Batch rectification system based on intelligent control

Abstract

The invention relates to a batch rectification system based on intelligent control, and relates to the technical field of batch rectification. A data acquisition module; the data acquisition module is used for acquiring temperature, pressure, flow and material concentration data in real time; the control module is used for receiving various data of the data acquisition module; a model prediction control algorithm is built in the control module; according to the model prediction control algorithm, a rectification process dynamic model is established, a current system state and a set control target are combined, an optimal control strategy is obtained through rolling optimization calculation, and a control instruction is sent by an execution mechanism based on the optimal control strategy; an execution mechanism; the touch display screen is connected with the control module; the operation parameters of the rectification system are displayed in real time.

Description

Technical Field

[0001] The present application relates to the technical field of distillation systems, and in particular to an intermittent distillation system based on intelligent control. Background Art

[0002] In the chemical production process, intermittent distillation is a commonly used separation technology, which is widely used in the fields of pharmaceuticals, fine chemicals, petrochemicals, etc. to separate and purify mixtures with different boiling points. Traditional intermittent distillation systems usually rely on manual operation or simple control strategies, which have many limitations.

[0003] The distillation process is a complex dynamic process, which is affected by many factors such as feed composition, flow rate, ambient temperature, etc. The current technology uses fixed reflux ratio, heating power, etc., which cannot be dynamically adjusted according to the actual changes in the distillation process. There is an urgent need for an intermittent distillation system that can monitor the distillation process parameters in real time and perform intelligent control according to actual conditions. Summary of the invention

[0004] In order to at least partially solve the above technical problems, the present application provides an intermittent distillation system based on intelligent control.

[0005] The present application provides an intermittent distillation system based on intelligent control, which adopts the following technical solution.

[0006] An intermittent distillation system based on intelligent control, comprising: Distillation tower; Data acquisition module; the data acquisition module is used to collect temperature, pressure, flow and material concentration data in real time; Control module: The control module receives various data from the data acquisition module; the control module has a built-in model predictive control algorithm; the model predictive control algorithm establishes a dynamic model of the distillation process, combines the current system state and the set control target, obtains the optimal control strategy through rolling optimization calculation, and issues control instructions based on the optimal control strategy execution mechanism; Actuator; the actuator includes a regulating valve, a reflux ratio regulating valve and a variable frequency centrifugal pump; the regulating valve is installed on the heating medium pipeline of the reboiler, and adjusts the heating medium flow rate according to the control instruction issued by the control module to control the heating power of the reboiler; the reflux ratio regulating valve is installed on the reflux pipeline; the variable frequency centrifugal pump is installed on the raw material conveying pipeline and the product output pipeline, and adjusts the material conveying flow rate according to the control instruction of the control module; The touch screen is connected to the control module and displays the operating parameters of the distillation system in real time.

[0007] By adopting the above technical solution, the temperature, pressure, flow rate and material concentration data of the distillation tower are collected in real time through the data acquisition module; after receiving these data, the control module uses the built-in model predictive control algorithm, according to the dynamic model of the distillation process, based on the current system state and the set control objectives, and uses the rolling optimization method to calculate the optimal control strategy, thereby avoiding the lag of traditional control methods.

[0008] Optionally, the data acquisition module includes: Temperature sensor; at the top of the distillation tower, the temperature sensor is installed on the inner wall of the pipe near the steam outlet at the top of the tower; at the bottom of the tower, the temperature sensor is installed near the bottom of the tower and away from the heating element; at the tray position, the temperature sensor is installed in the liquid phase area near the tray downcomer; Pressure sensor; at the top of the tower, the pressure sensor is installed on the inner wall of the gas phase space at the top of the tower near the top of the tower; at the bottom of the tower, the pressure sensor is installed on the inner wall of the gas phase space at the bottom of the tower; Flow sensor; on the raw material delivery pipeline, the flow sensor is installed near the feed port of the distillation tower; on the heating medium pipeline, the flow sensor is installed near the entrance of the reboiler; on the reflux pipeline, the flow sensor is installed at the position where the reflux liquid enters the tower top; Concentration sensor; at the top of the tower, the concentration sensor is installed on the condensate reflux pipe at the top of the tower; in the tower kettle, the concentration sensor is installed on the tower kettle discharge pipe.

