Control method and control system of multi-effect rectification system

Abstract

The invention discloses a multi-effect rectification system control method and a control system thereof.The method comprises the steps that technological parameter data in a multi-effect rectification system are collected in real time, and the technological parameter data comprise the steam flow, the feeding flow, the reflux flow and the recovery quantity of each stage of rectification tower; the temperature and the pressure of each tower plate in each stage of rectifying tower are measured; for each stage of rectifying tower, acquiring static operation parameters of the rectifying tower in a set load section based on a full-automatic load regulation algorithm; the static operation parameters comprise the steam flow, the reflux quantity and the extraction quantity of each stage of rectifying tower; for each stage of rectifying tower, on the basis of the static operation parameters, dynamic operation parameters in the load change process are obtained through a load transition algorithm, and the multi-effect rectifying system is controlled according to the dynamic operation parameters. The method can automatically adapt to feeding change and production load adjustment, optimizes energy efficiency and product quality in the rectification process, automatically adjusts system parameters when the production load fluctuates, and realizes optimal control of energy efficiency and product quality.

Description

Technical Field

[0001] The present invention relates to the technical field of liquid phase separation, and in particular to a control method and a control system of a multiple-effect distillation system. Background Art

[0002] With the rapid development of the chemical industry, multi-effect distillation systems are widely used in chemical production due to their high energy efficiency and flexible operation. However, the existing technology often relies on the operator's experience to make manual adjustments when dealing with feed fluctuations and production load changes. This is not only inefficient, but also difficult to ensure the stability of product quality, and also causes energy waste. Summary of the invention

[0003] Technical purpose: In view of the defect in the prior art that load adjustment depends on manual experience, the present invention discloses a control method and a control system for a multi-effect distillation system, which can automatically adapt to feed changes and production load adjustments, optimize the energy efficiency and product quality of the distillation process, and automatically adjust system parameters when the production load fluctuates to achieve optimal control of energy efficiency and product quality.

[0004] Technical solution: In order to achieve the above technical objectives, the present invention adopts the following technical solution.

[0005] A control method for a multiple-effect distillation system, the method comprising:

[0006] Real-time collection of process parameter data in the multi-effect distillation system, including steam flow, feed flow, reflux flow, and production flow of each stage of the distillation tower; as well as the temperature and pressure of each tray in each stage of the distillation tower;

[0007] For each stage of distillation tower, the static operating parameters in the set load section are obtained based on the fully automatic load adjustment algorithm; the static operating parameters include the steam flow, reflux flow, and production volume of each stage of distillation tower;

[0008] For each stage of distillation tower, on the basis of static operating parameters, the dynamic operating parameters in the load change process are obtained through the load transition algorithm, and the multi-effect distillation system is controlled according to the dynamic operating parameters.

[0009] A control system for a multiple-effect distillation system, used to implement the control method for the multiple-effect distillation system described above, comprising:

[0010] Data acquisition module: used to collect process parameter data in the multi-effect distillation system in real time, including feed flow, steam flow, reflux flow, feed load, temperature and pressure of each stage of distillation tower;

[0011] Intelligent controller module: used to obtain the static operating parameters of each distillation tower in the set load section based on the fully automatic load adjustment algorithm; based on the static operating parameters, the dynamic operating parameters in the load change process are obtained through the load transition algorithm, and the multi-effect distillation system is controlled according to the dynamic operating parameters;

[0012] The online monitoring module is used to monitor the process parameters and operating status of the multiple-effect distillation system in real time to ensure the stable operation of the multiple-effect distillation system.

[0013] Beneficial effects:

[0014] 1. The present invention is based on a fully automatic load regulation algorithm and a load transition algorithm, which can automatically adjust the operating parameters of the system when the production load fluctuates to achieve optimal control of product quality and energy efficiency; the key process parameters of the system are acquired in real time through the data acquisition module, and the intelligent controller module executes a control strategy based on an adaptive genetic algorithm to automatically adjust the steam flow, reflux flow and feed flow of each tower.

