Control device and control method of partition wall rectifying tower and partition wall rectifying tower

Abstract

The embodiment of the invention provides a control device and a control method of a partition wall rectifying tower and the partition wall rectifying tower, and belongs to the technical field of partition wall rectifying. The control device comprises a first control loop which comprises a first sensor used for obtaining a detection value of a first controlled parameter; the MPC loop is used for generating a first manipulated variable according to the detection value and the set value of the first controlled parameter so as to perform first control on the first manipulated parameter; the second control loop comprises a second sensor used for acquiring a detection value of a second controlled parameter; and the PI controller is used for generating a second manipulated variable according to the detection value and the set value of the second controlled parameter so as to perform second control on the second manipulated parameter. According to the method, PI control and MPC are combined, so that the state parameters of the isolation wall rectifying tower are more accurately controlled, the control stability of the isolation wall rectifying tower is improved, and the dynamic performance effect of the isolation wall rectifying tower is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of dividing wall distillation, and in particular to a control device and a control method for a dividing wall distillation tower and a dividing wall distillation tower. Background Art

[0002] DWDC (Dividing Wall Distillation Column) has obvious advantages in the integration and intensification of the distillation process, which can significantly reduce energy consumption, save costs and floor space.

[0003] However, due to the complex internal structure, numerous variables, and strong coupling of DWDCs, concerns about their controllability have limited their widespread application in industry. Traditional PID control can stabilize DWDC operation, but its performance is often suboptimal. Summary of the Invention

[0004] The present invention provides a control device and method for a dividing wall distillation column, as well as a dividing wall distillation column. The control device utilizes a cascade control structure that combines MPC (Model Predictive Control) and PI (Proportional Integral Control). The secondary control loop utilizes PI control to rapidly stabilize the DWDC, while the primary control loop utilizes MPC control to mitigate the high nonlinearity inherent in the DWDC due to its strong coupling.

[0005] To achieve the above objectives, an embodiment of the present invention provides a control device for a dividing wall distillation column, the control device comprising: a first control loop including: a first sensor for obtaining a detection value of a first controlled parameter of the dividing wall distillation column; an MPC loop for generating a first manipulated variable through model prediction and online optimization based on the detection value of the first controlled parameter and the set value of the first controlled parameter, and determining the set value of a second controlled parameter of the dividing wall distillation column based on the first manipulated variable; and a first control module for performing a first control on the first manipulated parameter of the dividing wall distillation column based on the first manipulated variable, wherein the second controlled parameter is one or more of the first manipulated parameters; and a second control loop connected in cascade with the first control loop, the second control loop comprising: a second sensor for obtaining a detection value of a second controlled parameter; a PI controller for generating a second manipulated variable through a proportional-integral method based on the detection value of the second controlled parameter and the set value of the second controlled parameter; and a second control module for performing a second control on the second manipulated parameter of the dividing wall distillation column based on the second manipulated variable.

[0006] Optionally, the first controlled parameter and the second controlled parameter are any one of the following: the concentration, liquid level, pressure, flow rate of a set component in the dividing wall distillation tower, or one or more temperatures of a set tray in the dividing wall distillation tower.

[0007] Optionally, the control device is a concentration-temperature cascade control structure, the first sensor is a concentration sensor, and the second sensor is a temperature sensor, wherein the first controlled parameter is the concentration of the set component in the dividing wall distillation tower, and the second controlled parameter is one or more temperatures of the set tower plate; and the first manipulation parameter includes: one or more temperatures of the set tower plate, the heat load of the reboiler of the dividing wall distillation tower, and the heat-coupled steam flow rate of the pre-fractionation tower refluxed to the dividing wall distillation tower.

[0008] Optionally, the set component concentration includes: the molar fraction of the impurity component or the molar fraction of the purity component.

[0009] Optionally, the molar fraction of the impurity components in the dividing wall distillation tower includes: the molar fraction of each impurity component in one or more streams of the main tower of the dividing wall distillation tower; and the molar fraction of each impurity component in the top stream and the bottom stream of the pre-fractionation tower of the dividing wall distillation tower; and the molar fraction of the purity component in the dividing wall distillation tower includes: the molar fraction of each purity component in one or more streams of the main tower.

[0010] Optionally, the MPC loop includes: a prediction model module for determining a prediction vector of the first controlled parameter at the second moment based on the state parameters of the partition wall distillation tower at the first moment; a feedback correction module for obtaining a predicted update value of the first controlled parameter at the second moment based on the deviation between the predicted vector of the first controlled parameter at the first moment and the true value; and an online optimization module for optimizing the performance index of the first controlled parameter at each sampling moment in the prediction time domain based on the predicted update value at the second moment and the set value of the first controlled parameter to generate the first manipulated variable.