[0009] Optionally, the model predictive control algorithm establishes a dynamic model of the distillation process, combines the current system state and sets the control target, obtains the optimal control strategy through rolling optimization calculation, and issues control instructions based on the optimal control strategy execution mechanism, including: The control module receives the temperature, pressure, flow rate and material concentration data transmitted in real time by the data acquisition module; the control module receives the control target input through the touch screen; the control target includes the target value of the tower top product concentration and the target value of the tower bottom residual liquid concentration; The control module predicts the output of the distillation system in the future period of time based on the established dynamic model and the current system status; The control module constructs an optimization problem with the purpose of minimizing the objective function according to the predicted system output and the set control target; by solving the optimization problem, the optimal control strategy at the current moment is obtained to determine the opening change of the regulating valve, the adjustment value of the reflux ratio and the material conveying flow setting value of the variable frequency centrifugal pump; The control module converts the calculated optimal control strategy into control instructions and sends them to the regulating valve, reflux ratio regulating valve and variable frequency centrifugal pump in the actuator respectively; the regulating valve adjusts the flow of heating medium according to the instructions to control the heating power of the reboiler; the reflux ratio regulating valve adjusts the reflux ratio according to the instructions; the variable frequency centrifugal pump adjusts the material conveying flow according to the instructions.

[0010] Optionally, the configuration process of the distillation process dynamic model includes: An initial dynamic model is constructed based on material balance, energy balance and phase balance; the initial dynamic model includes a material balance equation, an energy balance equation and a phase balance equation; wherein the material balance equation is used to establish a differential equation for each plate in the distillation tower according to the material flow and composition entering and leaving the plate to describe the material balance of each component; the energy balance equation is used to establish an energy balance equation for each plate to reflect the temperature change in the tower; the phase balance equation is used to describe the equilibrium relationship between the gas and liquid phases; Configure the tray characteristic parameters and operation parameters; the tray characteristic parameters include tray type, tray number, tray spacing, and tray diameter; the operation parameters include feed flow, feed composition, feed position, reflux ratio, and reboiler heating power; The parameters of the distillation process dynamic model are calibrated and verified using historical data under different operating conditions.

[0011] Optionally, the control module constructs an optimization problem for minimizing the objective function according to the predicted system output and the set control target; by solving the optimization problem, the optimal control strategy at the current moment is obtained to determine the opening change of the regulating valve, the adjustment value of the reflux ratio and the material conveying flow setting value of the variable frequency centrifugal pump, including: The control module constructs an optimization problem for minimizing the objective function based on the predicted system output and the set control target; the objective function is constructed based on the deviation of the tower top product concentration, the deviation of the tower bottom residual liquid concentration, the change of the regulating valve opening, the reflux ratio adjustment value and the deviation of the material delivery flow set value; When solving the optimization problem, the gradient descent method is used as the basic solution algorithm to calculate the gradient of the objective function with respect to the change of the regulating valve opening, the reflux ratio adjustment value and the material conveying flow setting value; Adding boundary constraints of operating variables to the solution process of the optimization problem; the boundary constraints include: the opening of the regulating valve is limited between the minimum opening and the maximum opening, the adjustment value of the reflux ratio is limited between the minimum reflux ratio and the maximum reflux ratio, and the material delivery flow rate setting value is limited between the minimum flow rate and the maximum flow rate; The values ​​of the operating variables are updated according to the gradient information to find the minimum value of the objective function, and the gradient calculation and variable update operations are iterated until the convergence condition is met; the convergence condition is that the change in the objective function value is less than a preset threshold.

[0012] Optionally, when solving the optimization problem, a gradient descent method is used as a basic solution algorithm to calculate the gradient of the objective function with respect to the change in the opening of the regulating valve, the adjustment value of the reflux ratio, and the set value of the material conveying flow rate, including: Initialize the regulating valve opening, reflux ratio adjustment value and material delivery flow setting value to the initial value; Calculating a first gradient of the objective function with respect to a change in the opening of the regulating valve; calculating a second gradient of the objective function with respect to the reflux ratio adjustment value; Calculating a third gradient of the objective function with respect to a set value of the material conveying flow rate; Update the operation variable based on the gradient calculated based on the preset gradient descent step size; Set the convergence condition and perform iterative solution, repeating the above process of gradient calculation, operation variable update and convergence judgment until the convergence condition is met.