[0015] 2. The present invention can not only adapt to production requirements under different loads, but also overcome the shortcomings of traditional genetic algorithms that are prone to fall into local optimality by adaptively adjusting the crossover rate and mutation rate, and realize efficient search for the global optimal solution;

[0016] 3. During the load transition process, the present invention combines the uniform speed adjustment of the pre-tower with the dynamic adjustment strategy of other towers to ensure the stable operation of the entire multi-effect distillation system;

[0017] 4. The present invention can greatly reduce manual operations, improve the automation level of system operation, reduce energy consumption, and improve production efficiency, providing an efficient and stable control solution for the multi-effect distillation system in chemical production. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of the method of the present invention;

[0019] Figure 2 It is a structural block diagram of the control system of the present invention;

[0020] Figure 3 It is a schematic diagram of the structure of the multiple-effect distillation system of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the present application, the following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solution of a control method for a multiple-effect distillation system and its control system in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0022] As attached Figure 1 As shown, a control method for a multiple-effect distillation system of the present invention comprises the following steps:

[0023] S1. Real-time collection of process parameter data in the multi-effect distillation system, including steam flow, feed flow, reflux flow, and production volume of each stage of the distillation tower; as well as the temperature and pressure of each plate in each stage of the distillation tower; the production volume includes the top production volume, side line production volume, and bottom production volume.

[0024] As attached Figure 3 As shown in the figure, the multi-effect distillation system includes several distillation towers connected in sequence. According to different distillation processes, the feed of each tower may come from inside or outside the system. Generally speaking, the tower with feed from outside the system is called a pre-tower or a crude tower. The external materials are simply purified by the pre-tower and then flow to other distillation towers. The material flow direction is different from the energy flow direction in the figure, which depends on the actual process. From the energy point of view, in the multi-effect distillation system, different distillation towers have different energy levels or temperature gradients. An energy level is a collection of distillation towers with the same energy or temperature range, usually including one or more distillation towers. The operating pressure of the tower gradually decreases from the first energy level to the Nth energy level, so that the top temperature of the tower at the previous energy level is slightly higher than the bottom temperature of the tower at the next energy level. The top steam of the tower at the previous energy level is used as a heat source to heat the bottom of the tower at the next energy level, and this process is repeated until the Nth energy level. In this way, in the entire process, only steam needs to be added to the first energy level and coolant needs to be added to the Nth energy level. The intermediate tower no longer needs to add steam and coolant, thus achieving energy saving effect.

[0025] S2. For each stage of the distillation tower, obtain its static operating parameters in the set load section based on the fully automatic load adjustment algorithm; the static operating parameters include the steam flow rate, reflux rate, and production rate of each stage of the distillation tower;

[0026] Fully automatic load regulation algorithms include:

[0027] S21. Load segmentation according to a preset range: Based on the calibration data and historical data of the multi-effect distillation system, the load range is divided into several intervals, and the operating parameters are automatically adjusted in each interval to optimize energy efficiency and product quality;

[0028] Among them, the calibration data of the multi-effect distillation system is the data calibrated by the design institute, including the standard feed flow, reflux ratio, steam consumption, etc. of each stage of the distillation tower, which are known data; however, in the actual production process, the load of each stage of the distillation tower always needs to be adjusted according to the actual production situation, and the operation of the multi-effect distillation system is affected by changes in feed components, changes in the external environment, hardware aging, etc., and there are certain discrepancies with the design conditions. Therefore, the calibration data can only be used for reference and comparison. Historical data includes the load of the multi-effect distillation system corresponding to the historical time period during the use of the multi-effect distillation system and the steam flow, reflux, and production of each stage of the distillation tower; the production includes the top production, side line production, and bottom production;

[0029] The fully automatic load regulation algorithm is applicable to working conditions where a single load change does not exceed 10%. In some embodiments of the present invention, the load range is divided into several intervals, such as 80%-85%, 85%-90%, 90%-95%, 95%-100%, and 100%-105%.

[0030] S22. Perform data preprocessing on the operating parameters in the historical data: extract parameters such as feed load, steam flow, reflux flow, and production volume in each load interval from the historical data, remove noise and outliers, and unify the dimensions for initialization and optimization of the adaptive algorithm.

[0031] S23. Obtain the optimal solution of each stage of the distillation tower by using an adaptive genetic algorithm. The optimal solution includes the steam flow rate, reflux rate, and production rate of each stage of the distillation tower. The adaptive genetic algorithm includes:

[0032] S231, encoding method, using real number encoding to represent the operating parameters of each individual, including pre-tower steam flow, pre-tower reflux flow, other tower steam flow, other tower reflux flow, other tower feed flow; the chromosome form of the individual is where v ps is the pre-tower steam flow rate, v pr is the pre-tower reflux flow rate, is the steam flow rate of the i-th distillation tower, is the reflux flow rate of the i-th distillation tower, is the feed flow rate of the i-th distillation tower.

