Control method and control system for multi-effect rectification system

Abstract

Disclosed in the present invention are a control method and control system for a multi-effect rectification system. The method comprises: collecting in real time process parameter data in a multi-effect rectification system, comprising a vapor flow rate, a feed flow rate, a reflux rate, and a product rate of each rectification column, and the temperature and pressure of each tray in each rectification column; for each rectification column, on the basis of a fully-automatic load adjustment algorithm, acquiring static operation parameters of each rectification column in a set load section, the static operation parameters comprising the vapor flow rate, the reflux rate, and the production rate of each rectification column; and for each rectification column, on the basis of the static operation parameters, obtaining dynamic operation parameters during load change by means of a load transition algorithm, and controlling the multi-effect rectification system on the basis of the dynamic operation parameters. The present invention can automatically adapt to feed change and production load adjustment, optimizes energy efficiency and product quality during rectification, automatically adjusts system parameters when a production load fluctuates, and realizes optimal control of energy efficiency and product quality.

Description

A control method and control system for a multi-effect distillation system Technical Field

[0001] This invention relates to the field of liquid phase separation technology, and in particular to a control method and control system for a multi-effect distillation system. Background Technology

[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, existing technologies often rely on manual adjustments based on operator experience when dealing with feed fluctuations and changes in production load. This is not only inefficient but also makes it difficult to guarantee the stability of product quality and results in energy waste. Summary of the Invention

[0003] Technical Objective: To address the shortcomings of existing technologies where load adjustment relies on manual experience, this invention discloses a control method and control system for a multi-effect distillation system. This system can automatically adapt to changes in feed and production load adjustments, optimize the energy efficiency and product quality of the distillation process, and automatically adjust system parameters when production load fluctuates, thereby achieving optimal control of energy efficiency and product quality.

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

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

[0006] Real-time acquisition of process parameter data in the multi-effect distillation system, including steam flow rate, feed flow rate, reflux flow rate, and output flow rate of each distillation column; as well as the temperature and pressure of each tray in each distillation column;

[0007] For each distillation column, its static operating parameters within a set load range are obtained based on a fully automatic load adjustment algorithm; the static operating parameters include the steam flow rate, reflux flow rate, and output rate of each distillation column.

[0008] For each distillation column, dynamic operating parameters during load changes are obtained based on static operating parameters using a load transition algorithm. The multi-effect distillation system is then controlled according to these dynamic operating parameters.

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

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

[0011] The intelligent controller module is used to obtain the static operating parameters of each distillation column under a set load range based on a fully automatic load adjustment algorithm; based on the static operating parameters, the dynamic operating parameters during the load change process are obtained through a 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 multi-effect distillation system in real time, ensuring the stable operation of the multi-effect distillation system. Beneficial effects:

[0013] 1. Based on fully automatic load adjustment algorithm and load transition algorithm, this invention can automatically adjust the system's operating parameters under fluctuating production load 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 the control strategy based on adaptive genetic algorithm to automatically adjust the steam flow, reflux flow and feed flow of each tower.

[0014] 2. This invention can not only adapt to production needs under different loads, but also overcomes the shortcomings of traditional genetic algorithms that are prone to getting trapped in local optima by adaptively adjusting the crossover rate and mutation rate, thus achieving efficient search for the global optimal solution;

[0015] 3. During the load transition process, this invention combines the uniform 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.

[0016] 4. This invention can significantly reduce manual operation, improve the automation level of system operation, reduce energy consumption, and improve production efficiency, providing an efficient and stable control solution for multi-effect distillation systems in chemical production. Attached Figure Description

[0017] Figure 1 is a flowchart of the method of the present invention;

[0018] Figure 2 is a block diagram of the control system structure of the present invention;

[0019] Figure 3 is a schematic diagram of the multi-effect distillation system of the present invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present application, the technical solution of a control method and control system for a multi-effect distillation system in the present application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present application.

