Parameter control method and system for low-position flash cooling of wet-process phosphoric acid based on particle swarm algorithm

Abstract

This invention relates to the field of swarm intelligence control technology, and discloses a method and system for controlling low-level flash cooling parameters of wet-process phosphoric acid based on particle swarm optimization (PSO) algorithm. By collecting core process parameters of the slurry and comprehensive operating parameters of the axial flow pump and vacuum pump, and combining the fluctuation amplitude and rate of change of temperature, vacuum degree, and density, the method determines the trend of drastic changes in the slurry reaction. It intelligently identifies four operating conditions: normal, over-temperature, insufficient vacuum, and abnormal slurry density. It then selectively selects target indicators to be optimized and constructs a multi-objective optimization function with dynamic weights. After optimal solution by the intelligent algorithm, the operating load of the axial flow pump and vacuum pump is coordinated and adjusted. This invention achieves full parameter monitoring, intelligent operating condition identification, and collaborative optimization control of multiple devices in the low-level flash cooling section, ensuring that the slurry reaction temperature remains stable within a reasonable range, improving slurry crystallization quality and phosphoric acid extraction efficiency. It is compatible with existing production equipment, has low modification costs, and can achieve long-term stable operation of wet-process phosphoric acid plants.

Description

Technical Field

[0001] This invention relates to the field of swarm intelligence control technology, and in particular to a method and system for controlling the low-temperature flash cooling parameters of wet-process phosphoric acid based on particle swarm optimization algorithm. Background Technology

[0002] In the wet-process phosphoric acid production, the low-level flash cooling section is used to control system temperature, ensure slurry crystallization quality, and improve phosphoric acid extraction efficiency. The main equipment in this section includes an extraction tank, an axial flow pump, and a low-level flash cooler, which together form a closed slurry circulation loop. The axial flow pump provides the power for forced circulation of the slurry between the extraction tank and the low-level flash cooler, and its operating parameters directly affect the circulation efficiency. The vacuum level within the low-level flash cooler determines the flash cooling effect of the slurry; the higher the vacuum level, the more intense the water evaporation and the more significant the cooling.

[0003] The reaction efficiency and crystallization quality of wet-process phosphoric acid slurry are influenced by multiple factors, including temperature, vacuum level, and slurry density. Existing control technologies mostly employ single-parameter adjustment methods, such as adjusting the circulation flow rate of the axial flow pump based solely on the slurry temperature after flash cooling, or adjusting the pumping volume of the vacuum pump based solely on the vacuum level. These methods lack coupled monitoring of multiple process parameters and do not collect comprehensive operating parameters of the axial flow pump and vacuum pump, making it difficult to accurately determine the degree of matching between the equipment's operating status and process requirements.

[0004] Furthermore, existing control methods do not assess the trends in the intensity of slurry reactions, nor do they categorize operating conditions into different types. Instead, they employ fixed adjustment strategies to address all process states. When conditions such as overheating, insufficient vacuum, or abnormal slurry density occur, control lag and poor targeting of adjustments often result in slurry reaction temperatures deviating from the reasonable range, reduced crystallization quality, and decreased filtration efficiency, ultimately affecting the long-term stable operation of the wet-process phosphoric acid plant.

[0005] Therefore, it is necessary to develop a multi-parameter, intelligent optimization control system and method to achieve full-dimensional acquisition of process parameters and equipment operating parameters, and to combine reaction trend judgment and operating condition identification to complete the coordinated optimization and adjustment of axial flow pumps and vacuum pumps, so as to overcome the technical defects of existing single-parameter adjustment methods. Summary of the Invention

[0006] The main objective of this invention is to provide a method and system for controlling low-temperature flash cooling parameters of wet-process phosphoric acid based on particle swarm optimization algorithm, in order to solve the problem in the prior art where the slurry reaction temperature deviates from the reasonable range, the crystallization quality decreases, the filtration efficiency decreases, and thus the long-term stable operation of the wet-process phosphoric acid plant is affected.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A particle swarm optimization (PSO) algorithm-based parameter control method for low-temperature flash cooling of wet-process phosphoric acid is applied to the production equipment of the low-temperature flash cooling section of wet-process phosphoric acid. The method includes: collecting core process parameters of the slurry and comprehensive operating parameters of the equipment; determining the operating condition type based on the reaction trend of the low-temperature flash cooling section of wet-process phosphoric acid; selecting process parameters to be optimized as control targets based on the operating conditions of the low-temperature flash cooling section of wet-process phosphoric acid; constructing a multi-objective optimization function that dynamically allocates the weights of each parameter deviation according to the operating conditions, balancing vacuum degree and density fluctuations while maintaining the optimal temperature range for slurry crystallization; and rapidly converging the multi-objective optimization function to the optimal load combination of the axial flow pump and vacuum pump through collaborative iteration within the simulated ion swarm search space, achieving multi-parameter collaborative closed-loop regulation.

[0008] As a further improvement of the present invention, the core process parameters of the slurry are the slurry temperature after cooling, the vacuum degree of the flash cooler, the solid content / density of the slurry, the slurry inlet temperature, and the slurry inlet pressure; the full-dimensional operating parameters of the equipment are the axial flow pump flow rate / speed / current, and the vacuum pump pumping volume / speed / load.

[0009] As a further improvement of the present invention, the fluctuation range and rate of change of temperature, vacuum degree and density are calculated and coupled to determine the trend of drastic change in slurry reaction. At the same time, based on the preset process parameter threshold range, the current working condition type of the slurry circulation system is identified. The working condition type is one of the following: normal working condition, over-temperature working condition, insufficient vacuum degree working condition, and abnormal slurry density working condition. The criteria for judging various operating conditions are as follows: Normal operating condition is when the temperature of the slurry after cooling, the vacuum degree of the flash cooler, and the solid content or density of the slurry are all within the preset reasonable threshold range; Over-temperature operating condition is when the temperature of the slurry after cooling exceeds the preset reasonable upper limit of temperature; Insufficient vacuum operating condition is when the vacuum degree of the flash cooler exceeds the preset reasonable upper limit of vacuum degree; Abnormal slurry density operating condition is when the solid content or density of the slurry exceeds the preset reasonable density range upper or lower limit.

[0010] As a further improvement of the present invention, the process of identifying the operating condition type of the current slurry circulation system includes the following steps: According to the preset sampling time window, the values ​​of three parameters—temperature of the cooled slurry, vacuum degree of the flash cooler, and solid density of the slurry—are collected in real time at the beginning and end of the window. For each parameter, the difference between the maximum and minimum values ​​within the window is calculated as the fluctuation amplitude of the parameter. At the same time, the difference between the end value and the beginning value of the window is divided by the sampling time window duration to obtain the average rate of change of the parameter. The fluctuation amplitude and rate of change of temperature, vacuum degree, and density are obtained respectively. For each parameter, its fluctuation amplitude is compared with the maximum allowable fluctuation amplitude obtained from the historical stable operation of the wet-process phosphoric acid low-temperature flash cooling section, and its rate of change is compared with the maximum allowable rate of change of the parameter, respectively, to obtain the dimensionless relative fluctuation ratio and relative change ratio. Based on the sensitivity of the parameter to the drastic changes in the reaction, fixed weights are assigned to the fluctuation amplitude and rate of change, and the weighted sum is used to obtain the single-parameter coupling value of the parameter. Then, the single-parameter coupling values ​​of the three parameters, temperature, vacuum degree, and density, are weighted and summed according to the actual influence weights of the three on the crystallization and extraction efficiency of the slurry in the wet-process phosphoric acid low-temperature flash cooling section, to calculate a comprehensive trend value of the degree of drastic change in the reaction. The trend value of the degree of drastic change in the reaction is used to characterize the stability of the current slurry reaction state. The system pre-stores reasonable threshold ranges for the slurry temperature, flash cooler vacuum, and slurry solids density under the requirements of the low-temperature flash cooling process, including upper and lower limits for temperature, vacuum, and density; and acquires the current temperature, vacuum, and density values ​​in real time.

[0011] As a further improvement of the present invention, according to the current working condition type, a set of target indicators to be optimized under the corresponding working condition is obtained by screening. The screening principle is to prioritize the core process parameters that exceed the threshold range as target indicators to be optimized. For example, under the over-temperature working condition, the slurry temperature after cooling and the vacuum degree of the flash cooler are prioritized as target indicators to be optimized. Under the abnormal density working condition, the slurry solids / density and the slurry temperature after cooling are prioritized as target indicators to be optimized. Boundary thresholds are set for the selected set of target indicators to be optimized, and dynamic weight coefficients are assigned to each indicator according to the working condition type. A multi-objective optimization function is constructed based on the indicator deviation value.

[0012] As a further improvement of the present invention, a particle swarm optimization algorithm is used to solve the multi-objective optimization function to obtain the optimal operating load control command for the axial flow pump and / or vacuum pump.

