An intermittent distillation system based on intelligent control

Abstract

This application relates to a batch distillation system based on intelligent control, belonging to the technical field of batch distillation. It includes: a distillation column; a data acquisition module, which is used to collect temperature, pressure, flow rate and material concentration data in real time; a control module, which receives various data from the data acquisition module, and the control module is built-in with a model predictive control algorithm. The model predictive control algorithm obtains the optimal control strategy through rolling optimization calculation by establishing a dynamic model of the distillation process, combining the current system state and the set control target, and issues a control instruction to the actuator based on the optimal control strategy; an actuator; a touch screen display, which is connected to the control module and displays the operating parameters of the distillation system in real time.

Description

Technical Field

[0001] This application relates to the technical field of distillation systems, and particularly to a batch distillation system based on intelligent control. Background Art

[0002] In the process of chemical production, batch 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 batch distillation systems usually rely on manual operation or simple control strategies, and there are many limitations in this way.

[0003] The distillation process is a complex dynamic process, which is affected by various factors such as feed composition, flow rate, and ambient temperature. In current technologies, fixed reflux ratios, heating powers, etc. are adopted and cannot be dynamically adjusted according to the actual changes in the distillation process. There is an urgent need for a batch distillation system that can monitor the parameters of the distillation process in real time and perform intelligent control according to the actual situation. Summary of the Invention

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

[0005] The batch distillation system based on intelligent control provided by this application adopts the following technical solutions.

[0006] A batch distillation system based on intelligent control, comprising:

[0007] A distillation column;

[0008] A data acquisition module; the data acquisition module is used to collect temperature, pressure, flow rate, and material concentration data in real time;

[0009] A control module: the control module receives various data from the data acquisition module; the control module has a model predictive control algorithm built in; the model predictive control algorithm obtains the optimal control strategy through rolling optimization calculation by establishing a dynamic model of the distillation process, combining the current system state and the set control target, and issues a control instruction to the actuator based on the optimal control strategy;

[0010] An actuator; the actuator includes a control valve, a reflux ratio control valve, and a variable frequency centrifugal pump; the control 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 control valve is installed on the reflux pipeline; the variable frequency centrifugal pump is installed on the raw material delivery pipeline and the product output pipeline and adjusts the material delivery flow rate according to the control instruction of the control module;

[0011] A touch screen display, connected to the control module; and it displays the operating parameters of the distillation system in real time.

[0012] By adopting the above technical solution, the data acquisition module collects the temperature, pressure, flow rate, and material concentration data of the distillation column in real time; after the control module receives these data, based on 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, the optimal control strategy is calculated by means of rolling optimization, avoiding the lag of the traditional control method.

[0013] Optionally, the data acquisition module includes:

[0014] Temperature sensors; at the top of the distillation column, the temperature sensors are installed on the inner wall of the pipeline near the steam outlet at the top; at the bottom of the column, the temperature sensors are installed at a position close to the bottom of the column and away from the heating element; at the tray position, the temperature sensors are installed in the liquid phase area near the downcomer of the tray;

[0015] Pressure sensors; at the top of the column, the pressure sensors are installed on the inner wall near the top of the gas phase space at the top; at the bottom of the column, the pressure sensors are installed on the inner wall of the gas phase space at the bottom of the column;

[0016] Flow sensors; on the raw material conveying pipeline, the flow sensors are installed at a position close to the feed inlet of the distillation column; on the heating medium pipeline, the flow sensors are installed at the inlet near the reboiler; on the reflux pipeline, the flow sensors are installed at the position where the reflux liquid enters the top of the column;

[0017] Concentration sensors; at the top of the column, the concentration sensors are installed on the reflux pipeline of the top condenser liquid; at the bottom of the column, the concentration sensors are installed on the discharge pipeline of the bottom of the column.

[0018] Optionally, the model predictive control algorithm obtains the optimal control strategy through rolling optimization by establishing a dynamic model of the distillation process, combining the current system state and the set control target, and issues a control instruction to the actuator based on the optimal control strategy, including:

[0019] 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 display; the control target includes the target value of the top product concentration and the target value of the bottom residue concentration;

[0020] The control module predicts the output of the distillation system in the next period of time according to the established dynamic model combined with the current system state;

[0021] The control module constructs an optimization problem aiming at 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 set value of the material conveying flow rate of the variable frequency centrifugal pump;

[0022] The control module converts the calculated optimal control strategy into control instructions and sends them to the control valve, reflux ratio control valve, and variable-frequency centrifugal pump in the actuator respectively; the control valve adjusts the heating medium flow rate according to the instruction to control the heating power of the reboiler; the reflux ratio control valve adjusts the reflux ratio according to the instruction; the variable-frequency centrifugal pump adjusts the material conveying flow rate according to the instruction.