[0021] As shown in Figure 1, a control method for a multi-effect distillation system according to the present invention includes the following steps:

[0022] S1. Real-time acquisition of process parameter data in the multi-effect distillation system, including steam flow rate, feed flow rate, reflux flow rate, and output rate of each distillation column; as well as the temperature and pressure of each tray in each distillation column; output rate includes top output rate, side stream output rate, and bottom output rate.

[0023] As shown in Figure 3, a multi-effect distillation system comprises several distillation columns connected in sequence. Depending on the distillation process, the feed for each column may come from inside or outside the system. Generally, columns with feed from outside the system are called pre-columns or rougher columns. The external material undergoes simple purification in the pre-column before flowing to other distillation columns. The material flow direction differs from the energy flow direction shown in the figure, depending on the actual process. From an energy perspective, different distillation columns in a multi-effect distillation system have different energy levels or temperature gradients. An energy level is a collection of distillation columns with the same energy or temperature range, typically containing one or more columns. The operating pressure of the columns gradually decreases from the first energy level to the Nth energy level, causing the top temperature of the previous energy level to be slightly higher than the bottom temperature of the next energy level. The top vapor of the previous energy level acts as a heat source to heat the bottom of the next energy level, repeating this process until the Nth energy level. Thus, in the entire process, only steam needs to be added to the first energy level and coolant to the Nth energy level; intermediate columns no longer require external steam or coolant, achieving energy savings.

[0024] S2. For each distillation column, obtain its static operating parameters within a set load range based on a fully automatic load adjustment algorithm; the static operating parameters include the steam flow rate, reflux flow rate, and output rate of each distillation column.

[0025] The fully automatic load adjustment algorithm includes:

[0026] S21. Load segmentation based on 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.

[0027] The calibration data for the multi-effect distillation system is based on data calibrated by the design institute, including standard feed flow rate, reflux ratio, steam consumption, etc., for each distillation column, and is known data. However, in actual production, the load of each distillation column always needs to be adjusted according to actual production conditions, and the operation of the multi-effect distillation system is affected by changes in feed composition, external environment, hardware aging, etc., resulting in some discrepancies with the design conditions. Therefore, the calibration data can only be used for reference. Historical data includes the load of the multi-effect distillation system and the steam flow rate, reflux rate, and output rate of each distillation column during historical time periods; the output rate includes the top output rate, side stream output rate, and bottom output rate.

[0028] The fully automatic load adjustment algorithm is suitable for operating conditions where the load change does not exceed 10% in a single operation. 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%.

[0029] S22. Perform data preprocessing on the operating parameters in the historical data: Extract parameters such as feed load, steam flow rate, return flow rate, and output rate for each load range from the historical data, remove noise and outliers, and unify the units for use in the initialization and optimization of the adaptive algorithm.

[0030] S23. Obtain the optimal solution for each stage of the distillation column using an adaptive genetic algorithm. The optimal solution includes the steam flow rate, reflux flow rate, and output rate for each stage of the distillation column. The adaptive genetic algorithm includes:

[0031] S231, Coding method: Real number coding is used to represent the operating parameters of each individual, including pre-tower steam flow rate, pre-tower reflux flow rate, other tower steam flow rate, other tower reflux flow rate, and other tower feed flow rate; the chromosome form of the individual is... Where v ps v is the pre-steam flow rate of the tower. pr For the pre-tower return flow rate, Let be the steam flow rate of the i-th distillation column. Let be the reflux flow rate of the i-th distillation column. Let be the feed flow rate of the i-th distillation column.

[0032] S232. Initial population generation: Extract several sets of operating parameter data and load intervals from historical data to generate the initial population. Randomly generate the steam flow rate, reflux flow rate and feed flow rate 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.