[0013] Optionally, the system further comprises an alarm module, which is connected to the control module. When the control module detects that the temperature, pressure, flow or material concentration data exceeds a preset safety threshold, the alarm module sends out an alarm signal.

[0014] Optionally, an anti-clogging device is provided inside the distillation tower, and the anti-clogging device is located between the tower plates to prevent impurities or precipitates in the material from clogging the channels on the tower plates.

[0015] Optionally, the alarm module can send out alarm signals in three ways: sound, light and screen display, and different types of abnormal data trigger different alarm signals.

[0016] Optionally, the temperature sensor is a thermocouple temperature sensor; the pressure sensor is a capacitive pressure sensor; and the flow sensor is an electromagnetic flow sensor.

[0017] The intermittent distillation system based on intelligent control provided by the present invention can collect the temperature, pressure, flow rate and material concentration data of the distillation tower in real time by adopting the model predictive control algorithm, and dynamically optimize according to these data and the set control target, so as to accurately control the various parameters in the distillation process, and avoid the lag and error accumulation in the traditional control method. The system effectively improves the stability and separation efficiency of the distillation process through the optimized control strategy, reduces energy consumption and improves product quality. At the same time, the touch screen displays the operating parameters in real time, so that the operator can easily monitor the system status, timely discover and adjust abnormal conditions, and ensure the safe and reliable operation of the system. In addition, the system also has an alarm function and an anti-blocking device, which improves the operational safety and the long-term stability of the system, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a system block diagram of an intermittent distillation system based on intelligent control according to an embodiment of the present application; Figure 2 is a schematic diagram of a distillation tower; In the figure, 101 is a distillation tower; 102 is a data acquisition module; 103 is a control module; 104 is an actuator; 105 is a touch screen; 106 is a regulating valve; 107 is a reflux ratio regulating valve; 108 is a variable frequency centrifugal pump; 109 is a reboiler; 110 is a reflux pipeline; 111 is a raw material delivery pipeline; 112 is a product output pipeline. DETAILED DESCRIPTION

[0019] The following is combined with Figure 1-2 The present application is further described with specific embodiments: The present application embodiment discloses an intermittent distillation system based on intelligent control, comprising: Distillation tower 101; Data acquisition module 102; the data acquisition module is used to collect temperature, pressure, flow and material concentration data in real time; Control module 103: The control module receives various data from the data acquisition module; the control module has a built-in model predictive control algorithm; the model predictive control algorithm establishes a dynamic model of the distillation process, combines the current system state and the set control target, obtains the optimal control strategy through rolling optimization calculation, and issues control instructions based on the optimal control strategy execution mechanism; Actuator 104; the actuator includes a regulating valve 106, a reflux ratio regulating valve 107 and a variable frequency centrifugal pump 108; the regulating valve is installed on the heating medium pipeline of the reboiler 109, and adjusts the heating medium flow rate to control the heating power of the reboiler 109 according to the control instruction issued by the control module; the reflux ratio regulating valve 107 is installed on the reflux pipeline 110; the variable frequency centrifugal pump 108 is installed on the raw material conveying pipeline 111 and the product output pipeline 112, and adjusts the material conveying flow rate according to the control instruction of the control module 103; The touch screen 105 is connected to the control module 103 and displays the operating parameters of the distillation system in real time.

[0020] Specifically, the regulating valve 106 installed on the heating medium pipeline of the reboiler 109 can adjust the flow rate of the heating medium according to the control instruction, and then control the heating power of the reboiler 109, so that the temperature condition of the distillation process is stable; the variable frequency centrifugal pump adjusts the material delivery flow rate on the raw material delivery pipeline 111 and the product output pipeline 112 according to the control instruction. The touch screen 105 is connected to the control module 103, which can display the operating parameters of the distillation system in real time, so that the operator can intuitively monitor the operating status of the system. The temperature, pressure, flow rate and material concentration data of the distillation tower are collected in real time through the data acquisition module; after receiving these data, the control module uses the built-in model predictive control algorithm, according to the dynamic model of the distillation process, based on the current system state and the set control target, and uses the rolling optimization method to calculate the optimal control strategy, avoiding the lag of the traditional control method.