[0023] Optionally, the configuration process of the dynamic model of the rectification process includes:

[0024] Construct an initial dynamic model based on material balance, energy balance, and phase equilibrium; the initial dynamic model includes a material balance equation, an energy balance equation, and a phase equilibrium equation; wherein, the material balance equation is used to establish a differential equation for each tray in the rectification column to describe the material balance of each component according to the material flow rate and composition entering and leaving the tray; the energy balance equation is used to establish an energy balance equation for each tray to reflect the temperature change in the column; the phase equilibrium equation is used to describe the equilibrium relationship between the gas and liquid phases;

[0025] Configure tray characteristic parameters and operating parameters; the tray characteristic parameters include tray type, number of trays, tray spacing, and column diameter; the operating parameters include feed flow rate, feed composition, feed location, reflux ratio, and reboiler heating power;

[0026] Calibrate and verify the parameters of the dynamic model of the rectification process using historical data under different operating conditions.

[0027] Optionally, the control module constructs an optimization problem aiming at 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 control valve, the adjustment value of the reflux ratio, and the set value of the material conveying flow rate of the variable-frequency centrifugal pump, including:

[0028] The control module constructs an optimization problem aiming at minimizing the objective function based on the predicted system output and the set control target; wherein the construction of the objective function is based on the deviation of the top product concentration, the deviation of the bottom residue concentration, the opening change of the control valve, the adjustment value of the reflux ratio, and the deviation of the set value of the material conveying flow rate;

[0029] When solving the optimization problem, the gradient descent method is used as the basic solution algorithm to calculate the gradients of the objective function with respect to the opening change of the control valve, the adjustment value of the reflux ratio, and the set value of the material conveying flow rate;

[0030] Add boundary constraints on the operating variables to the solution process of the optimization problem; the boundary constraints include: the opening degree of the regulating valve is limited between the minimum opening degree and the maximum opening degree, the adjustment value of the reflux ratio is limited between the minimum reflux ratio and the maximum reflux ratio, and the set value of the material conveying flow rate is limited between the minimum flow rate and the maximum flow rate;

[0031] Update the values of the operating variables according to the gradient information to find the minimum value of the objective function, and iteratively perform gradient calculation and variable update operations until the convergence condition is satisfied; the convergence condition is that the change in the objective function value is less than the preset threshold.

[0032] Optionally, when solving the optimization problem, the gradient descent method is used as the basic solution algorithm to calculate the gradients of the objective function with respect to the change in the opening degree of the regulating valve, the adjustment value of the reflux ratio, and the set value of the material conveying flow rate, including:

[0033] Initialize the opening degree of the regulating valve, the adjustment value of the reflux ratio, and the set value of the material conveying flow rate to the initial values;

[0034] Calculate the first gradient of the objective function with respect to the change in the opening degree of the regulating valve;

[0035] Calculate the second gradient of the objective function with respect to the adjustment value of the reflux ratio;

[0036] Calculate the third gradient of the objective function with respect to the set value of the material conveying flow rate;

[0037] Update the operating variables based on the gradients calculated according to the preset step size of the gradient descent;

[0038] Set the convergence condition and perform iterative solution, repeating the above process of gradient calculation, operating variable update, and convergence judgment until the convergence condition is satisfied.

[0039] Optionally, the system further includes an alarm module, and the alarm module is connected to the control module. When the control module detects that the temperature, pressure, flow rate, or material concentration data exceeds the preset safety threshold, the alarm module emits an alarm signal.

[0040] Optionally, an anti-blocking device is provided inside the distillation column, and the anti-blocking device is located between the trays to prevent impurities or precipitates in the material from blocking the channels on the trays.

[0041] Optionally, the alarm module can emit alarm signals in three ways: sound, light, and screen display, and different types of abnormal data trigger different alarm signals.