[0011] Optionally, the prediction model module is also used to determine the state parameters of the partition wall distillation tower at the second moment and the prediction vector of the first controlled parameter at the first moment and the second moment based on the state parameters, first manipulated variable and disturbance vector of the partition wall distillation tower at the first moment.

[0012] Optionally, the prediction model module is used to determine the state parameter of the dividing wall distillation column at the second moment and the prediction vector of the first controlled parameter at the first moment and the second moment, including: according to the vector x(k) of the state parameter at moment k, the vector u(k) of the first manipulated variable, and the disturbance vector d(k), determine the state parameter x(k+1) at moment k+1, the prediction vector y(k) of the first controlled parameter at moment k, and the prediction vector y(k+1) at moment k+1 by the following formula:

[0013] x(k+1)=Ax(k)+Bu(k),

[0014] y(k)=Cx(k)+Dd(k),

[0015] y(k+1)=Cx(k+1)+Dd(k+1),

[0016] Among them, A, B, and C are the model coefficient matrices of the prediction model module, which are obtained by linearizing the nonlinear dynamic process of the partition wall distillation column under stable operation; and D is a zero matrix.

[0017] Optionally, the MPC loop also includes: a feedback correction module, used to obtain an updated value of the prediction vector of the first controlled parameter at the second moment based on the deviation between the prediction vector and the true value of the first controlled parameter at the first moment; and an online optimization module, used to optimize the performance index of the first controlled parameter at each sampling moment in the prediction time domain based on the updated value of the prediction vector at the second moment, so as to determine the optimal control sequence in the future control time domain, and only select the manipulated variable at the first moment to determine the set value of the second controlled parameter of the isolation wall distillation tower.

[0018] On the other hand, the present invention further provides a dividing wall distillation tower, comprising: the control device for the dividing wall distillation tower described above.

[0019] On the other hand, the present invention also provides a control method for a partition wall distillation tower, the control method comprising: obtaining a detection value of a first controlled parameter of the partition wall distillation tower; generating a first manipulated variable through model prediction and online optimization according to the detection value of the first controlled parameter and the set value of the first controlled parameter, so as to perform a first control on the first manipulated parameter of the partition wall distillation tower according to the first manipulated variable, wherein the set value of the second controlled parameter is determined based on the first manipulated variable, and the second controlled parameter is one or more of the first manipulated parameters; obtaining a detection value of a second controlled parameter of the partition wall distillation tower; and generating a second manipulated variable through a proportional integral method according to the detection value of the second controlled parameter and the set value of the second controlled parameter, so as to perform a second control on the second manipulated parameter of the partition wall distillation tower according to the second manipulated variable.

[0020] Through the above technical solution, the present invention combines PI control with MPC to achieve more precise control of the state parameters of the dividing wall distillation tower, thereby improving the control stability of the dividing wall distillation tower and improving the dynamic performance effect of the dividing wall distillation tower.

[0021] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation of the embodiments of the present invention.

[0023] In the picture:

[0024] Figure 1 This is a schematic diagram of the cascade control structure of the dividing wall distillation column provided by the present invention.

[0025] Figure 2 The present invention provides a temperature control structure realized by pure PI control of the dividing wall distillation column.

[0026] Figure 3 The present invention provides an impurity concentration-temperature cascade control structure implemented by pure PI control of a dividing wall distillation column.

[0027] Figure 4 The present invention provides a product concentration-temperature cascade control structure implemented by pure PI control of a dividing wall distillation column.

[0028] Figure 5 The present invention provides an impurity concentration-temperature cascade control structure realized by combining MPC and PI control of a dividing wall distillation column.

[0029] Figure 6 The present invention provides a product concentration-temperature cascade control structure realized by combining MPC and PI control of a dividing wall distillation column.

[0030] Figures 7a-7l Comparative curves of dynamic responses of the three pure PI control structures of the dividing wall distillation tower provided by the present invention after interference is added.

[0031] Figures 8a-8l The dynamic response comparison curves of the PI-ICC and MPC-ICC control structures of the dividing wall distillation column provided by the present invention after interference is added.

[0032] Figures 9a-9l The dynamic response comparison curves of the PI-PCC and MPC-PCC control structures of the dividing wall distillation column provided by the present invention after interference is added.

[0033] Figures 10a-10l The dynamic response comparison curves of the PI-TC and MPC-PCC control structures of the dividing wall distillation column provided by the present invention after interference is added. DETAILED DESCRIPTION

[0034] The following describes the specific implementation of the embodiment of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiment of the present invention and is not used to limit the embodiment of the present invention.