[0033] S233. Fitness function design: Taking into account both product quality and energy consumption objectives, the weighting coefficients are dynamically adjusted to adapt to different production environments. The formula for calculating the fitness function is: F(j)=ω1×Q j -ω2×E j

[0034] Where F(j) is the fitness function of the j-th individual, Q j For the quality of the top product of the j-th individual, E j Let ω1 be the energy consumption of the j-th individual, which is represented by the cumulative steam flow rate, i.e., the integral of the flow rate. It is usually expressed by the cumulative steam flow rate or converted into electricity consumption (kWh). ω1 is the weighting coefficient of the product quality at the top of the tower for the j-th individual, and ω2 is the weighting coefficient of the energy consumption for the j-th individual. ω1 and ω2 are dynamically adjusted according to environmental parameters (load) and can be preset to adjust.

[0035] In this invention, the quality index of the product at the top of the tower is calculated using a soft measurement method. This method combines the following three key input parameters to estimate the real-time quality of the product at the top of the tower:

[0036] (1) Top temperature of the column: The top temperature of the column is one of the basic parameters for calculating product quality. Since the temperature difference near the top of the column is very small when separating relatively pure components, even a small change in temperature will have a significant impact on the purity of the final product.

[0037] (2) Sensitive plate temperature: The sensitive plate temperature represents the temperature point in the system that is most sensitive to changes in components. Small fluctuations in the sensitive plate temperature directly affect the separation efficiency of the product.

[0038] (3) Dual Temperature Difference: Dual temperature difference refers to the temperature difference between the upper section and the lower section of the distillation column. The upper temperature difference reflects the influence of feed flow rate and column pressure changes on the heat and mass transfer state near the top of the column, while the lower temperature difference reflects the changes near the bottom of the column. The difference between the two can effectively offset the interference caused by changes in pressure drop within the column, ensuring the reliability of product quality calculations.

[0039] The product quality calculation at the top of the tower is based on the above three input parameters, and the calculation formula is: Q = f(T) 塔顶 ,T 灵敏板 ,ΔT 上段 ,ΔT 下段 )

[0040] Among them, T 塔顶 T 灵敏板 ΔT 上段 ΔT 下段These are the top temperature, the sensitive plate temperature, the temperature difference between the upper and lower sections of the distillation column, and the temperature difference between the lower and upper sections of the distillation column, respectively; Q is the top product quality, and f() is the soft sensing function; by substituting these input parameters into the soft sensing function, the top product quality is calculated in real time and used as a constraint in the intelligent control algorithm; the soft sensing function is calculated based on factory production data through multivariate linear regression fitting.

[0041] 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 capability, and a lower crossover rate and a higher mutation rate in the later stage to avoid local optima.

[0042] The formulas for calculating the crossover rate and the mutation rate are:

[0043] Among them, P c , These represent the crossover rate, the upper boundary of the crossover rate, and the lower boundary of the crossover rate, respectively, P. m , Here, represents the mutation rate, the upper bound of the mutation rate, and the lower bound 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 generation and the average fitness of the population; Δm is the difference between the fitness of the mutated 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 These are the higher fitness among the crossover parents, the population average fitness, and the fitness of the variant individuals, respectively.

[0044] S235. Selection Operation: The tournament selection operation is adopted. After testing and verification, tournament selection has shown superior performance in terms of convergence speed and diversity. Specific implementation details include: randomly selecting a number of individuals from the current population to form a tournament; selecting the individual with the best fitness in the tournament to enter the next generation; and continuously repeating the above process.

[0045] S236. Crossover operation: A single-point crossover operation is used to randomly select two individuals from the selected parent individuals and crossover at random positions in their gene sequences to generate two new offspring individuals.

[0046] S237. Mutation Operation: To maintain population diversity and avoid local optima, an adaptive mutation mechanism is employed. Specifically, the adaptive mutation mechanism includes: randomly selecting certain operational parameters of individuals and making minor perturbations to ensure that the mutated individuals maintain a reasonable fitness range.