[0013] As a further improvement of the present invention, the process of obtaining the optimal operating load control command for axial flow pumps and / or vacuum pumps includes the following steps: The rotational speed or flow rate of the axial flow pump and the rotational speed or pumping capacity of the vacuum pump are used as decision variables to be optimized. The position vector of each particle corresponds to a set of operating load command combinations of the axial flow pump and the vacuum pump. The operating load command combination is input into the coupled response program of the wet-process phosphoric acid low-temperature flash cooling system, and the corresponding predicted values ​​of the slurry temperature, flash cooler vacuum degree and slurry solids density after cooling are obtained, thus obtaining the process parameter response set under the control command represented by each particle. The process parameter response set corresponding to each particle is substituted into the multi-objective optimization function to calculate the weighted deviation of each particle under the current control command as the fitness value. The individual optimal position of the particle and the global optimal position of the population are updated according to the fitness value to obtain the optimal axial flow pump and vacuum pump cooperative control command pair under the current iteration step. The position boundaries of particles are constrained based on the upper limit of temperature, upper limit of vacuum, and upper and lower limits of density in the low-temperature flash cooling section of wet-process phosphoric acid. Position vectors exceeding the boundaries are mapped back to the boundary values. The velocity and position update formula of the particle swarm algorithm is iteratively optimized. The process terminates when the global optimal fitness value no longer decreases after multiple iterations or reaches the maximum number of iterations. The final global optimal position is output as the optimal operating load control command for the axial flow pump and / or vacuum pump.

[0014] As a further improvement of the present invention, the process of mapping the position vector that exceeds the boundary back to the boundary value includes the following steps: Obtain the axial flow pump speed or flow rate value in the current particle position vector, input it into the coupled response model to obtain the corresponding predicted value of the slurry temperature after cooling. If the predicted value of the slurry temperature after cooling exceeds the upper limit of the temperature of the wet process phosphoric acid low-temperature flash cooling section, reduce the axial flow pump speed or flow rate value until the predicted temperature drops to within the upper limit of the temperature, and obtain the temperature-constrained axial flow pump control component. Based on the temperature-constrained axial flow pump control component, the vacuum pump speed or pumping volume value in the current particle position vector is obtained, and it is input into the coupled response model to obtain the corresponding flash cooler vacuum degree prediction value. If the flash cooler vacuum degree prediction value exceeds the vacuum degree upper limit, the vacuum pump speed or pumping volume value is increased until the vacuum degree prediction value drops to within the vacuum degree upper limit, thus obtaining the vacuum pump control component with vacuum degree constraint. Based on the temperature-constrained axial flow pump control component and the vacuum pump control component constrained by vacuum degree, the corresponding slurry solids density prediction value is obtained by inputting the coupled response program. If the slurry solids density prediction value exceeds the upper or lower limit of density, the control components of the axial flow pump and the vacuum pump are adjusted proportionally and synchronously until the density prediction value returns to the upper or lower limit range of density, thus obtaining the effective particle position vector after boundary constraints.

[0015] As a further improvement of the present invention, the rotational speed / flow rate of the axial flow pump and / or the rotational speed / pumping volume of the vacuum pump are adjusted according to the optimal operating load control command, so that the core process parameters of the slurry return to the preset threshold range, thereby achieving efficient slurry reaction.

[0016] To achieve the above objectives, the present invention also provides the following technical solution: A particle swarm optimization (PSO) algorithm-based low-temperature flash cooling parameter control system for wet-process phosphoric acid is provided, which is applied to the aforementioned PSO algorithm-based low-temperature flash cooling parameter control method for wet-process phosphoric acid. The PSO algorithm-based low-temperature flash cooling parameter control system for wet-process phosphoric acid includes: The parameter acquisition module is used to collect core process parameters of the slurry and all-dimensional operating parameters of the equipment; it integrates multiple sensors and acquisition instruments to achieve synchronous and real-time acquisition of process and equipment parameters. The operating condition identification module is used to calculate the fluctuation range and rate of change of temperature, vacuum degree and density based on the collected core process parameters of slurry. By coupling the three, it judges the trend of the degree of drastic change of slurry reaction and identifies the operating condition type of the current slurry circulation system based on the threshold of core process parameters. The target indicator filtering module is used to filter and obtain the set of target indicators to be optimized under the corresponding working condition based on the identified current working condition type. The objective function construction module is used to set boundary thresholds and dynamic weight coefficients for each indicator in the set of objective indicators to be optimized, and to construct a multi-objective optimization function. The optimization solution module is used to solve the multi-objective optimization function to obtain the optimal control commands for the axial flow pump and the vacuum pump. The execution adjustment module is used to adjust the operating load of the axial flow pump and / or vacuum pump according to the optimal control command, so as to achieve precise control of the parameters of the slurry circulation system.

[0017] To achieve the above objectives, the present invention also provides the following technical solution: An electronic device includes a processor and a memory coupled to the processor, the memory storing program instructions executable by the processor; when the processor executes the program instructions stored in the memory, it implements the particle swarm optimization algorithm-based wet phosphoric acid low-temperature flash cooling parameter control method described above.

[0018] To achieve the above objectives, the present invention also provides the following technical solution: A storage medium storing program instructions, which, when executed by a processor, implement the particle swarm optimization algorithm-based low-temperature flash cooling parameter control method for wet-process phosphoric acid as described above.

[0019] This invention achieves coordinated regulation of axial flow pumps and vacuum pumps through full-dimensional parameter acquisition, reaction trend coupling judgment, intelligent operating condition identification, and multi-objective optimization solution. This ensures that the slurry reaction is within a reasonable temperature range, improves the slurry crystallization quality and reaction efficiency, and guarantees the long-term stable operation of the wet-process phosphoric acid plant. Attached Figure Description

[0020] Figure 1This is a schematic flowchart of one embodiment of the wet-process phosphoric acid low-temperature flash cooling parameter control method based on particle swarm optimization algorithm of the present invention. Figure 2 This is a schematic diagram of the steps for identifying the operating condition type of the current slurry circulation system in an embodiment of the wet-process phosphoric acid low-temperature flash cooling parameter control method based on particle swarm optimization algorithm of the present invention. Figure 3 This is a schematic flowchart illustrating the steps of obtaining the optimal operating load control command for an axial flow pump and / or a vacuum pump in an embodiment of the wet-process phosphoric acid low-temperature flash cooling parameter control method based on particle swarm optimization algorithm of the present invention. Figure 4 This is a schematic flowchart illustrating the steps of adjusting the speed / flow rate of the axial flow pump and / or the speed / pumping capacity of the vacuum pump in an embodiment of the wet-process phosphoric acid low-temperature flash cooling parameter control method based on particle swarm optimization algorithm of the present invention. Figure 5 This is a functional module diagram of an embodiment of the wet-process phosphoric acid low-temperature flash cooling parameter control system based on particle swarm optimization algorithm of the present invention; Figure 6 This is a schematic diagram of the structure of an embodiment of the electronic device of the present invention; Figure 7 This is a schematic diagram of the structure of a storage medium according to an embodiment of the present invention; Figure 8 This is a schematic diagram of an existing closed slurry circulation system. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] The terms "first," "second," and "third" in this invention are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of those features. In the description of this invention, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this invention are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indication changes accordingly. Furthermore, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0023] References to embodiments herein mean that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0024] In this invention, based on the particle swarm optimization algorithm, other intelligent optimization algorithms such as genetic algorithms and simulated annealing algorithms can also be used; however, this invention primarily focuses on the particle swarm optimization algorithm. For genetic algorithms and simulated annealing algorithms, their principles can be directly applied by inputting the relevant parameters of this invention.

[0025] like Figure 1 As shown, this embodiment provides an example of a method for controlling the low-temperature flash cooling parameters of wet-process phosphoric acid based on particle swarm optimization (PSO) algorithm. In this embodiment, the method specifically includes the following steps: Step S1: Real-time acquisition of core process parameters of the slurry and full-dimensional operating parameters of the equipment. The core process parameters of the slurry are the slurry temperature after cooling, the vacuum degree of the flash cooler, the solids content / density of the slurry, the slurry inlet temperature, and the slurry inlet pressure. The full-dimensional operating parameters of the equipment are the axial flow pump flow rate / speed / current and the vacuum pump pumping volume / speed / load. The acquired parameters are transmitted to the working condition identification module in real time, and the data update frequency is adapted to the real-time control requirements of industrial production. Step S2: Calculate the fluctuation range and rate of change of temperature, vacuum degree, and density, and coupled them to determine the trend of drastic changes in the slurry reaction. At the same time, based on the preset process parameter threshold range, identify the current working condition type of the slurry circulation system. The working condition type is one of the following: normal working condition, over-temperature working condition, insufficient vacuum degree working condition, and abnormal slurry density working condition. The criteria for judging various operating conditions are as follows: Normal operating condition is when the temperature of the slurry after cooling, the vacuum degree of the flash cooler, and the solid content / density of the slurry are all within the preset reasonable threshold range; Over-temperature operating condition is when the temperature of the slurry after cooling exceeds the preset reasonable upper limit of temperature; Insufficient vacuum operating condition is when the vacuum degree of the flash cooler exceeds the preset reasonable upper limit of vacuum degree; Abnormal slurry density operating condition is when the solid content / density of the slurry exceeds the upper or lower limit of the preset reasonable density range. Step S3: Based on the current operating condition type, select the set of target indicators to be optimized under the corresponding operating condition. The selection principle is to prioritize core process parameters that exceed the threshold range as target indicators to be optimized. For example, under the over-temperature condition, prioritize the slurry temperature after cooling and the vacuum degree of the flash cooler as target indicators to be optimized. Under the abnormal density condition, prioritize the slurry solids / density and the slurry temperature after cooling as target indicators to be optimized. Step S4: Set boundary thresholds for the selected set of target indicators to be optimized, assign dynamic weight coefficients to each indicator according to the working condition type, and construct a multi-objective optimization function based on the indicator deviation value; The formula for constructing a multi-objective optimization function is: in, To optimize the function value for multiple objectives, The number of target indicators to be optimized For the first The dynamic weighting coefficients of each indicator, and , For the first The actual collected values ​​of each indicator For the first The deviation function of each indicator is defined as the relative deviation between the actual collected value of the indicator and the boundary threshold. The objective of multi-objective optimization is to make... Take the minimum value.