[0035] The present invention first provides a control device for a dividing wall distillation column, which may include a first control loop and a second control loop connected in series. The first control loop may include a first sensor, an MPC loop, and a first control module, and the second control loop may include a second sensor, a PI controller, and a second control module.

[0036] Wherein, the first sensor is used to detect the detection value (i.e., input variable) of the first controlled parameter of the dividing wall distillation column. The MPC loop can be used to generate a first manipulated variable (i.e., output variable) through model prediction and online optimization based on the detection value of the first controlled parameter and the set value of the first controlled parameter, so as to perform a first control on the first manipulated parameter of the dividing wall distillation column according to the first manipulated variable through the first control module; and determine the set value of the second controlled parameter of the dividing wall distillation column based on the first manipulated variable. Wherein, the first manipulated variable is the adjustment value of the first manipulated parameter. It can be understood that the MPC loop can obtain the target value of the first manipulated parameter based on the detection value of the first controlled parameter and the set value of the first controlled parameter, and then generate a feedback manipulated variable (i.e., the first manipulated variable) based on the difference between the detection value and the target value of the first manipulated parameter to adjust the first manipulated parameter. It can be seen that the first manipulated variable and the first manipulated parameter are the same type of parameters. Wherein, the set value of the second controlled parameter can be determined based on the first manipulated variable, and the second controlled parameter is one or more of the first manipulated parameters.

[0037] The first controlled parameter and the first manipulated parameter are both one or more parameters in the dividing wall distillation column. For example, they can be any one or more of the following parameters: concentration, liquid level, pressure, flow rate of a set component in the dividing wall distillation column, or one or more temperatures of a set tray. Furthermore, the first controlled parameter and the first manipulated parameter can be parameters of the same category (e.g., both are temperature, concentration, etc.) or parameters of different categories (e.g., one is temperature, the other is concentration, etc.).

[0038] The second sensor is used to detect the detection value (i.e., input variable) of the second controlled parameter of the dividing wall distillation column, wherein the second controlled parameter is one or more of the first manipulated parameters. The PI controller is used to generate a second manipulated variable (i.e., output variable) by proportional-integral method based on the detection value of the second controlled parameter and the set value of the second controlled parameter, so as to perform a second control on the second manipulated parameter of the dividing wall distillation column according to the second manipulated variable through the second control module. Wherein, the second controlled parameter and the second manipulated parameter are both one or more parameters in the dividing wall distillation column, for example, they can be any one or more of the following parameters: the concentration, liquid level, pressure, flow rate of the set component in the dividing wall distillation column, or one or more temperatures of the set trays. At the same time, the two can be parameters of the same category (e.g., both are temperature, concentration, etc.), or parameters of different categories (e.g., one is temperature and the other is valve opening, etc.). Therefore, the innovation of the present invention lies in combining the model predictive control method with the proportional-integral control method and applying it to the dividing wall distillation column to achieve cascade control of two control loops.

[0039] In one embodiment, the first controlled parameter and the second controlled parameter may be any one of the following: concentration, liquid level, pressure, flow rate of a set component in the dividing wall distillation column, or one or more temperatures of a set tray in the dividing wall distillation column.

[0040] In one embodiment, the first manipulation parameter and the second manipulation parameter can be any one or more of the following parameters: concentration, liquid level, pressure, flow rate of a set component in the dividing wall distillation column, one or more temperatures of a set tray, or opening of a set valve.

[0041] In one embodiment, the control device is a concentration-temperature cascade control structure. In this case, the first sensor may be a concentration sensor, and the second sensor may be a temperature sensor.

[0042] For details, please refer to Figure 1 Schematic diagram of the structure: the first control loop is the main control loop, and the second control loop is the sub-control loop. For the main control loop using MPC, the first controlled parameter can be the concentration y(k) of the set component in the dividing wall distillation column. For the sub-control loop using PI control, the second controlled parameter can be the set temperature T(k) of one or more trays. d(k) is the disturbance vector, such as the feed flow disturbance, and r(k) is the output reference vector. For the main control loop, the vector u(k) of the first manipulated variable is the set point of the second controlled parameter (e.g., the set temperature T(k) of one or more trays). Because the main and sub-loops are in a cascade control relationship, the output of the MPC is the set point of the PI controller. Therefore, the first manipulated parameter may include, but is not limited to, the second controlled parameter. For example, in addition to the set temperature T(k) of one or more trays (the second controlled parameter), the first manipulated parameter may also include: the heat load of the reboiler of the dividing wall distillation column and the heat-coupled steam flow rate of the pre-fractionation tower refluxed to the dividing wall distillation column.