[0021] The model predictive control algorithm mainly realizes control by establishing a dynamic model of the distillation process. Its steps include predicting the future output of the system, constructing the optimization problem and solving it to obtain the optimal control strategy.

[0022] When formulating an optimization problem, the objective function can take the following form: ; Where J is the objective function value, N p is the prediction time domain, y sp,k is the set value at time k; when solving the target value of the top product concentration, y sp,k Characterizes the concentration of the top product. When solving the target value of the bottom liquid concentration, y sp,k Characterize the concentration of the tower bottom residue, y k is the system output value predicted at time k, w1 and w2 are weight coefficients used to weigh the tracking error and control input changes; is the change in the control input at time k. The change in the control input includes the change in the opening of the regulating valve, the reflux ratio adjustment value, and the material conveying flow set value.

[0023] When solving this optimization problem, the gradient descent method is used to adjust the valve opening For example, the gradient is calculated as follows: ;in, The predicted output y k The partial derivative of the control valve opening.

[0024] As a specific implementation of an intermittent distillation system based on intelligent control, at the top of the distillation tower 101, the temperature sensor is installed on the inner wall of the pipe near the top steam outlet; at the bottom of the tower, the temperature sensor is installed near the bottom of the tower and away from the heating element; at the tower plate position, the temperature sensor is installed in the liquid phase area near the tower plate downcomer; At the top of the tower, the pressure sensor is installed on the inner wall of the gas phase space at the top of the tower near the top of the tower; at the bottom of the tower, the pressure sensor is installed on the inner wall of the gas phase space at the bottom of the tower; On the raw material delivery pipeline 111, the flow sensor is installed near the feed port of the distillation tower; on the heating medium pipeline of the reboiler 109, the flow sensor is installed near the entrance of the reboiler 109; on the reflux pipeline 110, the flow sensor is installed at the position where the reflux liquid enters the tower top; At the top of the tower, the concentration sensor is installed on the condensate reflux pipe at the top of the tower; in the tower kettle, the concentration sensor is installed on the tower kettle discharge pipe.

[0025] As a specific implementation of an intermittent distillation system based on intelligent control, the configuration process of the distillation process dynamic model includes: An initial dynamic model is constructed based on material balance, energy balance and phase balance; the initial dynamic model includes a material balance equation, an energy balance equation and a phase balance equation; wherein the material balance equation is used to establish a differential equation for each plate in the distillation tower according to the material flow and composition entering and leaving the plate to describe the material balance of each component; the energy balance equation is used to establish an energy balance equation for each plate to reflect the temperature change in the tower; the phase balance equation is used to describe the equilibrium relationship between the gas and liquid phases; Configure the tray characteristic parameters and operation parameters; the tray characteristic parameters include tray type, tray number, tray spacing, and tray diameter; the operation parameters include feed flow, feed composition, feed position, reflux ratio, and reboiler heating power; The parameters of the distillation process dynamic model are calibrated and verified using historical data under different operating conditions.

[0026] Specifically, the material balance equation is: For each plate i in the distillation tower, let is the liquid phase flow rate flowing down from the previous tray, is the gas phase flow rate rising to the next tower plate, Fzf is the feed flow rate, , are the mole fractions of a component in the liquid phase of the previous plate and the current plate, respectively. , are the mole fractions of a component in the gas phase on the current tray and the next tray, respectively, and the feed position is j; the material balance equation is: in, is the liquid holdup on plate i.

[0027] set up is the liquid phase flow rate flowing down from the previous tray, is the liquid phase flow rate under the current plate, , is the enthalpy of the liquid phase on the previous plate and the current plate, , is the enthalpy of the gas phase on the current plate and the next plate, is the gas phase flow rate rising from plate i, Qi is the heat load on plate i, and the energy balance equation is: .

[0028] Phase equilibrium equation: ;in, is the relative volatility, n is the number of components; represents the mole fraction of the jth component in the liquid phase; Represents the relative volatility of the jth component.