[0042] Optionally, the temperature sensor is a thermocouple type temperature sensor; the pressure sensor adopts a capacitive pressure sensor; the flow sensor is an electromagnetic flow sensor.

[0043] The batch 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 column in real time by adopting the model predictive control algorithm, and perform dynamic optimization according to these data and the set control objectives, so as to accurately control the various parameters in the distillation process, avoiding the hysteresis 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 the energy consumption and improves the product quality. At the same time, the touch screen displays the operating parameters in real time, enabling the operator to conveniently monitor the system status, promptly discover and adjust abnormal situations, and ensure the safe and reliable operation of the system. In addition, the system also has an alarm function and an anti-blocking device, improving the operation safety and the long-term stability of the system, and having a wide application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is the system block diagram of a batch distillation system based on intelligent control according to an embodiment of the present application;

[0045] Figure 2 is the schematic diagram of the distillation column;

[0046] In the figure, 101 - distillation column; 102 - data acquisition module; 103 - control module; 104 - actuator; 105 - touch screen; 106 - regulating valve; 107 - reflux ratio regulating valve; 108 - variable frequency centrifugal pump; 109 - reboiler; 110 - reflux pipeline; 111 - raw material conveying pipeline; 112 - product output pipeline. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The following further describes the present application in conjunction with the Figure 1-2 accompanying drawings and specific embodiments:

[0048] An embodiment of the present application discloses a batch distillation system based on intelligent control, including:

[0049] a distillation column 101;

[0050] a data acquisition module 102; the data acquisition module is used to collect temperature, pressure, flow rate and material concentration data in real time;

[0051] a control module 103: the control module receives various data from the data acquisition module; the control module is built-in with a model predictive control algorithm; the model predictive control algorithm obtains the optimal control strategy through establishing a dynamic model of the distillation process, combining the current system state and the set control objectives, and performing rolling optimization calculation, and issues a control instruction to the actuator based on the optimal control strategy;

[0052] 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 according to the control instruction issued by the control module to control the heating power of the reboiler 109; 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;

[0053] A touch screen display 105, connected to the control module 103; it displays the operating parameters of the distillation system in real time.

[0054] Specifically, the regulating valve 106 installed on the heating medium pipeline of the reboiler 109 can adjust the heating medium flow rate according to the control instruction, and then control the heating power of the reboiler 109, so that the temperature condition in the distillation process is stable; the variable frequency centrifugal pump adjusts the material conveying flow rate on the raw material conveying pipeline 111 and the product output pipeline 112 according to the control instruction. The touch screen display 105 is connected to the control module 103 and can display the operating parameters of the distillation system in real time, which is convenient for the operator to intuitively monitor the system operating state. The data acquisition module collects the temperature, pressure, flow rate, and material concentration data of the distillation column in real time; after receiving these data, the control module, based on the built-in model predictive control algorithm, according to the distillation process dynamic model, based on the current system state and the set control target, uses the rolling optimization method to calculate the optimal control strategy, avoiding the lag of the traditional control method.

[0055] The model predictive control algorithm mainly realizes control by establishing a distillation process dynamic model, and its steps include predicting the future output of the system, constructing an optimization problem and solving it to obtain the optimal control strategy.

[0056] When constructing the optimization problem, the objective function can adopt the following form:

[0057] ; where J is the objective function value, N p is the prediction horizon, y sp,k is the set value at time k; when solving the target value of the top product concentration, y sp,k represents the top product concentration, when solving the target value of the bottom residue concentration, y sp,k represents the bottom residue concentration, y k is the predicted system output value at time k, w1 and w2 are weight coefficients, used to balance the tracking error and the change of control input; is the change amount of the control input at time k, and the change amount of the control input includes the change of the regulating valve opening, the reflux ratio adjustment value, and the material conveying flow rate setting value.

[0058] When solving this optimization problem, the gradient descent method is adopted. Taking the change in the opening of the control valve as an example, its gradient calculation is as follows:

[0059] ; where is the partial derivative of the predicted output y k with respect to the change in the opening of the control valve.