[0043] Furthermore, the primary control loop using MPC is a fixed-value system that generates a corresponding control signal (the first manipulated variable) based on changes in product concentration. The secondary control loop using PI control is a follower system, which has a fast response speed and enhances the robustness of the DWDC system. Therefore, MPC can handle the complex dynamic characteristics of the dividing wall distillation column, improving control performance, while the PI controller can quickly stabilize the dividing wall distillation column and enhance the response speed of MPC. The two can be cascaded to maintain stable operation of the dividing wall distillation column.

[0044] As can be seen from the above technical solution, the present invention fully utilizes the combination of model predictive control and proportional-integral control to achieve more precise cascade control of the state parameters (such as concentration and temperature parameters) of the dividing wall distillation column. This improves the control stability of the dividing wall distillation column and further enhances the dynamic performance of the dividing wall distillation column. In addition, this control strategy can enhance the separation effect and process efficiency of the dividing wall distillation column in practical applications.

[0045] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific implementation examples and with reference to the accompanying drawings.

[0046] Example: A control structure combining model predictive control and proportional integral control of the present invention is used to implement concentration-temperature cascade control of a Petlyuk dividing wall distillation column.

[0047] The Petlyuk dividing wall distillation column used below is a single tower, consisting of a pre-fractionator, main tower, condenser, and reboiler. A condenser is installed at the top of the tower, and a reboiler at the bottom. The secondary control loop uses proportional-integral control to control the temperature of the sensitive plate, ensuring rapid and stable operation of the DWDC (dividing wall distillation column). The primary control loop uses model predictive control to control product concentration to meet product purity requirements. The output of the MPC primary control loop is the set point for the PI controller, and the two are cascaded to maintain stable operation of the DWDC.

[0048] Figure 2-Figure 6 In the Petlyuk column, the feeds consist of ethanol (E), n-propanol (P), and n-butanol (B) saturated liquids at equal molar fractions, with a feed rate of 1000 mol / h. The heat load of the overhead condenser is 26.007 kW, and the heat load of the bottom reboiler is 25.107 kW. The operating pressure at the top of the column is 1 atm, and the pressure drop per plate within the column is 0.0068 atm. The molar reflux ratio at the top of the column is 4.46. The vapor split ratio is 2.16, and the liquid split ratio is 1. With three product streams within the column, the product purity of each of the three streams is 99% by mole fraction.

[0049] Comparative example: The principle of temperature control of DWDC by pure PI control is as follows Figure 2 As shown. In the PI-TC control structure, the controlled parameters (i.e., input variables) and manipulated variables (i.e., output variables) are both 3. Among them, the 3 controlled parameters can be the temperature of the 6th sensitive plate in the pre-fractionation tower and the temperatures of the 9th and 47th sensitive plates in the main tower, corresponding to Figure 2The detection quantity input of the temperature controller TC1, TC2, and TC3. The three control parameters can be the flow rate of the liquid phase coupling stream flowing back into the pre-fractionation tower from the main tower, the reflux flow rate at the top of the main tower, and the flow rate of the side stream product, corresponding to Figure 2 The manipulated variable outputs of the temperature controllers TC1, TC2, and TC3 are shown in FIG. The pressure, level, and flow control are not shown in the schematic diagram, but those skilled in the art can obtain technical insights into the pressure, level, and flow control from this embodiment, so they will not be described in detail.

[0050] The principle of achieving DWDC concentration-temperature cascade control through pure PI control is as follows: Figure 3 and Figure 4 As shown in the figure, the secondary control loop—temperature control loop—has a faster response time and lower equipment and maintenance costs than the primary control loop—concentration control loop, making it more widely used in industry. However, it cannot maintain constant product purity, and component concentration control is slow. However, this control method can reduce steady-state deviations in product purity, adjusting product purity toward the desired value. Therefore, concentration-temperature cascade control combines the advantages of both concentration and temperature control, offering both high speed and the ability to maintain product purity.

[0051] exist Figure 3 Impurity concentration control (PI-ICC) and Figure 4 In the two control structures for product concentration control (PI-PCC), the temperature control loop is identical, and the controlled and manipulated parameters of the two concentration controllers (CCP1 and CCP2), located at the top and bottom of the pre-fractionation tower, are also identical. In CCP1, the controlled parameter is the mole fraction of impurity B in the pre-fractionation tower's top product, and the manipulated parameter is the set point of temperature controller TC1. In CCP2, the controlled parameter is the mole fraction of impurity E in the pre-fractionation tower's bottom product, and the manipulated parameter is the heat-coupled steam flow rate refluxed to the pre-fractionation tower. The manipulated parameters of the three stream concentration controllers in the main tower are also identical: the set points of TC2 and TC3, as well as the reboiler heat load. However, the controlled parameters differ. The PI-ICC control structure uses the mole fractions of the impurity components in each of the three main tower streams as the controlled parameters, while the PI-PCC control structure uses the mole fractions of the purity components in each of the three streams as the controlled parameters for the three main tower concentration controllers. The parameters of each controller are shown in Tables 1 and 2.