[0029] Specifically, the initial dynamic model is constructed based on material balance, energy balance and phase balance. The material balance equation is used to quantitatively describe the material flow and composition of each plate in the distillation tower. By establishing differential equations to describe the material balance of each component, the dynamic change process of the material during the distillation process can be clearly presented. The energy balance equation is established for each plate to reflect the temperature change in the tower. The phase balance equation describes the equilibrium relationship between the gas and liquid phases and is used to control the mutual transformation and equilibrium state of the gas and liquid phases during the process. The plate characteristic parameters and operating parameters are configured, where the plate characteristic parameters include the plate type, number of plates, plate spacing and tower diameter, etc. These parameters can reflect the physical structural characteristics of the tower, so that the model can be more in line with the actual distillation tower structure. The operating parameters include feed flow, feed composition, feed position, reflux ratio and reboiler heating power, etc., which reflect the operating factors in the distillation process. The calibration and verification of the parameters of the dynamic model of the distillation process using historical data under different operating conditions ensures the accuracy and reliability of the model, which is conducive to the automatic control of the distillation process.

[0030] As a specific implementation of an intermittent distillation system based on intelligent control, the control module constructs an optimization problem for the purpose of minimizing the objective function according to the predicted system output and the set control target; by solving the optimization problem, the optimal control strategy at the current moment is obtained to determine the opening change of the regulating valve, the adjustment value of the reflux ratio and the material delivery flow setting value of the variable frequency centrifugal pump, including: The control module constructs an optimization problem for minimizing the objective function based on the predicted system output and the set control target; the objective function is constructed based on the deviation of the tower top product concentration, the deviation of the tower bottom residual liquid concentration, the change of the regulating valve opening, the reflux ratio adjustment value and the deviation of the material delivery flow set value; When solving the optimization problem, the gradient descent method is used as the basic solution algorithm to calculate the gradient of the objective function with respect to the change of the regulating valve opening, the reflux ratio adjustment value and the material conveying flow setting value; Adding boundary constraints of operating variables to the solution process of the optimization problem; the boundary constraints include: the opening of the regulating valve is limited between the minimum opening and the maximum opening, the adjustment value of the reflux ratio is limited between the minimum reflux ratio and the maximum reflux ratio, and the material delivery flow rate setting value is limited between the minimum flow rate and the maximum flow rate; The values ​​of the operating variables are updated according to the gradient information to find the minimum value of the objective function, and the gradient calculation and variable update operations are iterated until the convergence condition is met; the convergence condition is that the change in the objective function value is less than a preset threshold.

[0031] Specifically, the control module constructs an optimization problem for the purpose of minimizing the objective function according to the predicted system output and the set control target, making the control process more targeted. Multiple key factors such as the deviation of the top product concentration, the deviation of the bottom liquid concentration, the change of the regulating valve opening, the adjustment value of the reflux ratio and the deviation of the material delivery flow set value are incorporated into the construction of the objective function, and multiple important performance indicators of the distillation system are comprehensively considered. When solving the optimization problem, the gradient descent method is used as the basic solution algorithm, and the gradient of the objective function with respect to the change of the regulating valve opening, the adjustment value of the reflux ratio and the material delivery flow set value is calculated. The gradient descent method enables the system to update the value of the operating variable along the direction where the objective function drops the fastest, thereby speeding up the optimization solution. The boundary constraints of the operating variables are added to the solution process of the optimization problem, and the regulating valve opening is limited between the minimum opening and the maximum opening, the adjustment value of the reflux ratio is limited between the minimum reflux ratio and the maximum reflux ratio, and the material delivery flow set value is limited between the minimum flow and the maximum flow, ensuring that the operating variables are within a reasonable range, avoiding problems such as equipment damage and system instability caused by over-range operation in actual operation. Such an optimized control process can continuously adjust the control valve opening, reflux ratio and material delivery flow set values ​​according to the system's predicted output and control objectives, so that the system can achieve the optimal operating state while meeting various constraints, thereby improving the product quality of the distillation system, stabilizing the distillation process, and reducing product quality fluctuations.

[0032] As one of the implementation methods of an intermittent distillation system based on intelligent control, when solving the optimization problem, the gradient descent method is used as the basic solution algorithm to calculate the gradient of the objective function with respect to the change of the regulating valve opening, the reflux ratio adjustment value and the material delivery flow setting value, including: Initialize the regulating valve opening, reflux ratio adjustment value and material delivery flow setting value to the initial value; Calculating a first gradient of the objective function with respect to a change in the opening of the regulating valve; calculating a second gradient of the objective function with respect to the reflux ratio adjustment value; Calculating a third gradient of the objective function with respect to a set value of the material conveying flow rate; Update the operation variable based on the gradient calculated based on the preset gradient descent step size; Set the convergence condition and perform iterative solution, repeating the above process of gradient calculation, operation variable update and convergence judgment until the convergence condition is met.