[0026] Step S5: Use an intelligent optimization algorithm to solve the multi-objective optimization function to obtain the optimal operating load control command for the axial flow pump and / or vacuum pump. The intelligent optimization algorithm can be a particle swarm optimization algorithm, a genetic algorithm, or a simulated annealing algorithm, all of which have the characteristics of fast solution speed and strong global optimization ability, and are suitable for industrial real-time control. Step S6: According to the optimal control command, adjust the speed / flow rate of the axial flow pump and / or the speed / pumping volume of the vacuum pump to bring the core process parameters of the slurry back to the preset threshold range, so as to achieve efficient slurry reaction; Different adjustment strategies are adopted for different operating conditions: when the over-temperature condition is identified, the circulation flow rate of the axial flow pump is increased and the pumping volume of the vacuum pump is increased to increase the flash cooling capacity and flash evaporation intensity of the slurry; when the vacuum condition is identified, the pumping volume of the vacuum pump is increased to improve the vacuum of the flash cooler, and then the circulation flow rate of the axial flow pump is adjusted according to the temperature change; when the density abnormal condition is identified, the circulation flow rate of the axial flow pump is adjusted as the main factor, the residence time of the slurry is changed, and the operating load of the vacuum pump is adjusted as an auxiliary factor to avoid the temperature fluctuation exacerbating the density abnormality.

[0027] Preferably, this embodiment achieves closed-loop precise control of the low-temperature flash cooling process parameters of wet-process phosphoric acid by combining multi-dimensional parameter coupling analysis with intelligent optimization algorithms. First, based on real-time collected core slurry process parameters and equipment operating parameters, the severity of the reaction is quantified using both fluctuation amplitude and rate of change indicators, combined with preset threshold ranges to accurately identify the operating condition type. The multi-parameter coupling judgment mechanism significantly improves the accuracy of operating condition classification, laying the foundation for subsequent optimization target selection. Second, a differentiated target indicator selection strategy is adopted for different operating condition types, prioritizing the inclusion of core process parameters deviating from the threshold into the optimization set, and setting boundary thresholds and dynamic weight coefficients. By constructing a multi-objective optimization function, the process control problem is transformed into a mathematical optimization problem, ensuring a high degree of matching between control commands and current operating condition requirements. Intelligent optimization algorithms such as particle swarm optimization are used to solve the multi-objective function, utilizing their rapid convergence characteristics to generate optimal equipment control commands. This algorithm considers multiple conflicting objectives during the solution process, achieving a balance between computational efficiency and solution quality, meeting the requirements of industrial real-time control. Finally, rapid parameter regression is achieved by executing differentiated equipment adjustment strategies. Under over-temperature conditions, the parameters of the axial flow pump and vacuum pump are adjusted in a coordinated manner to enhance the flash cooling effect; when the vacuum level is insufficient, the vacuum system is optimized first and then the circulation flow rate is adjusted; under abnormal density conditions, the slurry residence time is controlled as the main factor. This condition-specific control method significantly improves the targeting of parameter adjustment and effectively avoids the negative coupling effect between parameters.

[0028] In summary, this embodiment achieves coordinated optimization control of key parameters such as temperature, vacuum degree, and density during the low-temperature flash cooling process of wet-process phosphoric acid, thereby improving process stability and reaction efficiency. By continuously correcting the equipment operating status through a closed-loop feedback mechanism, it ensures that the system always operates within the optimal operating range, solving the problems of response lag and coarse adjustment in traditional control methods.

[0029] This embodiment achieves full-dimensional parameter acquisition, simultaneously collecting two types of parameters: first, process parameters such as slurry temperature, vacuum level, and density; second, equipment operating parameters such as flow rate, speed, and current / load of axial flow pumps and vacuum pumps. Compared to methods that only collect a single type of parameter, this acquisition strategy can more completely reflect the process and equipment status of the low-level flash cooling section, providing a comprehensive data foundation for control and avoiding control deviations caused by incomplete parameters. The coupled judgment of reaction trends, based on the fluctuation amplitude and rate of change of temperature, vacuum level, and density, performs multi-parameter coupled analysis to determine the changing trend of the intensity of the slurry reaction. Compared to trend judgment methods that rely solely on a single parameter, this method is more consistent with the actual reaction process of wet-process phosphoric acid slurry, enabling early prediction of process status changes and thus reducing control lag issues. Operating condition classification and differentiated control divide the operating conditions into four categories: normal, over-temperature, insufficient vacuum, and abnormal slurry density. For each type of operating condition, priority target indicators are selected for optimization, and corresponding control strategies are adopted. This overcomes the shortcomings of existing technologies that use fixed strategies to address all operating conditions, improving the targeting and control accuracy of the adjustment. Multi-objective collaborative optimization with dynamic weights constructs a multi-objective optimization function containing dynamic weight coefficients. Based on the currently identified operating condition type, different weights are assigned to indicators such as temperature, vacuum level, and density, ensuring that the control strategy prioritizes meeting the requirements of key process parameters. This function is solved through an intelligent optimization algorithm, achieving coordinated adjustment of the axial flow pump and vacuum pump, rather than independent adjustment of a single device, thus solving the problem of target imbalance under single-parameter adjustment. With its adaptability to existing equipment and low modification threshold, it can be directly applied to existing production equipment in the low-level flash cooling section of wet-process phosphoric acid, without requiring large-scale structural modifications to the extraction tank, axial flow pump, or low-level flash cooler. Only necessary sensors, data acquisition instruments, and control modules need to be added, resulting in low modification costs, convenient installation and commissioning, and good conditions for industrial promotion and application. Improved operational stability and production efficiency: Through the aforementioned control strategies, key process parameters of the slurry (temperature, vacuum, density, etc.) can be stably controlled within a preset reasonable range, ensuring that the reaction proceeds within a suitable temperature range. This effectively improves the slurry crystallization quality and phosphoric acid extraction efficiency. Simultaneously, it helps reduce internal scaling of equipment, lowers equipment failure rates, supports long-term stable operation of the wet-process phosphoric acid unit, and enhances enterprise production efficiency.

[0030] Furthermore, such as Figure 2 As shown, the process of identifying the current operating condition type of the slurry circulation system in step S2 specifically includes the following steps: Step S21: According to the preset sampling time window, collect the values ​​of three parameters in real time at the beginning and end of the window: slurry temperature, flash cooler vacuum degree, and slurry solids density. For each parameter, calculate the difference between the maximum and minimum values ​​within the window as the fluctuation amplitude of the parameter. At the same time, calculate the difference between the end value and the beginning value of the window and divide it by the sampling time window duration as the average rate of change of the parameter. The fluctuation amplitude and rate of change of temperature, vacuum degree, and density are obtained respectively. Step S22: For each parameter, its fluctuation amplitude is compared with the maximum allowable fluctuation amplitude obtained from the historical stable operation of the wet-process phosphoric acid low-temperature flash cooling section, and its rate of change is compared with the maximum allowable rate of change of the parameter, respectively, to obtain the dimensionless relative fluctuation ratio and relative change ratio; according to the sensitivity of the parameter to the drastic changes in the reaction, fixed weights are assigned to the fluctuation amplitude and rate of change, and the weighted sum is used to obtain the single-parameter coupling value of the parameter; then, the single-parameter coupling values ​​of the three parameters, temperature, vacuum degree, and density, are weighted and summed according to the actual influence weights of the three on the crystallization and extraction efficiency of the slurry in the wet-process phosphoric acid low-temperature flash cooling section (temperature fluctuation has the greatest influence, followed by vacuum degree, and then density), and finally a comprehensive trend value of the degree of drastic change in the reaction is calculated; the trend value of the degree of drastic change in the reaction is used to characterize the stability of the current slurry reaction state, and the larger the value, the more drastic and unstable the reaction; Step S23: Pre-store the reasonable threshold range of slurry temperature, flash cooler vacuum degree, and slurry solids density under the requirements of low-temperature flash cooling process, including upper temperature limit, upper vacuum degree limit (i.e., minimum allowable vacuum degree), upper density limit, and lower density limit; acquire the current temperature, vacuum degree, and density values ​​in real time; The system proceeds as follows: First, it checks if the current temperature exceeds the upper temperature limit; if so, it is identified as a high-temperature condition. If the temperature is normal, it checks if the current vacuum level exceeds the upper vacuum limit; if so, it is identified as a vacuum-insufficient condition. If the vacuum level is normal, it checks if the current density exceeds the upper or lower density limit; if so, it is identified as a density deviation condition. If all three are within a reasonable range, it is identified as a steady-state condition. Simultaneously, when the trend value of the drastic change in the reaction calculated in step two exceeds a pre-set trend warning threshold, even if the current process parameters have not yet exceeded a reasonable range, the system will mark the condition as a pre-abnormal state corresponding to the potential anomaly type. This is used to dynamically adjust the weight coefficients of the objective function during particle swarm optimization to achieve proactive and coordinated adjustment.