[0033] S232, generating an initial population, extracting several groups of operating parameter data and load intervals from historical data to generate an initial population, and randomly generating steam flow, reflux flow and feed flow of all distillation columns in each load interval to supplement the population, so as to ensure the diversity and extensiveness of the adaptive genetic algorithm;

[0034] S233, fitness function design, comprehensively considers the two goals of product quality and energy consumption, and dynamically adjusts the weight coefficient to adapt to different production environments. The fitness function calculation formula is:

[0035] F(j)=ω1×Q j -ω2×E j

[0036] Among them, F(j) is the fitness function of the jth individual, Q j is the quality of the top product of the jth individual, E j is the energy consumption of the jth individual, which is expressed by the cumulative steam flow, i.e., the integral of the flow, usually expressed by the cumulative steam flow or converted into electricity consumption (kwh), ω1 is the weight coefficient of the quality of the top product of the jth individual, ω2 is the weight coefficient of the energy consumption of the jth individual; ω1 and ω2 are dynamically adjusted according to environmental parameters (load), and can be adjusted by preset values;

[0037] In the present invention, the calculation of the tower top product quality index adopts a soft measurement method. This method combines the following three key input parameters to deduce the real-time quality of the tower top product:

[0038] (1) Tower top temperature: Tower top temperature is one of the basic parameters for calculating product quality. Since the temperature difference near the tower top is very small when separating relatively pure components, a slight change in temperature will have a significant impact on the purity of the final product.

[0039] (2) Sensitive plate temperature: The sensitive plate temperature represents the temperature point in the system that is most sensitive to component changes. Small fluctuations in the sensitive plate temperature directly affect the separation efficiency of the product.

[0040] (3) Double temperature difference: Double temperature difference refers to the temperature difference of the upper section and the lower section of the distillation tower. The upper section temperature difference reflects the influence of feed flow rate and tower pressure changes on the heat and material transfer state near the top of the tower, while the lower section temperature difference reflects the changes near the bottom of the tower. The difference between the two can effectively offset the interference caused by the pressure drop change in the tower, ensuring the reliability of product quality calculation.

[0041] The calculation of the tower top product quality is based on the above three input parameters, and the calculation formula is:

[0042] Q=f(T 塔顶 , T 灵敏板 , ΔT 上段 , ΔT 下段 )

[0043] Among them, T 塔顶 , T 灵敏板 , ΔT 上段 , ΔT 下段They are respectively the tower top temperature, the sensitive plate temperature, the upper section temperature difference of the distillation tower, and the lower section temperature difference of the distillation tower; Q is the tower top product quality, and f() is the soft measurement function; by bringing these input parameters into the soft measurement function, the tower top product quality is calculated in real time as a constraint condition in the intelligent control algorithm; the soft measurement function is calculated based on the factory production data through multi-variable linear regression fitting.

[0044] S234, Adaptive crossover rate and mutation rate design. The adaptive genetic algorithm uses a higher crossover rate and a lower mutation rate in the initial stage to improve the global search capability, and a lower crossover rate and a higher mutation rate in the later stage to avoid local optimality.

[0045] The calculation formulas for crossover rate and mutation rate are:

[0046]

[0047] Among them, P c , are the crossover rate, the upper boundary of the crossover rate, and the lower boundary of the crossover rate, respectively. m , are the mutation rate, the upper boundary of the mutation rate, and the lower boundary of the mutation rate respectively; u is a constant with a value of 9.90438; Δc is the difference between the fitness of the better individual in the crossover parent and the average fitness of the population, Δm is the difference between the fitness of the mutant individual and the average fitness of the population, and Δ is the difference between the fitness of the best individual in the population and the average fitness of the population. f avg 、f m They are respectively the larger fitness among the crossover parents, the average fitness of the population and the fitness of the mutant individuals.

[0048] S235, selection operation: The tournament selection operation is adopted. After testing and verification, the tournament selection is superior in terms of convergence speed and diversity. The specific implementation details include: randomly selecting a number of individuals from the current population to form a tournament, selecting the individuals with the best fitness in the tournament to enter the next generation, and repeating the above process continuously.

[0049] S236, Crossover operation: Using a single-point crossover operation, two individuals are randomly selected from the selected parent individuals, and crossover is performed at random positions in their gene sequences to generate two new offspring individuals.