[0060] As a specific implementation manner of an intermittent distillation system based on intelligent control, at the top of the distillation column 101, a temperature sensor is installed on the inner wall of the pipeline near the steam outlet at the top; at the bottom of the column, the temperature sensor is installed at a position close to the bottom of the column kettle and far from the heating element; at the tray position, the temperature sensor is installed in the liquid phase region near the downcomer of the tray;

[0061] At the top, a pressure sensor is installed on the inner wall near the top of the gas phase space at the top; at the bottom of the column, the pressure sensor is installed on the inner wall of the gas phase space at the bottom of the column kettle;

[0062] On the raw material delivery pipeline 111, a flow sensor is installed at a position close to the feed inlet of the distillation column; on the heating medium pipeline of the reboiler 109, the flow sensor is installed near the inlet of the reboiler 109; on the reflux pipeline 110, the flow sensor is installed at the position where the reflux liquid enters the top of the column;

[0063] At the top, a concentration sensor is installed on the condensate reflux pipeline at the top; at the bottom of the column, the concentration sensor is installed on the discharge pipeline at the bottom of the column kettle.

[0064] As a specific implementation manner of an intermittent distillation system based on intelligent control, the configuration process of the dynamic model of the distillation process includes:

[0065] Construct an initial dynamic model based on material balance, energy balance, and phase equilibrium; the initial dynamic model includes a material balance equation, an energy balance equation, and a phase equilibrium equation; where, the material balance equation is used for each tray in the distillation column, and according to the material flow rate and composition entering and leaving the tray, a differential equation is established to describe the material balance of each component; the energy balance equation is used to establish an energy balance equation for each tray to reflect the temperature change in the column; the phase equilibrium equation is used to describe the equilibrium relationship between the gas and liquid phases;

[0066] Configure tray characteristic parameters and operating parameters; the tray characteristic parameters include tray type, number of trays, tray spacing, tower diameter; the operating parameters include feed flow rate, feed composition, feed position, reflux ratio, reboiler heating power;

[0067] Calibrate and verify the parameters of the dynamic model of the distillation process using historical data under different operating conditions.

[0068] Specifically, the material balance equation: For each tray i in the distillation column, let be the liquid flow rate flowing down from the previous tray, be the vapor flow rate rising from the next tray, Fzf be the feed flow rate, , be the mole fractions of a certain component in the liquid phase on the previous tray and the current tray respectively, , be the mole fractions of a certain component in the vapor phase on the current tray and the next tray respectively, and the feed position is j; the material balance equation is:

[0069]

[0070] where is the holdup on tray i.

[0071] Let be the liquid flow rate flowing down from the previous tray, be the liquid flow rate flowing down from the current tray, , be the enthalpies of the liquid phase on the previous tray and the current tray respectively, , be the enthalpies of the vapor phase on the current tray and the next tray respectively, be the vapor flow rate rising from tray i, Qi be the heat load on tray i, and the energy balance equation is:

[0072] .

[0073] Phase equilibrium equation: ; where is the relative volatility and n is the number of components; represents the mole fraction of the j-th component in the liquid phase; represents the relative volatility of the j-th component.

[0074] Specifically, an 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 rate and composition of each tray in the distillation column. 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 tray to reflect the temperature change inside the column. The phase balance equation describes the equilibrium relationship between the gas and liquid phases and is used to control the mutual conversion and equilibrium state of the gas and liquid phases during the process. Tray characteristic parameters and operating parameters are configured. The tray characteristic parameters include tray type, number of trays, tray spacing, and column diameter, etc. These parameters can reflect the physical structure characteristics of the column, enabling the model to better fit the actual distillation column structure. The operating parameters include feed flow rate, feed composition, feed location, reflux ratio, and reboiler heating power, etc. They reflect the operating factors during the distillation process. Calibrating and validating the dynamic model parameters of the distillation process using historical data under different operating conditions ensures the accuracy and reliability of the model and helps with the automatic control of the distillation process.

[0075] As a specific implementation of an intermittent distillation system based on intelligent control, the control module constructs an optimization problem aimed at 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 control valve, the adjustment value of the reflux ratio, and the set value of the material delivery flow rate of the variable-frequency centrifugal pump, including:

[0076] The control module constructs an optimization problem aimed at minimizing the objective function based on the predicted system output and the set control target; where the construction of the objective function is based on the deviation of the top product concentration, the deviation of the bottom residue concentration, the opening change of the control valve, the adjustment value of the reflux ratio, and the deviation of the set value of the material delivery flow rate;

[0077] When solving the optimization problem, the gradient descent method is used as the basic solution algorithm to calculate the gradients of the objective function with respect to the opening change of the control valve, the adjustment value of the reflux ratio, and the set value of the material delivery flow rate;

[0078] Boundary constraints on the operating variables are added to the solution process of the optimization problem; the boundary constraints include: the opening of the control 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 set value of the material delivery flow rate is limited between the minimum flow rate and the maximum flow rate;

[0079] Update the values of the operating variables according to the gradient information to find the minimum value of the objective function, and iteratively perform gradient calculation and variable update operations until the convergence condition is satisfied; the convergence condition is that the change in the objective function value is less than the preset threshold.