[0047] S238. Population Update: The update process of each generation retains the best-performing individuals from the previous generation (elite strategy) based on fitness values, while generating new offspring individuals through selection, crossover, and mutation operations to form the next generation population.

[0048] S239. Termination condition: If the change in population fitness is less than the set threshold over several consecutive generations, it indicates that the algorithm has converged to the optimal solution.

[0049] S24. Interpolation Processing: For load intervals not covered by historical data, linear interpolation is used to estimate the operating parameters of these intervals. The linear interpolation calculation formula includes:

[0050] Where 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.

[0051] S25. Dynamic optimization: Periodically integrate the data obtained from interpolation into the dataset and re-optimize the adaptive genetic algorithm to improve the accuracy of load control and environmental adaptability.

[0052] S3. For each distillation column, based on the static operating parameters, a load transition algorithm is used to obtain the dynamic operating parameters during the load change process. The multi-effect distillation system is then controlled according to these dynamic operating parameters. In the multi-effect distillation system, to ensure that the operating parameters of each column are adjusted in a coordinated manner during load changes and to avoid system instability and energy efficiency loss, this invention adopts a load transition algorithm, the contents of which include:

[0053] S31. A uniform rate adjustment strategy is adopted for the pre-column: Since the pre-column has a relatively small impact on the quality of the top product and its main function is to provide material distribution, a uniform rate change strategy is adopted for adjusting the steam and reflux flow rates of the pre-column. This uniform rate adjustment strategy ensures a smooth transition of steam and reflux flow rates during load changes, reducing the impact on subsequent columns. The specific adjustment rate is automatically set according to the load change amplitude and time requirements of the multi-effect distillation system.

[0054] S32. For other towers, a PID dynamic adjustment strategy based on a feedforward neural network is adopted:

[0055] S321. When the load changes, the operating parameters of each tower, such as steam flow rate, reflux flow rate, and feed flow rate, are adjusted successively to ultimately achieve the target static operating parameters calculated by the adaptive genetic algorithm. Taking a certain steam flow rate as an example, the target steam flow rate is S. target The current steam flow rate is S. current Each adjustment is made according to a certain proportion, and the adjustment formula is: S new =S current +α(Starget -S current )

[0056] Among them, S new The adjusted steam flow rate is represented by α, which is an adjustment coefficient used to control the adjustment step size and should be selected according to the actual situation.

[0057] S322. After each modification of the setpoint, an adjustment period is introduced to ensure system stability during the adjustment process. During this adjustment period, the system performs feedback corrections based on mass and energy constraints. Mass is obtained through a soft sensor model; the energy constraint is the steam consumption constraint. Taking methanol distillation as an example, a manufacturer requires a methanol concentration greater than 99.96% in the product and an energy saving rate greater than 1.5%. Substituting the obtained mass and energy, the range of operating parameters is solved using linear programming. This process is implemented using existing technology.

[0058] S323. During the adjustment period, the setpoint is not changed directly. Instead, the PID parameters are slightly adjusted based on the neural network's assessment of the current state of the multi-effect distillation system. After each PID parameter adjustment, the system generates new feedback, and the neural network continues to adjust the PID parameters based on this feedback until the predetermined steady state is reached or the next adjustment period begins.

[0059] S324, Neural Network Algorithm: To ensure fast response and efficient adjustment, a simplified feedforward neural network is employed. The input layer receives the current loop error, error rate of change, column top temperature, sensitive plate temperature, column bottom temperature, and the upstream and downstream thermal integration temperature and flow rate. After processing through a hidden layer (10-20 neurons), the output layer provides adjustment suggestions for the PID parameters. The feedforward neural network is trained using gradient descent, adjusting the PID parameters in real time based on historical data.