[0050] S237, mutation operation: In order to maintain population diversity and avoid local optimal solutions, an adaptive mutation mechanism is used. Specifically, the adaptive mutation mechanism includes: randomly selecting certain operating parameters in individuals and performing small perturbations to ensure that the mutated individuals maintain a reasonable fitness range.

[0051] S238. Update population: The update process of each generation retains the best performing individuals in the previous generation (elite strategy) according to the fitness value, and generates new offspring individuals through selection, crossover and mutation operations to form the next generation population.

[0052] S239, Termination condition: In several consecutive generations, the change in population fitness is less than the set threshold, indicating that the algorithm has converged to the optimal solution.

[0053] S24, interpolation processing: For load intervals not covered by historical data, the linear interpolation method is used to estimate the operating parameters of these intervals. The linear interpolation calculation formula includes:

[0054]

[0055] Among them, y is the operating parameter, y2 and y1 are the upper and lower boundary values ​​corresponding to the operating parameter, x2 and x1 are the upper and lower boundary values ​​corresponding to the known load, and x is the load to be interpolated.

[0056] S25, Dynamic Optimization: Regularly integrate the data obtained by interpolation processing into the data set and re-optimize the adaptive genetic algorithm to improve the accuracy and environmental adaptability of load control.

[0057] S3. For each stage of distillation tower, based on the static operating parameters, the dynamic operating parameters during the load change process are obtained through the load transition algorithm, and the multi-effect distillation system is controlled according to the dynamic operating parameters; in the multi-effect distillation system, in order to ensure that the operating parameters of each tower are coordinated and adjusted during the load change period to avoid system instability and energy efficiency loss, the present invention adopts a load transition algorithm, the content of which includes:

[0058] S31. Use a uniform speed adjustment strategy for the pre-tower: Since the pre-tower has a relatively small impact on the quality of the top product and its main function is to provide material distribution, the pre-tower steam flow and reflux flow are adjusted using a uniform speed change strategy. The pre-tower uniform speed adjustment strategy ensures that the pre-tower steam and reflux flows transition smoothly during load changes, reducing the impact on subsequent towers. The specific adjustment rate is automatically set according to the load change amplitude and time requirements of the multi-effect distillation system.

[0059] S32. Adopt PID dynamic adjustment strategy based on feedforward neural network for other towers:

[0060] S321. When the load changes, the steam flow rate, reflux flow rate, feed flow rate and other operating parameters of each tower are adjusted successively, and finally the target static operating parameters calculated by the adaptive genetic algorithm are achieved. Taking a certain steam flow rate as an example, the target steam flow rate is S target , the current steam flow is S current , each adjustment is made according to a certain proportion, and the adjustment formula is:

[0061] S new =S current +α(S target -S current )

[0062] Among them, S new is the adjusted steam flow rate, and α is the adjustment coefficient, which is used to control the adjustment step and is selected according to actual conditions.

[0063] S322. After each modification of the set value, an adjustment period is introduced to ensure that the system remains stable during the adjustment process. During this adjustment period, the system performs feedback corrections based on quality constraints and energy constraints. Quality is obtained through a soft measurement model; energy constraints are steam usage constraints. Taking methanol distillation as an example, a manufacturer requires that the methanol concentration in the product is greater than 99.96% and that the energy saving rate is greater than 1.5%. The obtained quality and energy are brought in, and the range of operating parameters is solved through linear programming. This process is implemented using existing technologies.

[0064] S323. During the adjustment period, the set value is not changed directly, but the PID parameters are slightly corrected by the neural network based on the judgment of the current state of the multi-effect distillation system. After each PID parameter correction, the system generates new feedback, and the neural network continues to correct the PID parameters based on these feedbacks until a predetermined steady state is reached or the next adjustment period is entered.

[0065] S324, Neural Network Algorithm: To ensure fast response and efficient adjustment, a simplified feedforward neural network is used. The input layer receives the current loop error, error change rate, tower top temperature, sensitive plate temperature, tower bottom temperature, and upstream and downstream heat integration temperature and flow. After processing through a hidden layer (10-20 neurons), the output layer gives PID parameter adjustment suggestions. The feedforward neural network is trained using the gradient descent method, based on historical data, to adjust the PID parameters in real time.