[0052] Table 1 PI-ICC loop controller parameters

[0053]

[0054] Table 2 PI-PCC loop controller parameters

[0055]

[0056] During the implementation of the present invention, the inventors discovered that while traditional PID control can stabilize DWDC operation, its performance is often suboptimal. For example, the aforementioned use of pure PI control suffers from the drawbacks of difficulty maintaining constant product purity and slow control of component concentrations. Meanwhile, model predictive control (MPC) is an advanced, multi-input, multi-output intelligent control system that can significantly improve the dynamic performance of complex chemical systems. However, its response speed is typically slower than that of PID control, making the application of pure MPC to complex DWDCs limited. Therefore, to further enhance control performance, a cascade control structure combining MPC and PI control can be employed to improve DWDC process control performance.

[0057] Specifically, the present invention proposes the following embodiment of a concentration-temperature cascade control method combining MPC and PI control. By combining PI control with MPC, more precise control of the concentration and temperature in a distillation column is achieved, thereby improving the robustness of the control system. PI control is used in the secondary control loop, while MPC is used in the primary control loop. This allows the control structure to remain operational even if MPC in the primary control loop is inoperative, thereby enhancing the stability of the MPC-PI control.

[0058] In this embodiment, the concentration of the set component may include the mole fraction of the impurity component or the mole fraction of the purity component. The mole fraction of the impurity component in the dividing wall distillation column may include the mole fraction of each impurity component in one or more streams of the main column of the dividing wall distillation column, as well as the mole fraction of each impurity component in the top stream and bottom stream of the pre-fractionation column of the dividing wall distillation column. The mole fraction of the purity component in the dividing wall distillation column may include the mole fraction of each purity component in one or more streams of the main column.

[0059] Specifically, the impurity concentration control (MPC-ICC) structure and product concentration control (MPC-PCC) structure proposed in the present invention are as follows: Figure 5 and Figure 6 As shown in Figure 1. The secondary control loop controls the tray temperature using a PI controller. The temperature controller used here is the same controller as that used in pure PI control. The primary control loop uses MPC to control concentration. The outputs of the MPC-ICC and MPC-PCC control structures are the same, with five manipulated variables: the set points of the temperature controllers TC1, TC2, and TC3, the reboiler heat load (Q R ) and the heat-coupled steam flow rate (V p ). The inputs of the MPC-ICC and MPC-PCC control structures are different, which are the five controlled parameters of their respective systems.

[0060] Take the MPC-ICC control structure as an example, Figure 5 As shown in Table 3, the five controlled parameters are the mole fractions of the impurity components in the top and bottom streams of the pre-fractionation tower, and the mole fractions of the impurity components in the three streams of the main tower. The specific parameters are shown in Table 3. In order to further reduce the steady-state error, the present invention proposes an MPC-PCC control structure, such as Figure 6 Among them, the five controlled parameters are the mole fractions of the impurity components in the top and bottom streams of the pre-fractionation tower, and the mole fractions of the purity components in the three streams of the main tower.

[0061] Table 3 MPC weight factor parameters in the MPC-ICC structure

[0062]

[0063] In one embodiment, the MPC loop may include three modules: a prediction model, feedback correction, and online optimization. It can be understood that the prediction model module can optimize the performance indicator of the first controlled parameter at each sampling time within the prediction time domain based on the predicted value of the first controlled parameter at the second time and the set value of the first controlled parameter through the online optimization module to determine the optimal control sequence in the future control time domain, and only select the first manipulated variable at the first time, that is, the optimal value of the first manipulated parameter at the first time, to adjust the first manipulated parameter.

[0064] The prediction model module is configured to determine a prediction vector of the first controlled parameter at a second moment based on the state parameters of the dividing wall distillation column at the first moment. In one embodiment, the prediction model module may also be configured to perform the following function: determine the state parameters of the dividing wall distillation column at a second moment and the prediction vectors of the first controlled parameter at the first moment and the second moment based on the state parameters of the dividing wall distillation column at the first moment, the first manipulated variable, and the disturbance vector. The second moment is the next moment that is a set time interval from the first moment.

[0065] The predictive model in the predictive model module is the core of the MPC loop. It can be used to calculate the controlled parameter value from time k to time k + j. For example, the state space model is a linear time-invariant model and can be used as the predictive model in this paper.