[0033] Specifically, the regulating valve opening, the reflux ratio adjustment value and the material delivery flow setting value are initialized to the initial value, and then the first gradient of the objective function with respect to the change of the regulating valve opening, the second gradient of the reflux ratio adjustment value and the third gradient of the material delivery flow setting value are calculated. Through the calculation of these gradients, the system can clearly understand the degree and direction of influence of each operating variable on the objective function, which enables the operating variable to be accurately adjusted in the direction of reducing the objective function value during the optimization process; based on the pre-set step size of the gradient descent, the operating variable is updated using the calculated gradient, which will neither cause the optimization process to miss the optimal solution due to too large a step size, nor cause the optimization process to be too slow due to too small a step size, thus achieving high efficiency and stability of the optimization process. Through gradient calculation and step size setting, the accuracy and efficiency of the solution are improved, blind search is avoided, computing resources are effectively saved, the control accuracy of the distillation system is improved, and the operating state of the system is stabilized.

[0034] As one embodiment of an intermittent distillation system based on intelligent control, the system also includes an alarm module, which is connected to the control module. When the control module detects that the temperature, pressure, flow or material concentration data exceeds a preset safety threshold, the alarm module sends an alarm signal.

[0035] The alarm module in the present invention also includes a priority alarm response mechanism, which is used to classify and prioritize alarm signals according to the severity of different faults and the degree of impact on the production process. When parameters such as temperature, pressure, flow or material concentration in the system exceed the preset safety threshold, the alarm module automatically determines and sets different alarm levels based on the nature of each fault event and its potential impact on system safety, equipment stability and production efficiency. Specifically, when the monitored parameter value exceeds the set threshold, the alarm module will automatically classify and trigger different levels of response according to the degree of impact of the fault on the distillation process.

[0036] For example, when the system detects abnormal temperature or pressure, if the abnormal value poses a serious threat to the safety of the equipment (such as high temperature and high pressure may cause equipment rupture or leakage), the alarm module will judge it as a high-priority alarm according to the severity of the fault, and respond promptly through measures such as sound and light alarm, automatic closure of related valves, or initiation of emergency shutdown procedures to ensure the safety of the system. At this time, the alarm module will also synchronously send an emergency alarm signal to the operator and display detailed fault information for timely processing.

[0037] If the system detects that the flow rate or material concentration exceeds the set threshold, but the fault does not involve direct equipment safety hazards and may only affect product quality or production efficiency, the alarm module will judge it as a medium priority alarm based on the preset response strategy. At this time, the alarm module will issue an alarm signal to remind the operator to pay attention to the current operating status, display detailed abnormal parameters, and recommend measures such as adjusting the flow rate or concentration to optimize the production process.

[0038] For some minor parameter fluctuations, such as temperature, pressure and other values ​​that slightly deviate from the safety threshold, but have little impact on the safety of the system and the production process, the alarm module will classify them as low priority alarms and remind operators to pay attention to relevant parameters in a concise way. Such alarm signals will not immediately take emergency measures such as shutdown, but will be recorded in the system log for operators to check and adjust later.

[0039] During the alarm response process, the system will automatically adjust the processing priority according to the alarm level. High-priority alarm signals will be displayed first and trigger the emergency processing mechanism, while low-priority alarms can remind operators to perform follow-up processing in a timely manner without interfering with other emergency operations. This priority-based alarm response mechanism can not only effectively prevent operators from making erroneous operations due to excessive alarm information, but also ensure that appropriate countermeasures are taken in a timely and accurate manner when a fault occurs.

[0040] As one of the embodiments of an intermittent distillation system based on intelligent control, an anti-clogging device is provided inside the distillation tower. The anti-clogging device is located between the tower plates to prevent impurities or precipitates in the material from clogging the channels on the tower plates.

[0041] As one of the implementation methods of an intermittent distillation system based on intelligent control, the alarm module can send out alarm signals in three ways: sound, light and screen display. Different types of abnormal data trigger different alarm signals.