[0031] Preferably, this embodiment achieves real-time fluctuation feature extraction of temperature, vacuum, and density parameters by setting a dynamic sampling time window. The range within the calculation window is used as a fluctuation amplitude indicator, and the rate of change is calculated using the time-domain difference method, establishing a two-dimensional parameter dynamic evaluation system. The two indicators are dimensionless through relative fluctuation ratio and relative change ratio, eliminating comparability barriers between parameters of different dimensions. In the single-parameter coupling value calculation stage, fixed weights are assigned according to the influence mechanism of each parameter on crystallization and extraction efficiency, highlighting the dominant role of the temperature parameter. The weighting method accurately reflects the process characteristic that temperature sensitivity is stronger than vacuum and density sensitivity in the wet-process phosphoric acid flash cooling process. The multi-parameter coupling value is calculated through secondary weighting to obtain a comprehensive reaction intensity trend value, which can provide early warning of the deterioration trend of process parameters. The threshold judgment adopts a hierarchical progressive logic, sequentially detecting the exceeding of limits for temperature, vacuum, and density parameters. This sorting method is consistent with the fault propagation path of the wet-process phosphoric acid flash cooling process: abnormal temperature directly causes vacuum fluctuations, which in turn affect density stability. A specially designed pre-abnormal state identification mechanism triggers a prediction mode when the trend value of the severity of the reaction exceeds the warning threshold, enabling the control system to intervene and adjust in advance.

[0032] In summary, this embodiment significantly improves the timeliness and accuracy of operating condition identification through the synergistic effect of three technical aspects: parameter fluctuation feature quantification, multi-level weighted coupling calculation, and hierarchical threshold judgment. The identification results not only reflect the absolute numerical state of the current parameters, but also predict the development direction of potential faults through trend values, providing accurate operating condition input for subsequent multi-objective optimization of the particle swarm optimization algorithm.

[0033] Furthermore, the process of calculating a comprehensive trend value for the degree of dramatic change in the response in step S22 specifically includes the following steps: Step S221: Based on the process mechanism of the low-temperature flash cooling section of wet-process phosphoric acid, define the individualized weights for temperature, vacuum degree, and density respectively. The personalized weighting of temperature is determined based on the negative correlation between the width of the metastable region of calcium sulfate dihydrate crystals in the phosphoric acid slurry and the rate of temperature change, reflecting the dominant influence of temperature fluctuations on supersaturation and crystal size. The personalized weighting of vacuum is determined based on the slope of the correlation function between the gas-liquid equilibrium pressure in the flash cooler and the saturation temperature of the slurry, reflecting the direct impact of vacuum changes on flash cooling efficiency. The personalized weighting of density is determined based on the degree of influence of changes in the solid content of the slurry on circulation resistance, axial flow pump shaft power, and crystal residence time distribution. The three weight values ​​are pre-calibrated through on-site step response testing or orthogonal experiments and then fixed in the control system. Based on the process mechanism of the low-temperature flash cooling section of wet-process phosphoric acid, the individualized weights of temperature, vacuum degree, and density are quantitatively calibrated according to their influence on crystallization efficiency, flash evaporation efficiency, and circulation resistance, respectively. The calculation formulas and related explanations for the three weights are as follows: The formula for calculating the personalized weight of temperature is as follows: In the formula, Personalized weights representing temperature; Indicates the supersaturation degree of calcium sulfate dihydrate in phosphoric acid slurry (kg / m³). 3 ); This indicates the temperature of the slurry after cooling (°C). Indicates temperature The width of the metastable region (°C) is defined as the temperature range corresponding to the critical supersaturation of homogeneous nucleation. This indicates the gas-liquid equilibrium pressure (kPa) inside the flash cooler. This indicates the vacuum level of the flash cooler (kPa, absolute pressure). This represents the pressure difference (kPa) between the inlet and outlet of the axial flow pump in the slurry circulation loop. Indicates the solids content of the slurry (kg / m³) 3 The formula uses the partial derivative of supersaturation with respect to temperature. Reflecting the direct effect of temperature changes on the crystallization driving force, and using the reciprocal of the width of the metastable region. The risk of runaway crystallization due to temperature fluctuations is significant; the narrower the metastable region, the more easily temperature disturbances trigger explosive nucleation. The larger the product of these two factors, the stronger the dominant influence of temperature on crystallization quality. The higher the value, the better.

[0034] Personalized weighting formula for vacuum degree: In the formula, Personalized weights representing vacuum levels; This indicates the gas-liquid equilibrium pressure (kPa) inside the flash cooler, which corresponds one-to-one with the saturation temperature of the slurry. This indicates the vacuum level (kPa, absolute pressure) of the flash cooler; changes in vacuum level directly affect the gas-liquid equilibrium pressure. This, in turn, changes the flash intensity; The sensitivity of the pressure inside the flash cooler to the vacuum level is characterized by a linear relationship with a slope of 1. The larger the partial derivative value, the more significant the direct impact of vacuum level fluctuations on flash cooling efficiency. The weighting ensures that the control strategy prioritizes adjusting the vacuum pump when the vacuum level is insufficient.

[0035] The formula for calculating the personalized weight of density is as follows: In the formula, Individualized weights representing density; This represents the pressure difference (kPa) between the inlet and outlet of the axial flow pump in the slurry circulation loop. Indicates the solids content of the slurry (kg / m³) 3 Increased density leads to increased slurry viscosity and increased circulation resistance, manifested as follows: It is positive and increases with increasing density; the partial derivative quantifies the influence of density change on the power consumption and circulation flow rate of the axial flow pump, and indirectly affects the residence time distribution of crystals in the extraction tank; since density is a slowly varying parameter, its contribution to the intensity of the instantaneous reaction is minimal, therefore The lowest value among the three.

[0036] The denominators of the three formulas above are all the sum of the three sensitivity terms, ensuring that... ; values ​​of each partial derivative (e.g. ) and the width of the metastable region All of these can be obtained through on-site step response testing or orthogonal experiments in a wet-process phosphoric acid plant. The specific method is as follows: standard step disturbances are applied to temperature, vacuum, and density respectively; changes in supersaturation, flash cooler pressure, and pump differential pressure are recorded; the static gains of each are calculated; and then these are substituted into formulas to calculate the three weights. The calculation results are embedded in the control system and do not require online updates. Each formula is directly related to the temperature-metastable region relationship, vacuum-gas-liquid balance relationship, and density-circulation resistance relationship described in the claims. Step S222: Multiply the single-parameter coupling value of temperature by the personalized weight of temperature to obtain the weighted contribution value of temperature; then multiply the single-parameter coupling value of vacuum degree by the personalized weight of vacuum degree to obtain the weighted contribution value of vacuum degree; and add the weighted contribution value of temperature and the weighted contribution value of vacuum degree to obtain the weighted cumulative value of temperature-vacuum degree. Step S223: Multiply the single-parameter coupling value of density by the personalized weight of density to obtain the weighted contribution value of density, and add this value to the obtained weighted cumulative value of temperature-vacuum degree to obtain the comprehensive trend value of the degree of drastic change in the reaction.

[0037] Preferably, this embodiment establishes a quantitative evaluation system for the coupled effects of multiple parameters. Through personalized weighting of three core parameters—temperature, vacuum, and density—it achieves differentiated quantitative characterization of the impact of different process parameters on the intensity of the reaction. Temperature weighting is related to the metastable characteristics of calcium sulfate dihydrate crystallization; vacuum weighting is related to the pressure sensitivity of flash evaporation efficiency; and density weighting is related to the slurry rheological properties and equipment operating load. These three factors together constitute a complete evaluation dimension reflecting the instability of the flash cooling process. This improves the sensitivity and accuracy of process status monitoring. The weighted accumulation algorithm converts the absolute value fluctuation of a single parameter into its contribution to the overall reaction trend, overcoming the lag of traditional threshold alarms. The coupled superposition of temperature and vacuum can identify the risk of flash equilibrium disruption early, and the introduction of density weighting further captures secondary disturbances such as crystallizer scaling or pump efficiency reduction caused by abnormal solids content. It provides feedforward compensation for closed-loop control. The comprehensive trend value, as a feedforward variable, can be used to adjust the opening of the flash cooler vacuum valve, the axial flow pump speed, and the heat exchange medium flow rate in a linked manner. When the trend value exceeds the set threshold, the control system prioritizes adjusting parameters with high weighting, such as vacuum level, to distribute control actions according to the proportion of disturbance contribution, maintaining the supersaturation of calcium sulfate dihydrate crystals within the metastable region. Optimizing the dynamic matching relationship of process parameters, through weighting coefficients calibrated by step response, enables the system to automatically adapt to process characteristic deviations caused by different phosphoric acid feedstock grades (such as fluctuations in P2O5 content) or changes in solid content, maintaining a dynamic match between flash cooling intensity and crystallization kinetics requirements. This directly reflects in reducing the amplitude of fluctuations in the metastable region width of the phosphoric acid slurry, and actual measurements show a reduction in particle size distribution dispersion.

[0038] Furthermore, such as Figure 3 As shown, the process of obtaining the optimal operating load control command for the axial flow pump and / or vacuum pump in step S5 specifically includes the following steps: Step S51: The rotational speed or flow rate of the axial flow pump and the rotational speed or pumping volume of the vacuum pump are used as decision variables to be optimized. The position vector of each particle corresponds to a set of operating load command combinations of the axial flow pump and the vacuum pump. The operating load command combination is input into the coupled response program of the wet-process phosphoric acid low-temperature flash cooling system, and the corresponding predicted values ​​of the slurry temperature, flash cooler vacuum degree and slurry solid density are obtained, thus obtaining the process parameter response set under the control command represented by each particle. Step S52: Substitute the process parameter response set corresponding to each particle into the multi-objective optimization function, calculate the weighted deviation of each particle under the current control command as the fitness value, and update the individual optimal position of the particle and the global optimal position of the population according to the fitness value to obtain the optimal axial flow pump and vacuum pump cooperative control command pair under the current iteration step. The optimal position for an individual is the combination of historical control commands that minimizes the sum of weighted biases; the optimal position for the population is the combination of control commands that minimizes the sum of weighted biases among all particles. Step S53: Constrain the position boundaries of particles based on the upper limit of temperature, upper limit of vacuum, and upper and lower limits of density of the low-temperature flash cooling section of wet-process phosphoric acid, and map the position vectors that exceed the boundaries back to the boundary values; iterate and optimize according to the velocity and position update formula of the particle swarm algorithm, and terminate when the global optimal fitness value no longer decreases after multiple iterations or reaches the maximum number of iterations, and output the final global optimal position as the optimal operating load control command for the axial flow pump and / or vacuum pump.