[0080] Specifically, the control module constructs an optimization problem aimed at minimizing the objective function based on the predicted system output and the set control target, making the control process more targeted. Multiple key factors such as the concentration deviation of the top product, the concentration deviation of the bottom residue, the change in the opening of the regulating valve, the adjustment value of the reflux ratio, and the deviation of the set value of the material conveying flow rate are incorporated into the construction of the objective function, comprehensively considering multiple important performance indicators of the distillation system. When solving the optimization problem, the gradient descent method is used as the basic solution algorithm, and the gradients 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 are calculated. The gradient descent method enables the system to update the values of the manipulated variables along the direction where the objective function decreases fastest, accelerating the speed of the optimization solution. Boundary constraints on the manipulated variables are added to the solution process of the optimization problem, limiting the opening of the regulating valve between the minimum opening and the maximum opening, the adjustment value of the reflux ratio between the minimum reflux ratio and the maximum reflux ratio, and the set value of the material conveying flow rate between the minimum flow rate and the maximum flow rate, ensuring that the manipulated variables are within a reasonable range and avoiding problems such as equipment damage and system instability caused by out-of-range operations in actual operation. Such an optimized control process can continuously adjust the opening of the regulating valve, the reflux ratio, and the set value of the material conveying flow rate according to the predicted output and control target of the system, enabling the system to reach the optimal operating state under the premise of meeting various constraints, thereby improving the product quality of the distillation system, stabilizing the distillation process, and reducing product quality fluctuations.

[0081] As one of the implementation manners 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, and the gradients 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 are calculated, including:

[0082] Initialize the opening of the regulating valve, the adjustment value of the reflux ratio, and the set value of the material conveying flow rate to initial values;

[0083] Calculate the first gradient of the objective function with respect to the change in the opening of the regulating valve;

[0084] Calculate the second gradient of the objective function with respect to the adjustment value of the reflux ratio;

[0085] Calculate the third gradient of the objective function with respect to the set value of the material conveying flow rate;

[0086] Update the manipulated variables based on the gradients calculated according to the preset step size of the gradient descent;

[0087] Set the convergence condition and perform iterative solution, repeating the above processes of gradient calculation, manipulated variable update, and convergence judgment until the convergence condition is met.

[0088] Specifically, initialize the control valve opening, reflux ratio adjustment value, and material conveying flow rate set value to their initial values. Then, calculate the first gradient of the objective function with respect to the change in the control valve opening, the second gradient of the reflux ratio adjustment value, and the third gradient of the material conveying flow rate set value. Through these gradient calculations, the system can clarify the influence degree and direction of each operating variable on the objective function, which enables precise adjustment of the operating variables in the direction of decreasing the objective function value during the optimization process. Based on the preset step size of gradient descent, update the operating variables using the calculated gradients, which neither causes the optimization process to miss the optimal solution due to an overly large step size nor makes the optimization process too slow due to an overly small step size, achieving the 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, computational resources are effectively saved, the control accuracy of the distillation system is improved, and the operating state of the system is stabilized.

[0089] As one implementation of an intermittent distillation system based on intelligent control, the system further 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 the preset safety threshold, the alarm module issues an alarm signal.

[0090] The alarm module in the present invention further includes a priority alarm response mechanism for classifying and preferentially responding to alarm signals according to the severity of different faults and their impact on the production process. When parameters such as temperature, pressure, flow rate, or material concentration in the system exceed the preset safety threshold, the alarm module automatically judges and sets different alarm levels according to 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 according to the impact degree of the fault on the distillation process and trigger different levels of response.

[0091] 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 in a timely manner through measures such as audible and visual alarms, automatically closing relevant valves, or starting an emergency shutdown procedure 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 handling.