[0042] As one implementation of an intermittent distillation system based on intelligent control, the temperature sensor is a thermocouple temperature sensor; the pressure sensor is a capacitive pressure sensor; and the flow sensor is an electromagnetic flow sensor.

[0043] It should be noted that the above embodiments are only used to illustrate the present application and are not intended to limit the technical solutions described in the present application. Although the present application has been described in detail in this specification with reference to the above embodiments, a person of ordinary skill in the art should understand that a person of ordinary skill in the art can still modify or make equivalent substitutions to the present application, and all technical solutions and improvements thereof that do not depart from the spirit and scope of the present application should be included in the scope of the claims of the present application.

Claims

1. An intermittent distillation system based on intelligent control, characterized in that: include: Distillation tower; Data acquisition module; The data acquisition module is used to collect temperature, pressure, flow rate and material concentration data in real time; Control module: The control module receives various data from the data acquisition module; the control module has a built-in model predictive control algorithm; the model predictive control algorithm establishes a dynamic model of the distillation process, combines the current system state and the set control target, obtains the optimal control strategy through rolling optimization calculation, and issues control instructions based on the optimal control strategy execution mechanism; Actuator; the actuator includes a regulating valve, a reflux ratio regulating valve and a variable frequency centrifugal pump; the regulating valve is installed on the heating medium pipeline of the reboiler, and adjusts the heating medium flow rate according to the control instruction issued by the control module to control the heating power of the reboiler; the reflux ratio regulating valve is installed on the reflux pipeline; the variable frequency centrifugal pump is installed on the raw material conveying pipeline and the product output pipeline, and adjusts the material conveying flow rate according to the control instruction of the control module; The touch screen is connected to the control module and displays the operating parameters of the distillation system in real time.

2. The intermittent distillation system based on intelligent control according to claim 1, characterized in that: The data acquisition module comprises: Temperature sensor: At the top of the distillation tower, the temperature sensor is installed on the inner wall of the pipe near the steam outlet at the top of the tower; in the tower kettle, the temperature sensor is installed near the bottom of the tower kettle and away from the heating element; at the tower plate position, the temperature sensor is installed in the liquid phase area near the tower plate downcomer; Pressure sensor: At the top of the tower, the pressure sensor is installed on the inner wall of the gas phase space at the top of the tower near the top of the tower; at the bottom of the tower, the pressure sensor is installed on the inner wall of the gas phase space at the bottom of the tower; Flow sensor: On the raw material delivery pipeline, the flow sensor is installed near the feed port of the distillation tower; on the heating medium pipeline, the flow sensor is installed near the inlet of the reboiler; on the reflux pipeline, the flow sensor is installed at the position where the reflux liquid enters the tower top; Concentration sensor: At the top of the tower, the concentration sensor is installed on the condensate reflux pipe at the top of the tower; at the bottom of the tower, the concentration sensor is installed on the bottom of the tower discharge pipe.

3. The intermittent distillation system based on intelligent control according to claim 2, characterized in that: The model predictive control algorithm establishes a dynamic model of the distillation process, combines the current system state and the set control target, obtains the optimal control strategy through rolling optimization calculation, and issues control instructions based on the optimal control strategy execution mechanism, including: The control module receives the temperature, pressure, flow rate and material concentration data transmitted in real time by the data acquisition module; the control module receives the control target input through the touch screen; the control target includes the target value of the tower top product concentration and the target value of the tower bottom residual liquid concentration; The control module predicts the output of the distillation system in the future period of time based on the established dynamic model and the current system status; The control module constructs an optimization problem with the purpose of minimizing the objective function according to the predicted system output and the set control target; by solving the optimization problem, the optimal control strategy at the current moment is obtained to determine the opening change of the regulating valve, the adjustment value of the reflux ratio and the material conveying flow setting value of the variable frequency centrifugal pump; The control module converts the calculated optimal control strategy into control instructions and sends them to the regulating valve, reflux ratio regulating valve and variable frequency centrifugal pump in the actuator respectively; the regulating valve adjusts the flow of heating medium according to the instructions to control the heating power of the reboiler; the reflux ratio regulating valve adjusts the reflux ratio according to the instructions; the variable frequency centrifugal pump adjusts the material conveying flow according to the instructions.

