, the steady-state input variable target value being within the steady-state input variable target range;

and repeating the step 2 and the step 3 for a plurality of times, so that the steady-state input variable value of the at least one steady-state input variable obtained finally at the next moment is within the steady-state input variable target range.

6. The control method according to claim 5, characterized by further comprising:

setting a steady state output variable target range for at least one steady state output variable,

and repeating the step 2 and the step 3 for a plurality of times, so that the steady-state output variable value of the at least one steady-state output variable obtained by the steady-state functional relationship model at the next moment is within the steady-state output variable target range according to the steady-state input variable value at the next moment.

7. The control method according to claim 2,

wherein a portion of the plurality of steady state input variables are first steady state input variables for which a target set point is not set, and a portion of the steady state input variables are second steady state input variables for which the target set point is set, the target set point comprising a steady state input variable target value,

the objective function is:

a linear function of the product of the weight of the first steady-state input variable and the difference between the first steady-state input variable and the first steady-state input variable at the next moment, and a linear function of the product of the weight of the second steady-state input variable and the difference between the target value of the second steady-state input variable and the steady-state input variable at the next moment, or

The second steady-state input variable weight is calculated by adding the product of the difference between the first steady-state input variable and the current first steady-state input variable at the next moment and the first steady-state input variable weight to the second steady-state input variable weight.

8. A control apparatus for an industrial process having a plurality of input variables which are controllable variables of an industrial apparatus which executes the industrial process and a plurality of output variables which are variables relating to an operation result of the industrial process, which are variables that vary with a change in the plurality of input variables, a plurality of steady-state input variables and a plurality of steady-state output variables corresponding to the plurality of input variables and the plurality of output variables in a steady state are required to satisfy a preset condition,

characterized in that the control device comprises:

the setting device is used for setting a steady-state input variable weight value for each steady-state input variable and setting a steady-state input variable target value for at least one steady-state input variable;

an optimization calculation device, configured to perform optimization calculation based on an objective function and a steady-state function relationship model between the multiple steady-state output variables and the multiple steady-state input variables, so as to obtain, as a next-time steady-state input variable value, a value of a next-time steady-state input variable that causes the objective function to obtain an extremum when the preset condition is satisfied, where the objective function is a function in which the steady-state input variable weight value, the steady-state input variable target value are used as parameters, and the next-time steady-state input variable is used as a variable;

and the industrial equipment control device is used for transmitting the steady-state input variable value at the next moment to the basic control loop to control the controllable variable of the industrial equipment.

9. The control apparatus according to claim 8, wherein the objective function is a first-order function or a second-order function of a product of a difference between the steady-state input variable and the steady-state input variable target value at the next time and the steady-state input variable weight.

10. The control apparatus according to claim 8, characterized by further comprising:

target range setting means for setting a steady-state input variable target range for the at least one steady-state input variable, the steady-state input variable target value being within the steady-state input variable target range,

the optimization calculation means repeatedly executes the optimization calculation a plurality of times so that the steady-state input variable value at the next moment of the at least one steady-state input variable finally obtained is within the steady-state input variable target range.