[0092] If the system detects that the flow rate or material concentration exceeds the set threshold, but this fault does not involve direct equipment safety hazards and only may affect product quality or production efficiency, the alarm module will determine it as a medium-priority alarm according to the preset response strategy. At this time, the alarm module will emit an alarm signal to remind the operator to pay attention to the current operating status, display detailed abnormal parameters, and at the same time suggest measures such as adjusting the flow rate or concentration to optimize the production process.

[0093] For some minor parameter fluctuations, such as the values of temperature, pressure, etc. slightly deviating from the safety threshold, but having a small impact on the system's safety and production process, the alarm module will classify them as low-priority alarms and prompt the operator to pay attention to the relevant parameters in a concise manner. Such alarm signals will not immediately take emergency measures such as shutting down the machine, but will be recorded in the system log for the operator to check and adjust later.

[0094] During the alarm response process, the system will automatically adjust the processing priority according to the alarm level. High-priority alarm signals will be preferentially displayed and trigger the emergency processing mechanism, while low-priority alarms can timely remind the operator to perform subsequent processing without interfering with other emergency operations. This priority-based alarm response mechanism can not only effectively prevent the operator from making misoperations due to excessive alarm information, but also ensure that appropriate countermeasures are taken promptly and accurately when a fault occurs.

[0095] As one of the implementation manners of an intermittent distillation system based on intelligent control, an anti-blocking device is provided inside the distillation column. The anti-blocking device is located between the trays and is used to prevent impurities or precipitates in the material from blocking the channels on the trays.

[0096] As one of the implementation manners of an intermittent distillation system based on intelligent control, the alarm module can emit alarm signals in three ways: sound, light, and screen display. Different types of abnormal data trigger different alarm signals.

[0097] As one of the implementation manners of an intermittent distillation system based on intelligent control, the temperature sensor is a thermocouple temperature sensor; the pressure sensor uses a capacitive pressure sensor; the flow sensor is an electromagnetic flow sensor.

[0098] It should be noted that the above embodiments are only used to illustrate the present application and do not limit the technical solutions described in the present application. Although this specification has described the present application in detail with reference to the above embodiments, those of ordinary skill in the art should understand that those skilled in the art can still modify or equivalently replace the present application. All technical solutions and their improvements that do not depart from the spirit and scope of the present application shall be covered within the scope of the claims of the present application.

Claims

1. An intermittent distillation system based on intelligent control, characterized in that, Including: Rectifying column; 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 is built-in with a model predictive control algorithm; The model predictive control algorithm obtains the optimal control strategy through rolling optimization calculation by establishing a dynamic model of the rectification process, combining the current system state and the set control target, and issues a control instruction to the actuator based on the optimal control strategy; Actuator; The actuator includes a control valve, a reflux ratio control valve and a variable frequency centrifugal pump; The control 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 control 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; Touch screen, connected to the control module; Real-time display of the operating parameters of the rectification system; The model predictive control algorithm obtains the optimal control strategy through rolling optimization calculation by establishing a dynamic model of the rectification process, combining the current system state and the set control target, and issues a control instruction to the actuator based on the optimal control strategy, 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 top product concentration and the target value of the bottom residue concentration; The control module predicts the output of the rectification system in the next period of time according to the established dynamic model and the current system state; The control module constructs an optimization problem aiming at 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 control valve, the adjustment value of the reflux ratio and the set value of the material conveying flow rate of the variable frequency centrifugal pump; The control module converts the calculated optimal control strategy into a control instruction and sends it to the control valve, the reflux ratio control valve and the variable frequency centrifugal pump in the actuator respectively; The control valve adjusts the heating medium flow rate according to the instruction to control the heating power of the reboiler; The reflux ratio control valve adjusts the reflux ratio according to the instruction; The variable frequency centrifugal pump adjusts the material conveying flow rate according to the instruction.