4. The intermittent distillation system based on intelligent control according to claim 3, characterized in that: The configuration process of the distillation process dynamic model includes: An initial dynamic model is constructed based on material balance, energy balance and phase balance; the initial dynamic model includes a material balance equation, an energy balance equation and a phase balance equation; wherein the material balance equation is used to establish a differential equation for each plate in the distillation tower according to the material flow and composition entering and leaving the plate to describe the material balance of each component; the energy balance equation is used to establish an energy balance equation for each plate to reflect the temperature change in the tower; the phase balance equation is used to describe the equilibrium relationship between the gas and liquid phases; Configure the tray characteristic parameters and operation parameters; the tray characteristic parameters include tray type, tray number, tray spacing, and tray diameter; the operation parameters include feed flow, feed composition, feed position, reflux ratio, and reboiler heating power; The parameters of the distillation process dynamic model are calibrated and verified using historical data under different operating conditions.

5. The intermittent distillation system based on intelligent control according to claim 4, characterized in that: The control module constructs an optimization problem with the purpose of minimizing the objective function based on the predicted system output and the set control target; By solving the optimization problem, the optimal control strategy at the current moment is obtained to determine the opening change of the regulating valve, the adjustment value of the reflux ratio and the material conveying flow setting value of the variable frequency centrifugal pump, including: The control module constructs an optimization problem for minimizing the objective function based on the predicted system output and the set control target; the objective function is constructed based on the deviation of the tower top product concentration, the deviation of the tower bottom residual liquid concentration, the change of the regulating valve opening, the reflux ratio adjustment value and the deviation of the material delivery flow set value; When solving the optimization problem, the gradient descent method is used as the basic solution algorithm to calculate the gradient of the objective function with respect to the change of the regulating valve opening, the reflux ratio adjustment value and the material conveying flow setting value; Adding boundary constraints of operating variables to the solution process of the optimization problem; the boundary constraints include: the opening of the regulating valve is limited between the minimum opening and the maximum opening, the adjustment value of the reflux ratio is limited between the minimum reflux ratio and the maximum reflux ratio, and the material delivery flow rate setting value is limited between the minimum flow rate and the maximum flow rate; The values ​​of the operating variables are updated according to the gradient information to find the minimum value of the objective function, and the gradient calculation and variable update operations are iterated until the convergence condition is met; the convergence condition is that the change in the objective function value is less than a preset threshold.

6. The intermittent distillation system based on intelligent control according to claim 5, characterized in that: When solving the optimization problem, the gradient descent method is used as the basic solution algorithm to calculate the gradient of the objective function with respect to the change of the regulating valve opening, the reflux ratio adjustment value and the material conveying flow setting value, including: Initialize the regulating valve opening, reflux ratio adjustment value and material delivery flow setting value to the initial value; Calculating a first gradient of the objective function with respect to a change in the opening of the regulating valve; calculating a second gradient of the objective function with respect to the reflux ratio adjustment value; Calculating a third gradient of the objective function with respect to a set value of the material conveying flow rate; Update the operation variable based on the gradient calculated based on the preset gradient descent step size; Set the convergence condition and perform iterative solution, repeating the above process of gradient calculation, operation variable update and convergence judgment until the convergence condition is met.

7. The intermittent distillation system based on intelligent control according to claim 6, characterized in that: The system also includes an alarm module, which is connected to the control module. When the control module detects that the temperature, pressure, flow rate or material concentration data exceeds a preset safety threshold, the alarm module sends an alarm signal.

8. The intermittent distillation system based on intelligent control according to claim 7, characterized in that: An anti-clogging device is provided inside the distillation tower. The anti-clogging device is located between the tower plates and is used to prevent impurities or sediments in the material from clogging the channels on the tower plates.

9. The intermittent distillation system based on intelligent control according to claim 8, characterized in that: The alarm module can send out alarm signals in three ways: sound, light and screen display. Different types of abnormal data trigger different alarm signals.

10. The intermittent distillation system based on intelligent control according to claim 9, characterized in that: The temperature sensor is a thermocouple temperature sensor; the pressure sensor is a capacitive pressure sensor; and the flow sensor is an electromagnetic flow sensor.

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