[0066] As attached Figure 2 As shown, the present invention also discloses a control system for a multiple-effect distillation system, comprising:

[0067] Data acquisition module: used to collect process parameter data in the multi-effect distillation system in real time, including feed flow, steam flow, reflux, feed load, temperature, and pressure of each distillation tower; the module uses the OPC DA communication protocol to ensure data synchronization at least once every three seconds to ensure the real-time and accuracy of process data. In addition, the module also has a fault diagnosis function, which can quickly detect and issue an alarm when communication is interrupted or data is abnormal, ensuring stable operation of the system.

[0068] Intelligent controller module: used to obtain the static operating parameters of each stage of distillation tower in the set load section based on the fully automatic load adjustment algorithm; on the basis of the static operating parameters, the dynamic operating parameters in the load change process are obtained through the load transition algorithm, and the multi-effect distillation system is controlled according to the dynamic operating parameters; the intelligent controller operates independently of the DCS system, and communicates with the DCS system in two directions through the standard OPC DA communication protocol, and feeds back the dynamic operating parameters to the DCS system, and the DCS system performs specific operations. The DCS system, also known as the DCS distributed control system, is a control system designed for industrial process automation and is widely used in chemical, petroleum, electric power, metallurgy and other fields. Its core idea is to distribute the control functions to multiple distributed units, and at the same time realize centralized monitoring and management through the communication network.

[0069] The online monitoring module is used to monitor the process parameters and operating status of the multi-effect distillation system in real time to ensure the stable operation of the multi-effect distillation system. Once the process parameters are found to be out of the preset range, the online monitoring module will send an alarm message to the intelligent controller to indicate possible equipment failure or process fluctuation. The module also has data recording and trend analysis functions, which can analyze historical data and provide data support for subsequent system optimization and troubleshooting.

[0070] The present invention also discloses an electronic device, which includes: a memory for storing a computer program; and a processor for executing the computer program so that the device executes the aforementioned control method for a multiple-effect distillation system.

[0071] The present invention also provides a computer storage medium, wherein a computer program is stored on the computer-readable storage medium. When the computer program is executed, a device executing the computer program implements the aforementioned control method for a multiple-effect distillation system.

[0072] It can be known from the description of the above implementation mode that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, and the computer software product can be stored in a storage medium, and the memory can be various types of memory, which can be a random access memory, a read-only memory, a flash memory, etc., such as a read-only memory (English: read-only memory, ROM) / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0073] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A control method for a multiple-effect distillation system, characterized in that the method include: Real-time collection of process parameter data in the multi-effect distillation system, including steam flow, feed flow, reflux flow, and production flow of each stage of the distillation tower; as well as the temperature and pressure of each tray in each stage of the distillation tower; For each stage of distillation tower, the static operating parameters in the set load section are obtained based on the fully automatic load adjustment algorithm; the static operating parameters include the steam flow, reflux flow, and production volume of each stage of distillation tower; For each stage of distillation tower, on the basis of static operating parameters, the dynamic operating parameters in the load change process are obtained through the load transition algorithm, and the multi-effect distillation system is controlled according to the dynamic operating parameters.

2. The control method of a multiple-effect distillation system according to claim 1, characterized in that: Fully automatic load regulation algorithms include: Load segmentation according to preset range; Perform data preprocessing on the operating parameters in the historical data; The optimal solution of the distillation tower is obtained through an adaptive genetic algorithm, and the optimal solution includes the steam flow rate, reflux rate and production rate of each stage of the distillation tower.

3. The control method of a multiple-effect distillation system according to claim 2, characterized in that: The adaptive genetic algorithm includes the following steps: S231. Use real number coding to represent the operating parameters of each individual, including pre-tower steam flow, pre-tower reflux flow, other tower steam flow, other tower reflux flow, and other tower feed flow; the chromosome form of the individual is where v pr is the pre-tower reflux flow rate, is the steam flow rate of the i-th distillation tower, is the reflux flow rate of the i-th distillation tower, is the feed flow rate of the i-th distillation tower; S232, generating an initial population, extracting several groups of operating parameter data and load intervals from historical data to generate an initial population; S233, fitness function design, comprehensively considers the two goals of product quality and energy consumption, and dynamically adjusts the weight coefficient to adapt to different production environments; S234, Adaptive crossover rate and mutation rate design, the adaptive genetic algorithm uses a higher crossover rate and a lower mutation rate in the early stage to improve the global search ability, and a lower crossover rate and a higher mutation rate in the later stage to avoid local optimality; S235, selection operation: adopt the tournament selection operation, randomly select a number of individuals from the current population to form a tournament, and select the individual with the best fitness in the tournament to enter the next generation; S236, crossover operation: using a single-point crossover operation, two individuals are randomly selected from the selected parent individuals, and crossover is performed at random positions in their gene sequences to generate two new offspring individuals; S237, mutation operation: Adopting the adaptive mutation mechanism, randomly select several operation parameters in individuals and make small perturbations; S238, Update population: The update process of each generation retains the best performing individuals in the previous generation according to the fitness value according to the elite strategy, and generates new offspring individuals through selection, crossover and mutation operations to form the next generation population; S239, Termination condition: In several consecutive generations, the change in population fitness is less than the set threshold, indicating that the algorithm has converged to the optimal solution.