[0066] Specifically, the prediction model module can determine the state parameter x(k+1) at the second moment, the prediction vector y(k) of the first controlled parameter at the first moment k, and the prediction vector y(k+1) at the second moment k+1 according to the vector x(k) of the state parameter at the first moment k, the vector u(k) of the first manipulated variable, and the disturbance vector d(k):

[0067] x(k+1)=Ax(k)+Bu(k) (1)

[0068] y(k)=Cx(k)+Dd(k) (2)

[0069] y(k+1)=Cx(k+1)+Dd(k+1) (3)

[0070] Among them, A, B, and C are the model coefficient matrices of the prediction model module, which are obtained by linearizing the nonlinear dynamic process of the dividing wall distillation column under stable operation; D is the zero matrix.

[0071] Specifically, the A and B matrices are coefficient matrices for the state update of the bulkhead distillation column, and the C matrix is the influence matrix of the input on the state parameters of the bulkhead distillation column. In addition, the matrices A, B, and C are sparse matrices and can therefore be obtained by linearizing the bulkhead distillation column in a stable operating state. For example, the present invention uses the controller design interface (CDI) of the chemical process flow simulation software Aspen PlusDynamics to define the input and output variables of the controller. Then, by performing Taylor expansion at the initial point to achieve linearization, and ignoring high-order terms, the A, B, and C matrices can be obtained. The linear time-invariant state space model of the bulkhead distillation column can be determined by the A, B, and C matrices.

[0072] The feedback correction module can be used to obtain the predicted updated value of the first controlled parameter at the second moment based on the deviation between the predicted vector of the first controlled parameter at the first moment and the true value. The feedback correction module can be used to obtain the updated value of the predicted vector at the second moment based on the deviation between the predicted vector of the first controlled parameter at the first moment and the true value (i.e., the detected process output). The feedback correction step can be based on the difference between the detected process output y(k) and the predicted value y m The deviation between (k|k) is used to calculate the prediction vector y calculated by the above prediction model m (k+j|k) is fed back and corrected to obtain the updated value y(k+j|k) of the prediction vector.

[0073] An online optimization module is configured to optimize the performance index of the first controlled parameter at each sampling moment in the prediction time domain based on the predicted updated value at the second moment and the set value of the first controlled parameter to generate the first manipulated variable. The online optimization module can be configured to optimize the performance index of the first controlled parameter at each sampling moment in the prediction time domain based on the updated value of the prediction vector to determine the optimal control sequence in the future control time domain. In other words, the optimal value of the first manipulated variable can be obtained through the online optimization module in the MPC loop. Specifically, the online optimization module optimizes the performance index of the first controlled parameter at each sampling moment using the following objective function equation to obtain the optimal control sequence:

[0074] min

[0075] Au(k|k),...,Δu(m-1+k|k),ε

[0076]

[0077] Where p is the prediction horizon, during which the online optimization module performs optimization. m is the control horizon, during which the control input is allowed to vary. The prediction horizon p and the control horizon m are set to maintain the stability of the DWDC system and reduce oscillations.

[0078] The objective function in equation (3) above consists of three parts: the deviation w between the controlled parameter and the expected value y The sum of squares and the change in the manipulated variable w Δu The sum of squares of the values of , and the sum of squares of the deviations between the corresponding target values. In the present invention, since there is no target manipulated variable, the third part is not needed. Therefore, w u Take 0, and w Δu and w y Needs to be calibrated.

[0079] The sampling time Δk can usually be considered infinitesimal. In addition, when model predictive control (MPC) is implemented on a digital computer platform, the prediction model used is usually discrete. When discretizing a continuous dynamic model, it is necessary to select an appropriate sampling time Δk to ensure that the discrete model accurately reproduces the system dynamics. A smaller sampling period (i.e., the time interval between adjacent sampling times) can more accurately and quickly feed back information to the controller and suppress the impact of disturbances. However, an excessively small sampling period increases the computational complexity and hardware requirements of online optimization.

[0080] Therefore, the selection of the sampling period requires a comprehensive balance between the control effectiveness and computational workload of the dynamic system. The prediction horizon p and the control horizon m are typically multiples of the sampling period. Smaller p values result in faster dynamic response but poorer stability, while larger p values improve stability but result in slower dynamic response. Furthermore, the control horizon m must be smaller than p. A smaller m can compromise control effectiveness, while a larger m increases control input flexibility but reduces stability.

[0081] Generally speaking, when w y Larger and w Δu When w is small, the controller allows the manipulated variable to change significantly in the control loop, thereby ensuring a better control result. At the same time, such a result helps to reduce the adjustment time of the control system, but it is easy to increase the number of oscillations and overshoot of the control system, affecting the smooth operation of the system. y Smaller and w Δu When it is large, it helps the control system reduce the number of oscillations and reduce overshoot, but at the same time it will increase the adjustment time of the system. Therefore, the MPC weight factor w y and w Δu Debugging requires weighing and analyzing the dynamic and steady-state performance of the control system.