[0039] In the wet-process phosphoric acid low-temperature flash cooling parameter control method, step S53, iterative optimization according to the velocity and position update formula of the particle swarm algorithm, means that in each iteration, each particle in the population is updated dimension by dimension according to the velocity update formula and the position update formula. The speed update formula is: The new velocity of the current particle = inertia weight × current velocity of the current particle + individual learning factor × random number × (individual optimal position of the current particle - current position of the current particle) + social learning factor × random number × (global optimal position - current position of the current particle); Among them, the inertia weight is used to control the degree to which the particle maintains its original motion trend; the individual learning factor and the social learning factor are used to adjust the intensity of the particle's learning towards its own historical best position and the global best position of the population, respectively; the random number is a uniformly distributed random number between 0 and 1, used to increase the randomness of the search.

[0040] The position update formula is: The current particle's new position = the current particle's current position + the current particle's new velocity The above update process is performed independently for each dimension of each particle, namely, the decision variables such as axial flow pump speed or flow rate, vacuum pump speed or pumping volume; The specific execution method of iterative optimization is as follows: In each iteration, firstly, based on the current particle's velocity and position, calculate the new velocity using the velocity update formula mentioned above, and then calculate the new position using the position update formula; subsequently, input the combined axial flow pump and vacuum pump operating load commands at the new position into the coupled response program of the wet-process phosphoric acid low-level flash cooling system, and map the corresponding predicted values ​​of the cooled slurry temperature, flash cooler vacuum degree, and slurry solids density, and substitute them into the multi-objective optimization function to calculate the fitness value; based on the calculated fitness value, update the individual optimal position of the particle and the global optimal position of the population; then, substitute the updated individual optimal position and global optimal position into the velocity and position update formulas of the next iteration, and repeat the above process until the termination condition of step S53 is met. In the above velocity update formula and position update formula, the parameters, inertia weight, individual learning factor, and social learning factor are all preset fixed values ​​or dynamically adjusted values ​​according to preset rules during the iteration process. Through the above-mentioned explicit velocity update formula, position update formula, and their iterative execution method, the particle swarm optimization algorithm can be applied to the low-level flash cooling parameter control of wet phosphoric acid to solve the optimal operating load control command for axial flow pumps and vacuum pumps.

[0041] Preferably, in this embodiment, the rotational speed or flow rate of the axial flow pump and the rotational speed or pumping capacity of the vacuum pump are used as decision variables. The optimal solution is searched in the solution space using a particle swarm optimization algorithm. The position vector of each particle represents a combination of pump operating loads. After being input into the coupled response program of the wet-process phosphoric acid low-temperature flash cooling system, the predicted values ​​of the cooled slurry temperature, flash cooler vacuum degree, and slurry solids density are output, forming a process parameter response set. The weighted deviation sum under each particle control command is calculated using a multi-objective optimization function and used as the fitness value to evaluate the quality of the solution. Based on this, the individual optimal position and the global optimal position are updated. The individual optimal position reflects the historical optimal control command combination, while the global optimal position is the best solution in the current population. Through continuous iterative optimization, the optimal cooperative control command pair of the axial flow pump and the vacuum pump is finally obtained. To ensure the feasibility of the solution, the particle position boundary is limited by process constraints, including upper temperature limit, upper vacuum degree limit, and upper and lower density limits. Solutions exceeding the boundaries will be corrected to a reasonable range. The velocity and position update formulas of the particle swarm optimization algorithm are continuously optimized within the constraints until the global optimal fitness value converges or the maximum number of iterations is reached.

[0042] In summary, this embodiment can effectively coordinate the operating load of the axial flow pump and the vacuum pump, enabling the wet-process phosphoric acid low-level flash cooling system to optimize the comprehensive control performance of the slurry temperature after cooling, the flash cooler vacuum degree, and the slurry solids density while meeting process parameter constraints.

[0043] Furthermore, the process of mapping the position vector that exceeds the boundary back to the boundary value in step S53 specifically includes the following steps: Step S531: Obtain the axial flow pump speed or flow rate value in the current particle's position vector, input it into the coupled response model to obtain the corresponding predicted value of the slurry temperature after cooling. If the predicted value of the slurry temperature after cooling exceeds the upper limit of the temperature of the wet-process phosphoric acid low-temperature flash cooling section, reduce the axial flow pump speed or flow rate value until the predicted temperature value drops to within the upper limit of the temperature, and obtain the temperature-constrained axial flow pump control component. Step S532: Based on the axial flow pump control component after temperature constraint, obtain the vacuum pump speed or pumping volume value in the current particle position vector, input it into the coupled response model to obtain the corresponding flash cooler vacuum degree prediction value. If the flash cooler vacuum degree prediction value exceeds the upper limit of vacuum degree, increase the vacuum pump speed or pumping volume value until the vacuum degree prediction value drops to within the upper limit of vacuum degree, and obtain the vacuum pump control component after vacuum degree constraint. Step S533: Based on the axial flow pump control component constrained by temperature and the vacuum pump control component constrained by vacuum degree, input the coupled response program to obtain the corresponding slurry solids density prediction value. If the slurry solids density prediction value exceeds the upper or lower limit of density, then adjust the control components of the axial flow pump and the vacuum pump proportionally until the density prediction value returns to the upper or lower limit range of density, and obtain the effective particle position vector after boundary constraint.

[0044] Preferably, the boundary constraint mapping process described above in this embodiment achieves the feasible domain limitation of the particle position vector through cascaded constraint correction; parameter constraint sequence optimization, based on the coupling relationship of process parameters of temperature-vacuum degree-density, adopts a hierarchical constraint mechanism, first performs temperature boundary correction on the axial flow pump speed / flow rate, then performs vacuum degree boundary correction on the vacuum pump speed / pumping volume, and finally achieves density constraint through joint adjustment; ensuring that key process parameters prioritize meeting boundary conditions and avoiding parameter adjustment conflicts. Coupled response feedback control, using the dynamic coupled response program of the wet phosphoric acid flash cooling section, maps the equipment control quantities (axial flow pump speed, vacuum pumping volume) to the predicted values ​​of process parameters (temperature, vacuum degree, density); through iterative correction, the predicted values ​​of each parameter converge to the allowable range of the process, and the correction step size is adaptively adjusted according to the degree of parameter deviation to ensure correction efficiency and stability. Boundary collaborative correction, when the density exceeds the threshold, adopts a synchronous proportional adjustment strategy of the axial flow pump and vacuum pump control components to maintain the density return to the allowable range under temperature and vacuum degree constraints; avoids parameter oscillation caused by over-adjustment of a single device and maintains the overall dynamic balance of the system. The feasible solution generation guarantee ensures that the final output control command simultaneously satisfies the boundary conditions of the three core process parameters of temperature, vacuum degree, and density by gradually correcting each component of the particle position vector. This ensures that the optimization algorithm always searches within the feasible solution space, improving the convergence speed and the feasibility of the optimal solution.

[0045] In summary, this embodiment achieves dynamic matching between equipment control quantities and process parameters, ensuring that the instructions generated by the optimization solution module meet the actual production constraints, while maintaining the stability and controllability of the process system.

[0046] Furthermore, such as Figure 4 As shown, the process of adjusting the speed / flow rate of the axial flow pump and / or the speed / pumping capacity of the vacuum pump in step S6 specifically includes the following steps: Step S61: Input the axial flow pump speed or flow rate value in the optimal operating load control command into the frequency converter of the axial flow pump. The frequency converter adjusts the actual speed or circulation flow rate of the axial flow pump to obtain the adjusted slurry circulation flow rate. Step S62: The vacuum pump speed or pumping volume value in the optimal operating load control command is input into the frequency converter of the vacuum pump. The frequency converter adjusts the actual speed or pumping volume of the vacuum pump to obtain the adjusted vacuum degree of the flash cooler. Step S63: The adjusted slurry circulation flow rate and flash cooler vacuum work together to cool the low-level flash cooling system, so that the slurry temperature, flash cooler vacuum, and slurry solids density return to the preset threshold range after cooling, thereby achieving efficient reaction of wet-process phosphoric acid slurry.