2. The batch distillation system based on intelligent control according to claim 1, characterized in that, The data acquisition module includes: Temperature sensors: At the top of the distillation column, the temperature sensor is installed on the inner wall of the pipeline near the steam outlet at the top; at the bottom of the column, the temperature sensor is installed at a position close to the bottom of the column and away from the heating element; at the tray position, the temperature sensor is installed in the liquid phase region near the downcomer of the tray; Pressure sensors: At the top of the column, the pressure sensor is installed on the inner wall near the top of the gas phase space at the top; at the bottom of the column, the pressure sensor is installed on the inner wall of the gas phase space at the bottom of the column; Flow sensors: On the raw material delivery pipeline, the flow sensor is installed at a position close to the feed inlet of the distillation column; on the heating medium pipeline, the flow sensor is installed at the inlet near the reboiler; on the reflux pipeline, the flow sensor is installed at the position where the reflux liquid enters the top of the column; Concentration sensors: At the top of the column, the concentration sensor is installed on the condensate reflux pipeline at the top; at the bottom of the column, the concentration sensor is installed on the bottom product pipeline of the column.

3. The batch distillation system based on intelligent control according to claim 2, wherein The configuration process of the dynamic model of the distillation process includes: constructing an initial dynamic model based on material balance, energy balance, and phase equilibrium; the initial dynamic model includes a material balance equation, an energy balance equation, and a phase equilibrium equation; among them, the material balance equation is used to establish a differential equation for each tray in the distillation column to describe the material balance of each component according to the material flow rate and composition entering and leaving the tray; the energy balance equation is used to establish an energy balance equation for each tray to reflect the temperature change in the column; the phase equilibrium equation is used to describe the equilibrium relationship between the gas and liquid phases; configuring tray characteristic parameters and operating parameters; the tray characteristic parameters include tray type, number of trays, tray spacing, and column diameter; the operating parameters include feed flow rate, feed composition, feed position, reflux ratio, and reboiler heating power; calibrating and validating the parameters of the dynamic model of the distillation process using historical data under different operating conditions.

4. The intermittent distillation system based on intelligent control according to claim 3, wherein The control module constructs an optimization problem aiming at 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 control valve, the adjustment value of the reflux ratio, and the set value of the material conveying flow rate of the variable-frequency centrifugal pump, including: the control module constructs an optimization problem aiming to minimize the objective function based on the predicted system output and the set control target; the construction of the objective function is based on the deviation of the top product concentration, the deviation of the bottom residue concentration, the opening change of the control valve, the adjustment value of the reflux ratio, and the deviation of the set value of the material conveying flow rate; when solving the optimization problem, the gradient descent method is used as the basic solution algorithm to calculate the gradients of the objective function with respect to the opening change of the control valve, the adjustment value of the reflux ratio, and the set value of the material conveying flow rate; boundary constraints on the operating variables are added to the solution process of the optimization problem; the boundary constraints include: the opening of the control 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 set value of the material conveying flow rate 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 iteratively performed until the convergence condition is satisfied; the convergence condition is that the change in the objective function value is less than a preset threshold.

5. The intermittent rectification system based on intelligent control according to claim 4, characterized in that, When solving the optimization problem, the gradient descent method is used as the basic solution algorithm to calculate the gradients of the objective function with respect to the opening change of the control valve, the adjustment value of the reflux ratio, and the set value of the material conveying flow rate, including: initializing the opening of the control valve, the adjustment value of the reflux ratio, and the set value of the material conveying flow rate to initial values; calculating the first gradient of the objective function with respect to the opening change of the control valve; calculating the second gradient of the objective function with respect to the adjustment value of the reflux ratio; calculating the third gradient of the objective function with respect to the set value of the material conveying flow rate; updating the operating variables based on the gradients calculated with the preset step size of the gradient descent; setting the convergence condition and performing iterative solution, repeating the above processes of gradient calculation, operating variable update, and convergence judgment until the convergence condition is satisfied.

6. The intermittent rectification system based on intelligent control according to claim 5, characterized in that, The system further 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 the preset safety threshold, the alarm module emits an alarm signal.

7. The batch distillation system based on intelligent control according to claim 6, wherein, An anti-blocking device is provided inside the distillation column. The anti-blocking device is located between the trays and is used to prevent impurities or precipitates in the material from blocking the channels on the trays.

8. An intermittent rectification system based on intelligent control according to claim 7, characterized in that, The alarm module can emit alarm signals in three ways: sound, light, and screen display. Different types of abnormal data trigger different alarm signals.

9. An intermittent rectification system based on intelligent control according to claim 8, characterized in that, The temperature sensor is a thermocouple temperature sensor; the pressure sensor is a capacitive pressure sensor; the flow sensor is an electromagnetic flow sensor.

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