[0082] In another embodiment, Figures 7a-7l As shown in the figure, three pure PI control structures (PI-TC, PI-ICC, and PI-PCC) were subjected to ±20% feed flow rate disturbances and ±20% feed composition disturbances, respectively, to observe the dynamic response comparison curves of the different control structures. Comparing the three control structures, PI-ICC exhibited the highest number of oscillations and the longest stabilization time, while PI-TC exhibited the shortest stabilization time and the smallest overshoot. This demonstrates that the PI-TC control structure can achieve a faster response while maintaining product purity.

[0083] like Figures 8a-8l As shown in the figure, the dynamic response comparison curves of ±20% feed flow disturbance and ±20% feed component disturbance in the PI-ICC and MPC-ICC control structures are shown. The dynamic performance of the MPC-ICC control structure is significantly better than that of the PI-ICC control structure, with fewer oscillations, shorter time required for stabilization, smaller maximum deviation and steady-state deviation. However, when the feed E component is subjected to a +20% disturbance or the feed B component is subjected to a -20% disturbance, the steady-state deviation of the side stream is relatively large. Therefore, the steady-state deviation can be further reduced based on the MPC-PCC control structure proposed in the present invention.

[0084] like Figures 9a-9lFigure 9 shows a comparison of the dynamic responses of the PI-PCC and MPC-PCC control structures to a ±20% feed flow rate disturbance and a ±20% feed composition disturbance. Both the PI-PCC and MPC-PCC control structures exhibit very small steady-state deviations. Figure 9 shows that the MPC-PCC control structure significantly outperforms the PI-PCC structure in dynamic performance, exhibiting fewer oscillations, a shorter stabilization time, and a smaller maximum deviation. These results demonstrate that the introduction of MPC significantly improves the dynamic performance of the DWDC system.

[0085] like Figures 10a-10l As shown, the dynamic response comparison curves of PI-TC and MPC-PCC control structures to ±20% feed flow disturbance and ±20% feed component disturbance are displayed. PI-TC has the advantage of fast response speed compared with PI-ICC and PI-PCC control structures. The results show that when the MPC of the main control loop in the MPC-PCC control structure fails, the PI-TC control structure can still work. The time required for the stabilization of the PI-TC control structure is close to the time required for the stabilization of the MPC-PCC control structure. The maximum deviation and steady-state deviation of MPC-PCC are both smaller than those of PI-TC. Therefore, MPC-PCC is the best control structure among the five control structures studied in this invention, which proves that MPC is very suitable for complex, strongly coupled, multi-input, and multi-output isolation wall distillation columns.

[0086] The present invention adopts a product concentration-temperature cascade control structure combining model predictive control and proportional integral control to obtain very good dynamic response effect, with the least number of oscillations, the shortest time required for stabilization, and very small maximum deviation and steady-state deviation.

[0087] On the other hand, the present invention further provides a dividing wall distillation tower, which may include: the control device for the dividing wall distillation tower described above.

[0088] On the other hand, the present invention also provides a method for controlling a dividing wall distillation column, which may include the following steps:

[0089] Obtaining a detection value of a first controlled parameter of the dividing wall distillation column;

[0090] generating a first manipulated variable through model prediction and online optimization according to a detected value of the first controlled parameter and a set value of the first controlled parameter, so as to perform a first control on a first manipulated parameter of the dividing wall distillation column according to the first manipulated variable, wherein a set value of a second controlled parameter is determined based on the first manipulated variable, and the second controlled parameter is one or more of the first manipulated parameters;

[0091] obtaining a detected value of a second controlled parameter of the dividing wall distillation column; and

[0092] A second manipulated variable is generated by proportional integration according to the detected value of the second controlled parameter and the set value of the second controlled parameter, so as to perform a second control on the second manipulated parameter of the dividing wall distillation column according to the second manipulated variable.

[0093] The beneficial effects of the dividing wall distillation tower and the control method of the dividing wall distillation tower provided by the present invention can be referred to the above description of the control device of the dividing wall distillation tower, which will not be repeated here.