[0047] Preferably, in this embodiment, the execution adjustment process optimizes process parameters through precise control of the equipment load driven by frequency converters. The rapid response of the frequency converters is achieved by the frequency converters of the axial flow pump and vacuum pump receiving the optimal control commands generated by the ion swarm optimization algorithm and outputting variable frequency power through PWM modulation, enabling stepless adjustment of the motor speed within the range of 15-50Hz with a response time ≤200ms. This ensures synchronization between the equipment load adjustment and the output commands of the optimization solution module. Coupled control of process parameters is achieved by adjusting the axial flow pump flow rate, which directly changes the heat and mass transfer rate of the slurry circulation system, affecting the residence time and cooling efficiency of the slurry in the flash cooler. Adjustment of the vacuum pump's pumping volume changes the system's absolute pressure level. The two work synergistically to jointly regulate the three key parameters—the slurry temperature after cooling, the flash cooler vacuum, and the slurry density—through a gas-liquid balance relationship. Closed-loop stability is ensured by feeding back the actual equipment operating parameters (speed, flow rate, pumping volume) after frequency conversion adjustment to the parameter acquisition module via sensors, forming a closed-loop control chain of ion swarm optimization-equipment execution-parameter feedback. This ensures the system continuously maintains itself near the optimal operating point, suppressing parameter fluctuations caused by external disturbances. Optimal energy efficiency is achieved by using the Pareto optimal solution obtained through the ion swarm optimization algorithm. The frequency converter adjusts the system with the least energy consumption, such as prioritizing axial flow pump speed regulation over valve throttling, to ensure that the target process parameters are met and reduce ineffective power consumption. Through dynamic matching of equipment operating efficiency and process requirements, the consumption of phosphoric acid vapor per ton is reduced and power consumption is optimized.

[0048] In summary, the execution phase of this embodiment transforms the theoretical optimal solution output by the optimization algorithm into actual equipment actions, ensuring multi-parameter coordinated and stable control of the wet-process phosphoric acid low-temperature flash cooling system, while simultaneously meeting production process constraints and energy efficiency targets.

[0049] like Figure 5 As shown, this embodiment also provides an embodiment of a wet phosphoric acid low-temperature flash cooling parameter control system based on particle swarm optimization algorithm. In this embodiment, the wet phosphoric acid low-temperature flash cooling parameter control system based on particle swarm optimization algorithm is applied to the wet phosphoric acid low-temperature flash cooling parameter control method based on particle swarm optimization algorithm as described in the above embodiment. The wet phosphoric acid low-temperature flash cooling parameter control system based on particle swarm optimization algorithm includes a parameter acquisition module 1, a working condition identification module 2, a target index screening module 3, a target function construction module 4, an optimization solution module 5, and an execution adjustment module 6 that is communicatively connected to the optimization solution module 5. The parameter acquisition module 1 is used to collect core process parameters of the slurry and full-dimensional operating parameters of the equipment. The core process parameters of the slurry include the slurry temperature after cooling by the low-level flash cooler, the vacuum degree of the flash cooler, the solids content / density of the slurry, the slurry inlet temperature, and the slurry inlet pressure. The full-dimensional operating parameters of the equipment include the flow rate, speed, and current of the axial flow pump, as well as the pumping volume, speed, and load of the vacuum pump. It integrates multiple sensors and acquisition instruments to realize the synchronous and real-time acquisition of process and equipment parameters, providing a comprehensive and accurate data foundation for control. The parameter acquisition module 1 specifically includes a temperature sensor, a vacuum transmitter, a densitometer, a pressure sensor, a flow sensor, a speed sensor, a current sensor, and a load acquisition instrument. Specifically, the first temperature sensor is installed in the discharge pipe or lower slurry zone of the low-level flash cooler to detect the temperature of the cooled slurry; the second temperature sensor is installed in the slurry inlet pipe of the low-level flash cooler to detect the slurry inlet temperature; the vacuum transmitter is installed in the top gas phase space or gas phase pipe of the low-level flash cooler to detect the vacuum level of the flash cooler; the densitometer is installed in the return pipe from the low-level flash cooler to the extraction tank or inside the extraction tank to detect the solids content / density of the slurry; the pressure sensor is installed in the slurry inlet pipe of the low-level flash cooler to detect the slurry inlet pressure; the flow, speed, and current sensors are connected to the axial flow pump to detect the flow rate, speed, and current of the axial flow pump, respectively; the flow, speed, and load acquisition instrument are connected to the vacuum pump to detect the pumping volume, speed, and load of the vacuum pump, respectively. The operating condition identification module 2 calculates the fluctuation amplitude and rate of change of temperature, vacuum degree, and density based on the collected core process parameters of the slurry. By coupling these three parameters, it determines the trend of drastic changes in the slurry reaction and identifies the current operating condition type of the slurry circulation system based on the core process parameter thresholds. This module has a preset calculation program that automatically calculates the fluctuation amplitude and rate of change of temperature, vacuum degree, and density per unit time. When the fluctuation amplitude or rate of change of any parameter exceeds the preset threshold, the drastic change in the reaction is determined to be drastic; when the fluctuation amplitude and rate of change of all parameters are within the preset thresholds, the drastic change in the reaction is determined to be stable. The operating condition types are divided into four categories: normal operating condition, over-temperature operating condition, insufficient vacuum condition, and abnormal slurry density condition. Each type of operating condition has a corresponding threshold range for the core process parameters of the slurry. The target index screening module 3 filters the set of target indices to be optimized under the identified current operating condition type. The screening principle is to prioritize core process parameters exceeding the threshold range as target indices to be optimized, making the control strategy more targeted and avoiding ineffective adjustments. The objective function construction module 4 is used to set boundary thresholds and dynamic weight coefficients for each indicator in the set of objective indicators to be optimized, and to construct a multi-objective optimization function. The dynamic weight coefficients are allocated according to the control importance of each objective indicator under the corresponding operating conditions. The weight coefficients of the core control indicators are higher than those of the auxiliary control indicators, so as to ensure that the control strategy is tilted towards the key process parameters.The optimization solution module 5 is used to solve the multi-objective optimization function to obtain the optimal control commands for the axial flow pump and the vacuum pump. It can employ intelligent optimization algorithms such as particle swarm optimization, genetic algorithm, and simulated annealing algorithm, resulting in fast solution speed and accurate results, adapting to the real-time control requirements of industrial production. The execution adjustment module 6 is used to adjust the operating load of the axial flow pump and / or vacuum pump according to the optimal control commands, achieving precise parameter control of the slurry circulation system. This module uses actuators such as frequency converters and regulating valves to adjust the speed / flow rate of the axial flow pump and the speed / pumping capacity of the vacuum pump, achieving stepless adjustment of the equipment load.

[0050] Preferably, the wet-process phosphoric acid low-level flash cooling parameter control system based on the ion swarm optimization algorithm in this embodiment achieves closed-loop optimization control of process parameters through a modular technical architecture. Full-parameter coupled sensing: a parameter acquisition network composed of sensing units such as temperature sensors, vacuum transmitters, and densitometers enables synchronous monitoring of multiple physical fields—temperature field, pressure field, and density field—of the flash cooler slurry, providing 14 types of real-time dynamic parameter data for operating condition identification. Dynamic operating condition classification: based on the sliding window algorithm, the root mean square deviation and derivative rate of change of process parameters are calculated, and the system operating state is divided into four standard operating condition modes through three-dimensional parameter space threshold judgment. Multi-objective optimization control: differentiated objective functions are established for different operating condition types, and a dynamic weight allocation strategy is adopted to search for the optimal control quantity in the solution space using the ion swarm optimization algorithm. Equipment coordinated adjustment: the actuator adopts an incremental PID control algorithm, and the axial flow pump speed and vacuum pump pumping volume are adjusted through the frequency converter to achieve matching optimization of process parameters and equipment load; through the coordinated optimization of process parameters and equipment operating parameters, stable and efficient operation of the low-level flash cooling process in wet-process phosphoric acid production is achieved.

[0051] like Figure 6 As shown, this embodiment provides an embodiment of an electronic device 7, which includes a processor 71 and a memory 72 coupled to the processor 71.

[0052] The memory 72 stores program instructions for implementing the wet-process phosphoric acid low-temperature flash cooling parameter control method based on particle swarm optimization algorithm in any of the above embodiments.

[0053] The processor 71 is used to execute program instructions stored in the memory 72 to perform low-temperature flash cooling parameter control of wet phosphoric acid based on particle swarm optimization algorithm.

[0054] The processor 71 can also be referred to as a CPU (Central Processing Unit). The processor 71 may be an integrated circuit chip with signal processing capabilities. The processor 71 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor.

[0055] Furthermore, Figure 7 This is a schematic diagram of the structure of a storage medium according to an embodiment of this application. The storage medium 8 in this embodiment stores program instructions 81 capable of implementing all the methods described above. These program instructions 81 can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0056] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0057] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

[0058] The multi-parameter optimization control system for the low-level flash cooling section of wet-process phosphoric acid production, as described in this invention, is applied to the low-level flash cooling section of a wet-process phosphoric acid production unit. The slurry circulation system of this section consists of an extraction tank, an axial flow pump, and a low-level flash cooler. The preset reasonable threshold values ​​for the process parameters are: slurry temperature after cooling 76-79℃, flash cooler vacuum degree ≤40kPa, and slurry solids content / density 1.25-1.30g / cm³. 3 .

[0059] I. Hardware Configuration of the Control System The control system in this embodiment includes: Parameter acquisition module: The first temperature sensor (PT100 corrosion-resistant type) is installed on the discharge pipe of the low-level flash cooler; the second temperature sensor (PT100 corrosion-resistant type) is installed on the slurry inlet pipe; the vacuum transmitter (absolute pressure type) is installed on the top vapor phase pipe of the low-level flash cooler; the radioactivity density meter is installed on the reflux pipe from the flash cooler to the extraction tank; the pressure sensor is installed on the slurry inlet pipe; the flow sensor, speed sensor, and current sensor are connected to the axial flow pump; the flow sensor, speed sensor, and load acquisition instrument are connected to the vacuum pump; all sensors and acquisition instruments communicate with the industrial control computer via a 485 bus, and the data acquisition frequency is 1 time / second.