[0060] As shown in Figure 2, the present invention also discloses a control system for a multi-effect distillation system, comprising:

[0061] Data Acquisition Module: This module collects process parameter data from the multi-effect distillation system in real time, including feed flow rate, steam flow rate, reflux flow rate, feed load, temperature, and pressure for each distillation column. It uses the OPC DA communication protocol to ensure data synchronization at least every three seconds, guaranteeing the real-time performance and accuracy of the process data. Furthermore, this module has fault diagnosis capabilities, enabling it to quickly detect and issue alarms in the event of communication interruptions or data anomalies, ensuring stable system operation.

[0062] The intelligent controller module is used to obtain the static operating parameters of each distillation column under a set load range based on a fully automatic load adjustment algorithm. Based on these static parameters, a load transition algorithm is used to obtain the dynamic operating parameters during load changes. The multi-effect distillation system is then controlled according to these dynamic operating parameters. This intelligent controller operates independently of the DCS system but communicates bidirectionally with the DCS system via the standard OPC DA communication protocol, feeding back the dynamic operating parameters to the DCS system for execution of specific operations. The DCS system, or Distributed Control System, is a control system specifically designed for industrial process automation and is widely used in chemical, petroleum, power, and metallurgical industries. Its core idea is to distribute control functions across multiple distributed units while simultaneously achieving centralized monitoring and management through a communication network.

[0063] The online monitoring module is used to monitor the process parameters and operating status of the multi-effect distillation system in real time, ensuring its stable operation. If any process parameter exceeds the preset range, the online monitoring module will send an alarm message to the intelligent controller, indicating possible equipment failure or process fluctuations. This module also features data logging and trend analysis capabilities, enabling the analysis of historical data to provide data support for subsequent system optimization and troubleshooting.

[0064] The present invention also discloses an electronic device, the device comprising: a memory for storing a computer program; and a processor for executing the computer program to cause the device to perform the aforementioned control method for a multi-effect distillation system.

[0065] The present invention also provides a computer storage medium on which a computer program is stored. When the computer program is run, the device running the computer program implements the aforementioned control method for a multi-effect distillation system.

[0066] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that all or part of the steps in the methods of the above embodiments can be implemented by means of software plus a general-purpose hardware platform. Based on this understanding, the technical solution of this application can be embodied in the form of a software product. This computer software product can be stored in a storage medium. The memory can be various types of memory, such as random access memory, read-only memory, flash memory, etc., such as read-only memory (ROM) / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which can be a personal computer, server, or network communication device such as a router) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0067] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A control method for a multi-effect distillation system, characterized in that the method... include: Real-time acquisition of process parameter data in the multi-effect distillation system, including steam flow rate, feed flow rate, reflux flow rate, and output flow rate of each distillation column; as well as the temperature and pressure of each tray in each distillation column; For each distillation column, its static operating parameters within a set load range are obtained based on a fully automatic load adjustment algorithm; the static operating parameters include the steam flow rate, reflux flow rate, and output rate of each distillation column. For each distillation column, dynamic operating parameters during load changes are obtained based on static operating parameters using a load transition algorithm. The multi-effect distillation system is then controlled according to these dynamic operating parameters.

2. The control method for a multi-effect distillation system according to claim 1, characterized in that, The fully automatic load adjustment algorithm includes: The load is segmented according to a preset range; Perform data preprocessing on the operational parameters in historical data; The optimal solution for the distillation column is obtained through an adaptive genetic algorithm. The optimal solution includes the steam flow rate, reflux flow rate, and output rate for each stage of the distillation column.