[0094] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0095] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A control device for a dividing wall distillation column, characterized in that: The control device comprises: The first control loop includes: a first sensor, configured to obtain a detection value of a first controlled parameter of the dividing wall distillation column; an MPC loop, configured to generate a first manipulated variable through model prediction and online optimization according to the detected value of the first controlled parameter and the set value of the first controlled parameter, and determine the set value of a second controlled parameter of the dividing wall distillation column based on the first manipulated variable; and a first control module, configured to perform a first control on a first manipulated parameter of the dividing wall distillation column according to the first manipulated variable, wherein the second controlled parameter is one or more of the first manipulated parameters; and A second control loop is connected in series with the first control loop, and the second control loop includes: a second sensor, configured to obtain a detection value of the second controlled parameter; a PI controller, configured to generate a second manipulated variable by a proportional-integral method according to the detected value of the second controlled parameter and the set value of the second controlled parameter; and The second control module is configured to perform a second control on a second manipulated parameter of the dividing wall distillation column according to the second manipulated variable.

2. The control device according to claim 1, characterized in that The first controlled parameter and the second controlled parameter are any one or more of the following: The concentration, liquid level, pressure, flow rate of a set component in the dividing wall distillation column, or one or more temperatures of a set tray in the dividing wall distillation column.

3. The control device according to claim 2, characterized in that The control device is a concentration-temperature cascade control structure, the first sensor is a concentration sensor, the second sensor is a temperature sensor, wherein the first controlled parameter is the concentration of a set component in the dividing wall distillation column, and the second controlled parameter is one or more temperatures of the set tray; and The first operating parameters include: one or more temperatures of the set tray, a heat duty of a reboiler of the dividing wall distillation column, and a heat coupling steam flow rate of a pre-fractionation column refluxed to the dividing wall distillation column.

4. The control device according to claim 2 or 3, characterized in that: The set component concentration includes: the molar fraction of the impurity component or the molar fraction of the purity component.

5. The control device according to claim 4, characterized in that The molar fraction of the impurity components in the dividing wall distillation column includes: the mole fraction of each impurity component in one or more streams of the main column of the dividing wall distillation column; and the mole fraction of each impurity component in the top stream and the bottom stream of the pre-fractionation column of the dividing wall distillation column; and The mole fraction of the purity component in the dividing wall distillation column includes: the mole fraction of each purity component in one or more streams of the main column.

6. The control device according to claim 1, characterized in that The MPC loop includes: a prediction model module, configured to determine a prediction vector of the first controlled parameter at a second moment based on a state parameter of the dividing wall distillation column at a first moment; a feedback correction module, configured to obtain a predicted updated value of the first controlled parameter at the second moment based on a deviation between a predicted vector and a true value of the first controlled parameter at the first moment; and An online optimization module is used to optimize the performance index of the first controlled parameter at each sampling moment in the prediction time domain based on the predicted update value at the second moment and the set value of the first controlled parameter to generate the first manipulated variable.

7. The control device according to claim 6, characterized in that The prediction model module is also used to determine the state parameters of the dividing wall distillation tower at the second moment and the prediction vectors of the first controlled parameters at the first moment and the second moment based on the state parameters, first manipulated variables and disturbance vector of the dividing wall distillation tower at the first moment.

8. The control device according to claim 7, characterized in that: The prediction model module is used to determine the state parameters of the dividing wall distillation column at the second moment and the prediction vectors of the first controlled parameter at the first moment and the second moment, including: According to the state parameter vector x(k) at time k, the vector u(k) of the first manipulated variable, and the disturbance vector d(k), the state parameter x(k+1) at time k+1, the prediction vector y(k) of the first controlled parameter at time k, and the prediction vector y(k+1) at time k+1 are determined by the following formula: x(k+1)=Ax(k)+Bu(k), y(k)=Cx(k)+Dd(k), y(k+1)=Cx(k+1)+Dd(k+1), Among them, A, B, and C are the model coefficient matrices of the prediction model module, which are obtained by linearizing the nonlinear dynamic process of the partition wall distillation tower under stable operation; and D is a zero matrix.

9. A dividing wall distillation tower, characterized in that: The dividing wall distillation column comprises: a control device for the dividing wall distillation column according to any one of claims 1 to 8.

10. A method for controlling a dividing wall distillation column, characterized in that: The control method includes: Obtaining a detection value of a first controlled parameter of the dividing wall distillation column; generating a first manipulated variable through model prediction and online optimization according to the detected value of the first controlled parameter and the set value of the first controlled parameter, so as to perform a first control on the first manipulated parameter of the dividing wall distillation column according to the first manipulated variable, wherein the set value of a second controlled parameter is determined based on the first manipulated variable, and the second controlled parameter is one or more of the first manipulated parameters; obtaining a detected value of a second controlled parameter of the dividing wall distillation column; and A second manipulated variable is generated by proportional integration according to the detected value of the second controlled parameter and the set value of the second controlled parameter, so as to perform a second control on the second manipulated parameter of the dividing wall distillation column according to the second manipulated variable.

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