[0060] Operating condition identification module: Based on a preset calculation program on the industrial control computer, it automatically calculates the fluctuation amplitude and rate of change of temperature, vacuum degree, and density per minute. Preset fluctuation amplitude thresholds: temperature ±1℃, vacuum degree ±2kPa, density ±0.02g / cm³; preset rate of change thresholds: temperature 0.3℃ / min, vacuum degree 1kPa / min, density 0.01g / cm³. 3 / min; Preset operating condition judgment thresholds: over-temperature condition (T>79℃), insufficient vacuum condition (P>40kPa), abnormal density condition (ρ<1.25g / cm³ or ρ>1.30g / cm³). 3 ).

[0061] Target indicator screening module: Integrated with the working condition identification module in the industrial control computer, it automatically screens target indicators to be optimized based on the working condition type.

[0062] Objective function construction module: integrated with industrial control computer, it can automatically allocate dynamic weight coefficients according to the working condition type.

[0063] Optimization and solution module: The Particle Swarm Optimization algorithm is used to find the optimal solution based on the industrial control computer. The number of algorithm iterations is set to 50 and the convergence accuracy is set to 0.001.

[0064] The control module includes a frequency converter for the axial flow pump and a frequency converter for the vacuum pump, which are connected to the motors of the axial flow pump and the vacuum pump, respectively. It can receive control commands from the industrial control computer to achieve stepless speed regulation.

[0065] II. Control methods under over-temperature conditions The control system is used to optimize the control of the over-temperature condition. The specific steps are as follows: Step 1, Parameter Acquisition: The parameter acquisition module collects data in real time: slurry temperature after cooling T=82℃, flash cooler vacuum P=42kPa, slurry solids content / density ρ=1.28g / cm³. 3 Slurry inlet temperature Tin = 85℃; slurry inlet pressure 0.3MPa; axial flow pump flow rate Q1 = 10000 m2 3 / h, rotational speed n1=980r / min, current I=180A; vacuum pump pumping capacity Q2=500m 3 / min, rotational speed n2=1450r / min, load=80%.

[0066] Step 2, Trend Judgment and Operating Condition Identification: Calculations show that the temperature fluctuation amplitude is 3℃ and the rate of change is 0.5℃ / min, while the vacuum fluctuation amplitude is 2kPa and the rate of change is 0.8kPa / min. Both exceed the preset thresholds, indicating a drastic change in the reaction. Simultaneously, based on T=82℃>79℃, P=42kPa>40kPa, and ρ=1.28g / cm³,... 3 Within a reasonable range, the current operating condition is identified as an over-temperature condition.

[0067] Step 3: Optimize target selection: Based on the over-temperature condition, select the set of target indicators to be optimized as {slurry temperature T after cooling, flash cooler vacuum degree P}.

[0068] Step 4: Constructing the multi-objective function: Set the boundary threshold of T to ≤79℃ and the boundary threshold of P to ≤40kPa; allocate dynamic weight coefficients according to the over-temperature condition, with the weight coefficient of T ω1=0.7 and the weight coefficient of P ω2=0.3, and ω1+ω2=1; construct the deviation function: f1(X1)=(82-T) / 3, f2(X2)=(42-P) / 2; the multi-objective optimization function is: F=0.7×(82-T) / 3+0.3×(42-P) / 2, and the optimization objective is to minimize F.

[0069] Step 5, Optimal Solution: The above function is solved using the particle swarm optimization algorithm to obtain the optimal control command: the axial flow pump speed is adjusted to 1050 r / min, and the vacuum pump speed is adjusted to 1550 r / min.

[0070] Step 6, Load Adjustment: The adjustment module uses the frequency converter to adjust the axial flow pump speed to 1050 r / min (increasing the flow rate to 11000 m³ / min). 3 / h), adjust the vacuum pump speed to 1550 r / min (increase the pumping volume to 550 m³ / min). 3 / min); after adjustment, 3 minutes later, the parameter acquisition module collected data: T=78℃, P=38kPa, both of which returned to the preset reasonable threshold range, and the degree of drastic change was judged to be stable, thus achieving precise control of the over-temperature condition.

[0071] III. Control methods under abnormal density conditions The control system is used to optimize the control of abnormal density conditions (high density). The specific steps are as follows: Step 1, Parameter Acquisition: The parameter acquisition module collects data in real time: slurry temperature after cooling T=78℃, flash cooler vacuum P=39kPa, slurry solids content / density ρ=1.32g / cm³. 3 Slurry inlet temperature Tin = 80℃, slurry inlet pressure 0.28MPa; axial flow pump flow rate Q1 = 9500 m³ / h, speed n1 = 950 r / min, current I = 175 A; vacuum pump pumping capacity Q2 = 480 m2 3 / min, rotational speed n2=1420r / min, load=78%.

[0072] Step 2, Trend Judgment and Operating Condition Identification: The calculated density fluctuation range is 0.02 g / cm³. 3 The rate of change is 0.015 g / cm³ / min, which exceeds the preset threshold, and the degree of drastic change in the reaction is judged to be drastic; at the same time, according to ρ=1.32 g / cm³>1.30 g / cm³, T and P are both within the reasonable range, and the current working condition is identified as an abnormal density working condition.

[0073] Step 3: Optimization target selection: Based on the abnormal density conditions, the set of target indicators to be optimized is {solid content / density ρ of slurry, temperature T of slurry after cooling}.

[0074] Step 4: Constructing the multi-objective function: Set the boundary threshold for ρ to ≤ 1.30 g / cm³. 3 The boundary threshold of T is 76~79℃; dynamic weight coefficients are assigned, with the weight coefficient of ρ being ω1=0.8 and the weight coefficient of T being ω2=0.2; the deviation functions are constructed as follows: f1(X1)=(1.32-ρ) / 0.02, f2(X2)=(78-T) / 1; the multi-objective optimization function is: F=0.8×(1.32-ρ) / 0.02+0.2×(78-T) / 1, and the optimization objective is to minimize F.

[0075] Step 5, Optimal Solution: The optimal control command is obtained by using the particle swarm optimization algorithm: the axial flow pump speed is adjusted to 1000 r / min, and the vacuum pump speed is kept constant at 1420 r / min.

[0076] Step 6, Load Adjustment: Execute the adjustment module to adjust the axial flow pump speed to 1000 r / min (increase the flow rate to 10500 m³ / min).3 / h), the vacuum pump speed remained constant; 5 minutes after adjustment, the parameter acquisition module collected ρ=1.29g / cm 3 Both T=77.5℃ returned to the preset reasonable threshold range, achieving precise control of abnormal density conditions.

[0077] Combined with appendix Figure 8 The existing closed-loop slurry circulation system is described, comprising an extraction tank 9, an axial flow pump 10, a low-level flash cooler 11, and a vacuum pump 12. The extraction tank 9 is connected to the axial flow pump 10 by a horizontal pipe, and the outlet of the extraction tank 9 is connected to the inlet of the axial flow pump 10 to achieve slurry transport. The axial flow pump 10 is connected to the low-level flash cooler 11 by a horizontal pipe, and the outlet of the axial flow pump 10 is connected to the inlet of the low-level flash cooler 11 to send the slurry into the low-level flash cooler 11. The low-level flash cooler 11 is connected to the top and side of the extraction tank 9 by pipes, and the cooled slurry flows back to the extraction tank 9 to form a closed loop. The low-level flash cooler 11 is connected to the vacuum pump 12 by a pipe, and the vacuum pump 12 is connected to the vacuum interface of the low-level flash cooler 11 to remove the steam generated by flash evaporation and maintain a negative pressure environment inside the cooler. Axial flow pump 10 pumps the hot phosphoric acid slurry in extraction tank 9 into low-level flash cooler 11; vacuum pump 12 continuously removes gas from low-level flash cooler 11 to create a negative pressure environment; after the slurry enters, some water is rapidly vaporized under negative pressure, carrying away a large amount of heat and reducing the slurry temperature; the cooled slurry flows back to extraction tank 9 through pipelines to maintain the process temperature in extraction tank 9 and achieve continuous circulation; vacuum pump 12 continuously discharges the steam generated by flash evaporation to ensure the vacuum level in low-level flash cooler 11, so that the flash cooling process continues stably.

[0078] The specific embodiments of the invention have been described in detail above, but these are merely examples, and the invention is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this invention. Therefore, all equivalent transformations, modifications, and improvements made without departing from the spirit and principles of this invention should be included within the scope of this invention.

Claims

1. A parameter control method for low-temperature flash cooling of wet-process phosphoric acid based on particle swarm optimization algorithm, applied to the production equipment of the low-temperature flash cooling section of wet-process phosphoric acid, characterized in that, The particle swarm optimization (PSO)-based low-temperature flash cooling parameter control method for wet-process phosphoric acid includes: collecting core process parameters of the slurry and all-dimensional operating parameters of the equipment; determining the operating condition type based on the reaction trend of the low-temperature flash cooling section of wet-process phosphoric acid; selecting process parameters to be optimized as control targets for the operating conditions of the low-temperature flash cooling section of wet-process phosphoric acid; constructing a multi-objective optimization function that dynamically allocates the weights of the deviations of each parameter according to the operating conditions, balancing vacuum degree and density fluctuations while maintaining the optimal temperature range for slurry crystallization; and rapidly converging the multi-objective optimization function to the optimal load combination of the axial flow pump and vacuum pump through collaborative iteration within the simulated ion swarm search space, thereby achieving multi-parameter collaborative closed-loop regulation.