4. The control method of a multiple-effect distillation system according to claim 3, characterized in that: The calculation formula of the fitness function is: F(j)=ω1×Q j -ω2×E j Among them, F(j) is the fitness function of the jth individual, Q j is the quality of the top product of the jth individual, E j is the energy consumption of the jth individual, ω1 is the weight coefficient of the quality of the top product of the jth individual, and ω2 is the weight coefficient of the energy consumption of the jth individual.

5. The control method of a multiple-effect distillation system according to claim 4, characterized in that: The basic formula for calculating the quality of the tower top product is: Q=f(T 塔顶 ,T 灵敏板 ,ΔT 上段 ,ΔT 下段 ) Among them, T 塔顶 、T 灵敏板 , ΔT 上段 , ΔT 下段 are respectively the tower top temperature, the sensitive plate temperature, the upper section temperature difference of the distillation tower, and the lower section temperature difference of the distillation tower; Q is the tower top product quality, and f() is the soft measurement function.

6. The control method of a multiple-effect distillation system according to claim 4, characterized in that: The calculation formulas for crossover rate and mutation rate are: Among them, P c , are the crossover rate, the upper boundary of the crossover rate, and the lower boundary of the crossover rate, respectively. m , are the mutation rate, the upper boundary of the mutation rate, and the lower boundary of the mutation rate respectively; u is a constant with a value of 9.90438; Δc is the difference between the fitness of the better individual in the crossover parent and the average fitness of the population, Δm is the difference between the fitness of the mutant individual and the average fitness of the population, Δ is the difference between the fitness of the best individual in the population and the average fitness of the population, and f c max 、f avg 、f m They are respectively the larger fitness among the crossover parents, the average fitness of the population and the fitness of the mutant individuals.

7. The control method of a multiple-effect distillation system according to claim 2, characterized in that: Before obtaining the optimal solution of the distillation column through the adaptive genetic algorithm, interpolation processing and dynamic optimization are also included. The interpolation processing includes: for the load intervals not covered in the initial data set, the operation parameters of these intervals are estimated using the linear interpolation method; Dynamic optimization includes: regularly integrating the data obtained by interpolation processing into the data set and re-optimizing the adaptive genetic algorithm.

8. The control method of a multiple-effect distillation system according to claim 7, characterized in that: The linear interpolation calculation formula includes: Among them, y is the operating parameter, y2 and y1 are the upper and lower boundary values ​​corresponding to the operating parameter, x2 and x1 are the upper and lower boundary values ​​corresponding to the known load, and x is the load to be interpolated.

9. The control method of a multiple-effect distillation system according to claim 1, characterized in that: The load transition algorithm includes: adopting a uniform speed adjustment strategy for the pre-tower; and adopting a PID dynamic adjustment strategy based on a feedforward neural network for other towers.

10. A control system for a multiple-effect distillation system, used to implement a control method for a multiple-effect distillation system according to any one of claims 1 to 9, characterized in that: include: Data acquisition module: used to collect process parameter data in the multi-effect distillation system in real time, including feed flow, steam flow, reflux flow, feed load, temperature and pressure of each stage of distillation tower; Intelligent controller module: used to obtain the static operating parameters of each distillation tower in the set load section based on the fully automatic load adjustment algorithm; based on the static operating parameters, the dynamic operating parameters in the load change process are obtained through the load transition algorithm, and the multi-effect distillation system is controlled according to the dynamic operating parameters; The online monitoring module is used to monitor the process parameters and operating status of the multiple-effect distillation system in real time to ensure the stable operation of the multiple-effect distillation system.

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