3. The control method for a multi-effect distillation system according to claim 2, characterized in that, Adaptive genetic algorithms include the following steps: S231. The operating parameters of each individual are represented using real number encoding, including pre-tower steam flow rate, pre-tower reflux flow rate, other tower steam flow rate, other tower reflux flow rate, and other tower feed flow rate; the chromosome form of the individual is [v ps ,v pr ,v tis ,v tir ,v tif ], where v pr v is the pre-tower return flow rate. tis Let v be the steam flow rate of the i-th distillation column. tir Let v be the reflux flow rate of the i-th distillation column. tif Let be the feed flow rate of the i-th distillation column; S232. Initial population generation: Extract several sets of operational parameter data and load intervals from historical data to generate the initial population. S233. Fitness function design, which comprehensively considers the two objectives of product quality and energy consumption, and dynamically adjusts the weighting coefficients 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 capability, and a lower crossover rate and a higher mutation rate in the later stage to avoid local optima. S235. Selection operation: The tournament selection operation is adopted. Several individuals are randomly selected from the current population to form a tournament. The individual with the best fitness is selected from the tournament to enter the next generation. S236. Crossover operation: A single-point crossover operation is used to randomly select two individuals from the selected parent individuals and crossover at random positions in their gene sequences to generate two new offspring individuals. S237. Mutation operation: An adaptive mutation mechanism is adopted to randomly select several operation parameters in an individual and perform small perturbations. S238. Population Update: The update process of each generation is based on fitness values. According to the elite strategy, the best performing individuals from the previous generation are retained, while new offspring individuals are generated through selection, crossover, and mutation operations to form the next generation population. S239. Termination condition: If the change in population fitness is less than the set threshold over several consecutive generations, it indicates that the algorithm has converged to the optimal solution.

4. The control method for a multi-effect distillation system according to claim 3, characterized in that: The fitness function is calculated as follows: F(j)=ω1×Q j -ω2×E j Where F(j) is the fitness function of the j-th individual, Q j For the quality of the top product of the j-th individual, E j Let ω1 be the energy consumption of the j-th individual, ω2 be the weighting coefficient of the product quality at the top of the tower for the j-th individual, and ω3 be the weighting coefficient of the energy consumption of the j-th individual.

5. The control method for a multi-effect distillation system according to claim 4, characterized in that: The basic formula for calculating the quality of the product at the top of the tower 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 These represent the top temperature, the temperature of the sensitive plate, the temperature difference in the upper section of the distillation column, and the temperature difference in the lower section of the distillation column, respectively; Q is the product quality at the top of the column, and f() is a soft sensing function.

6. The control method for a multi-effect distillation system according to claim 4, characterized in that: The formulas for calculating the crossover rate and the mutation rate are: Among them, P c , These represent the crossover rate, the upper boundary of the crossover rate, and the lower boundary of the crossover rate, respectively, P. m , Here, represents the mutation rate, the upper bound of the mutation rate, and the lower bound 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 generation and the average fitness of the population; Δm is the difference between the fitness of the mutated 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 These represent the higher fitness among the crossover parents, the population average fitness, and the fitness of the variant individuals, respectively.

7. The control method for a multi-effect distillation system according to claim 2, characterized in that: Before obtaining the optimal solution for the distillation column using an adaptive genetic algorithm, interpolation and dynamic optimization are also included. The interpolation process includes: estimating the operating parameters of load intervals not covered in the initial dataset using a linear interpolation method. Dynamic optimization includes periodically integrating the data obtained from interpolation into the dataset and re-optimizing the adaptive genetic algorithm.

8. The control method for a multi-effect distillation system according to claim 7, characterized in that: The formulas for linear interpolation include: Where 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 for a multi-effect distillation system according to claim 1, characterized in that: The load transition algorithm includes: a uniform speed adjustment strategy for the pre-tower; and a PID dynamic adjustment strategy based on a feedforward neural network for other towers.

10. A control system for a multi-effect distillation system, used to implement the control method for a multi-effect distillation system according to any one of claims 1-9, 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 rate, steam flow rate, reflux flow rate, feed load, temperature, and pressure of each distillation column; The intelligent controller module is used to obtain the static operating parameters of each distillation column under a set load range based on a fully automatic load adjustment algorithm; based on the static operating parameters, the dynamic operating parameters during the load change process are obtained through a 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 multi-effect distillation system in real time, ensuring the stable operation of the multi-effect distillation system.

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