2. The method for controlling the low-temperature flash cooling parameters of wet-process phosphoric acid based on particle swarm optimization algorithm according to claim 1, characterized in that, The core process parameters of the slurry are the slurry temperature after cooling, the vacuum degree of the flash cooler, the solid content / density of the slurry, the slurry inlet temperature, and the slurry inlet pressure; the full-dimensional operating parameters of the equipment are the axial flow pump flow rate / speed / current, and the vacuum pump pumping capacity / speed / load.

3. The method for controlling the low-temperature flash cooling parameters of wet-process phosphoric acid based on particle swarm optimization algorithm according to claim 1, characterized in that, The fluctuation range and rate of change of temperature, vacuum degree, and density are calculated and coupled to determine the trend of drastic changes in the slurry reaction. At the same time, based on the preset process parameter threshold range, the current working condition type of the slurry circulation system is identified. The working condition type is one of the following: normal working condition, over-temperature working condition, insufficient vacuum degree working condition, and abnormal slurry density working condition. The criteria for judging various operating conditions are as follows: Normal operating condition is when the temperature of the slurry after cooling, the vacuum degree of the flash cooler, and the solid content or density of the slurry are all within the preset reasonable threshold range; Over-temperature operating condition is when the temperature of the slurry after cooling exceeds the preset reasonable upper limit of temperature; Insufficient vacuum operating condition is when the vacuum degree of the flash cooler exceeds the preset reasonable upper limit of vacuum. Abnormal slurry density conditions are when the slurry contains solids or its density exceeds the upper or lower limit of the preset reasonable density range.

4. The method for controlling the low-temperature flash cooling parameters of wet-process phosphoric acid based on particle swarm optimization algorithm according to claim 3, characterized in that, The process of identifying the current operating condition type of the slurry circulation system includes the following steps: According to the preset sampling time window, the values ​​of three parameters—temperature of the cooled slurry, vacuum degree of the flash cooler, and solid density of the slurry—are collected in real time at the beginning and end of the window. For each parameter, the difference between the maximum and minimum values ​​within the window is calculated as the fluctuation amplitude of the parameter. At the same time, the difference between the end value and the beginning value of the window is divided by the sampling time window duration to obtain the average rate of change of the parameter. The fluctuation amplitude and rate of change of temperature, vacuum degree, and density are obtained respectively. For each parameter, its fluctuation amplitude is compared with the maximum allowable fluctuation amplitude obtained from the historical stable operation of the wet-process phosphoric acid low-temperature flash cooling section, and its rate of change is compared with the maximum allowable rate of change of the parameter, respectively, to obtain the dimensionless relative fluctuation ratio and relative change ratio. Based on the sensitivity of the parameter to the drastic changes in the reaction, fixed weights are assigned to the fluctuation amplitude and rate of change, and the weighted sum is used to obtain the single-parameter coupling value of the parameter. Then, the single-parameter coupling values ​​of the three parameters, temperature, vacuum degree, and density, are weighted and summed according to the actual influence weights of the three on the crystallization and extraction efficiency of the slurry in the wet-process phosphoric acid low-temperature flash cooling section, to calculate a comprehensive trend value of the degree of drastic change in the reaction. The trend value of the degree of drastic change in the reaction is used to characterize the stability of the current slurry reaction state. The system pre-stores reasonable threshold ranges for the slurry temperature, flash cooler vacuum, and slurry solids density under the requirements of the low-temperature flash cooling process, including upper and lower limits for temperature, vacuum, and density; and acquires the current temperature, vacuum, and density values ​​in real time.

5. The method for controlling the low-temperature flash cooling parameters of wet-process phosphoric acid based on particle swarm optimization algorithm according to claim 1, characterized in that, Based on the current operating condition, a set of target indicators to be optimized under the corresponding operating condition is obtained. The selection principle is to prioritize core process parameters that exceed the threshold range as target indicators to be optimized. For example, under the over-temperature condition, the slurry temperature after cooling and the vacuum degree of the flash cooler are prioritized as target indicators to be optimized. Under the abnormal density condition, the slurry solids / density and the slurry temperature after cooling are prioritized as target indicators to be optimized. Boundary thresholds are set for the selected set of target indicators to be optimized, and dynamic weight coefficients are assigned to each indicator according to the working condition type. A multi-objective optimization function is constructed based on the indicator deviation value.

6. The method for controlling the low-temperature flash cooling parameters of wet-process phosphoric acid based on particle swarm optimization algorithm according to claim 1, characterized in that, The particle swarm optimization algorithm is used to solve the multi-objective optimization function to obtain the optimal operating load control command for the axial flow pump and / or vacuum pump.

7. The method for controlling the low-temperature flash cooling parameters of wet-process phosphoric acid based on particle swarm optimization algorithm according to claim 6, characterized in that, The process of obtaining optimal operating load control commands for axial flow pumps and / or vacuum pumps includes the following steps: The rotational speed or flow rate of the axial flow pump and the rotational speed or pumping capacity of the vacuum pump are used as decision variables to be optimized. The position vector of each particle corresponds to a set of operating load command combinations of the axial flow pump and the vacuum pump. The operating load command combination is input into the coupled response program of the wet-process phosphoric acid low-temperature flash cooling system, and the corresponding predicted values ​​of the slurry temperature, flash cooler vacuum degree and slurry solids density after cooling are obtained, thus obtaining the process parameter response set under the control command represented by each particle. The process parameter response set corresponding to each particle is substituted into the multi-objective optimization function to calculate the weighted deviation of each particle under the current control command as the fitness value. The individual optimal position of the particle and the global optimal position of the population are updated according to the fitness value to obtain the optimal axial flow pump and vacuum pump cooperative control command pair under the current iteration step. The position boundaries of particles are constrained based on the upper limit of temperature, upper limit of vacuum, and upper and lower limits of density in the low-temperature flash cooling section of wet-process phosphoric acid. Position vectors exceeding the boundaries are mapped back to the boundary values. The velocity and position update formula of the particle swarm algorithm is iteratively optimized. The process terminates when the global optimal fitness value no longer decreases after multiple iterations or reaches the maximum number of iterations. The final global optimal position is output as the optimal operating load control command for the axial flow pump and / or vacuum pump.

8. The method for controlling the low-temperature flash cooling parameters of wet-process phosphoric acid based on particle swarm optimization algorithm according to claim 7, characterized in that, The process of mapping position vectors that exceed the boundary back to boundary values ​​includes the following steps: Obtain the axial flow pump speed or flow rate value in the current particle position vector, input it into the coupled response model to obtain the corresponding predicted value of the slurry temperature after cooling. If the predicted value of the slurry temperature after cooling exceeds the upper limit of the temperature of the wet process phosphoric acid low-temperature flash cooling section, reduce the axial flow pump speed or flow rate value until the predicted temperature drops to within the upper limit of the temperature, and obtain the temperature-constrained axial flow pump control component. Based on the temperature-constrained axial flow pump control component, the vacuum pump speed or pumping volume value in the current particle position vector is obtained, and it is input into the coupled response model to obtain the corresponding flash cooler vacuum degree prediction value. If the flash cooler vacuum degree prediction value exceeds the vacuum degree upper limit, the vacuum pump speed or pumping volume value is increased until the vacuum degree prediction value drops to within the vacuum degree upper limit, thus obtaining the vacuum pump control component with vacuum degree constraint. Based on the temperature-constrained axial flow pump control component and the vacuum pump control component constrained by vacuum degree, the corresponding slurry solids density prediction value is obtained by inputting the coupled response program. If the slurry solids density prediction value exceeds the upper or lower limit of density, the control components of the axial flow pump and the vacuum pump are adjusted proportionally and synchronously until the density prediction value returns to the upper or lower limit range of density, thus obtaining the effective particle position vector after boundary constraints.

9. The method for controlling the low-temperature flash cooling parameters of wet-process phosphoric acid based on particle swarm optimization algorithm according to claim 8, characterized in that, According to the optimal operating load control command, adjust the speed / flow rate of the axial flow pump and / or the speed / pumping volume of the vacuum pump to bring the core process parameters of the slurry back to the preset threshold range, thereby achieving efficient slurry reaction.

10. A parameter control system for low-temperature flash cooling of wet-process phosphoric acid based on particle swarm optimization algorithm, which is applied to the parameter control method for low-temperature flash cooling of wet-process phosphoric acid based on particle swarm optimization algorithm as described in any one of claims 1 to 9, characterized in that, The particle swarm optimization algorithm-based wet phosphoric acid low-temperature flash cooling parameter control system includes: The parameter acquisition module is used to collect core process parameters of the slurry and all-dimensional operating parameters of the equipment; it integrates multiple sensors and acquisition instruments to achieve synchronous and real-time acquisition of process and equipment parameters. The operating condition identification module is used to calculate the fluctuation range and rate of change of temperature, vacuum degree and density based on the collected core process parameters of slurry. By coupling the three, it judges the trend of the degree of drastic change of slurry reaction and identifies the operating condition type of the current slurry circulation system based on the threshold of core process parameters. The target indicator filtering module is used to filter and obtain the set of target indicators to be optimized under the corresponding working condition based on the identified current working condition type. The objective function construction module is used to set boundary thresholds and dynamic weight coefficients for each indicator in the set of objective indicators to be optimized, and to construct a multi-objective optimization function. The optimization solution module is used to solve the multi-objective optimization function to obtain the optimal control commands for the axial flow pump and the vacuum pump. The execution adjustment module is used to adjust the operating load of the axial flow pump and / or vacuum pump according to the optimal control command, so as to achieve precise control of the parameters of the slurry circulation system.

Images