Phosphoric acid production process parameter intelligent optimization management method

Abstract

The invention relates to the technical field of phosphoric acid production, in particular to an intelligent optimization management method for phosphoric acid production process parameters. Obtaining adaptive window ranges at different moments according to data change characteristics of the phosphoric acid concentration sequence; obtaining a phosphoric acid production benefit value according to the phosphoric acid concentration in the self-adaptive window range; obtaining a benefit reference value according to the phosphoric acid production benefit value in the historical batch production process; obtaining a benefit comparison value according to the phosphoric acid production benefit value at the current moment and a benefit reference value; judging whether the production process is adjusted according to the benefit contrast value; obtaining a parameter deviation coefficient according to the difference characteristic of the sulfuric acid concentration of the production process corresponding to the benefit reference value and the current production process; and according to the parameter deviation coefficient and the benefit contrast value, obtaining a phosphorite putting amount difference coefficient. According to the parameter deviation coefficient, the phosphorite feeding amount difference coefficient and the benefit contrast value, the sulfuric acid concentration or the phosphorite feeding amount at the current moment is iteratively adjusted, and the quality of the phosphoric acid production process is improved.

Description

Technical Field

[0001] This invention relates to the field of phosphoric acid production technology, specifically to a method for intelligent optimization and management of phosphoric acid production process parameters. Background Technology

[0002] Phosphoric acid, as a basic inorganic chemical raw material, is widely used in fertilizers, food processing, metal surface treatment, and electronic material preparation. The wet process, as one method of producing phosphoric acid, involves the reaction of phosphate rock with sulfuric acid to generate phosphoric acid and byproducts. In actual production, due to the components of phosphate rock (such as P2O5, MgO, etc.), The content of phosphate rock and sulfuric acid varies depending on the mineral source, and the composition is difficult to control. This makes it difficult to determine the ratio of phosphate rock to sulfuric acid during the reaction process. As a result, the optimal sulfuric acid consumption needs to be continuously optimized and adjusted during the preparation of phosphoric acid. If the sulfuric acid is insufficient, the mineral will not be completely decomposed, while excessive sulfuric acid will cause the phosphoric acid impurities to increase and the material to be wasted. Therefore, there is a non-linear relationship between the amount of phosphate rock added and the concentration of sulfuric acid in phosphoric acid production. Production control methods based on human experience are difficult to achieve rapid and accurate reaction control, which affects the phosphoric acid production effect and is prone to defects such as insufficient decomposition, excessive impurities, or increased costs. Summary of the Invention

[0003] To address the aforementioned technical problems, the present invention aims to provide an intelligent optimization management method for phosphoric acid production process parameters. The specific technical solution adopted is as follows: Obtain the phosphoric acid concentration sequence, sulfuric acid concentration sequence, and phosphate rock feed sequence during different batches of phosphoric acid production; Based on the data variation characteristics of the phosphoric acid concentration sequence, an adaptive window range is obtained for different time periods; based on the distribution characteristics of phosphoric acid concentration within the adaptive window range, a phosphoric acid production benefit value is obtained; based on the difference characteristics of phosphoric acid production benefit values ​​at different time periods during historical batch production, a benefit reference value is obtained; based on the difference characteristics between the phosphoric acid production benefit value at the current time period and the benefit reference value during the current production process, a benefit comparison value is obtained. The production process is adjusted based on the benefit comparison value; the parameter deviation coefficient is obtained based on the difference in sulfuric acid concentration between the production process corresponding to the benefit reference value and the current production process; the phosphate rock addition difference coefficient is obtained based on the parameter deviation coefficient and the benefit comparison value. The sulfuric acid concentration or phosphate rock dosage is iteratively adjusted based on the parameter deviation coefficient, the phosphate rock dosage difference coefficient, and the benefit comparison value.

[0004] Furthermore, the step of obtaining the adaptive window range at different times based on the data change characteristics of the phosphate concentration sequence includes: The process involves calculating the average duration of the interval between adjacent peaks of all phosphate concentration sequences during historical batch production. The production process is then segmented based on this average duration to obtain different sub-segments. The coefficient of variation (COP) of phosphate concentration within each sub-segment is calculated to determine the degree of fluctuation. The slope of the COP change between the sub-segment and the previous sub-segment is calculated and positively correlated to obtain a fluctuation trend value. The product of the fluctuation degree and the fluctuation trend value is calculated and normalized to obtain the window adjustment parameter for each sub-segment. The difference between the preset maximum window length and the preset minimum window length is calculated to obtain the adjustment benchmark. The product of the adjustment benchmark and the window adjustment parameter of the sub-segment at any given time is calculated to obtain the adjustment amount. The sum of the preset minimum window length and the adjustment amount is calculated to obtain the adaptive window length at any given time. The range of adjacent adaptive window lengths before any given time is taken as the adaptive window range.

[0005] Further, the step of obtaining the phosphoric acid production benefit value based on the distribution characteristics of phosphoric acid concentration within the adaptive window range includes: Calculate the average phosphoric acid concentration at all times within the adaptive window range during any batch production process to obtain the average phosphoric acid concentration; calculate the average slope of the change in phosphoric acid concentration between any time and the previous time within the adaptive window range and perform a positive correlation mapping to obtain the trend value; calculate the product of the trend value and the average phosphoric acid concentration to obtain the phosphoric acid production benefit value within the adaptive window range.

[0006] Furthermore, the step of obtaining a benefit reference value based on the differences in phosphoric acid production benefit values ​​at different times during historical batch production includes: The maximum values ​​of phosphoric acid production benefits in each historical batch production process are clustered to obtain two clusters; the median of the cluster with the largest average value of phosphoric acid production benefits within the cluster is used as the benefit reference value.

[0007] Furthermore, the step of obtaining the benefit comparison value based on the difference between the phosphoric acid production benefit value at the current moment in the current production process and the benefit reference value includes: The ratio of the aforementioned benefit reference value to the phosphoric acid production benefit value at the current moment in the current production process is calculated as the benefit comparison value.

[0008] Furthermore, the step of determining whether to adjust the production process based on the benefit comparison value includes: When the benefit comparison value is greater than a constant 1, the current production process is adjusted.

[0009] Furthermore, the step of obtaining the parameter deviation coefficient based on the difference in sulfuric acid concentration between the production process corresponding to the benefit reference value and the current production process includes: In the formula, W represents the parameter deviation coefficient, X represents the average sulfuric acid concentration within the adaptive window range at the current moment in the current production process, and Y represents the average sulfuric acid concentration within the same adaptive window range in the production process corresponding to the benefit reference value at the current moment.

[0010] Further, the step of obtaining the phosphate rock application difference coefficient based on the parameter deviation coefficient and the benefit comparison value includes: In the formula, R represents the coefficient of difference in phosphate rock input, T represents the benefit comparison value, and W represents the parameter deviation coefficient; when the parameter deviation coefficient is not negative, W takes the value of 0.

[0011] Furthermore, the step of iteratively adjusting the sulfuric acid concentration or phosphate rock dosage at the current moment based on the parameter deviation coefficient, the phosphate rock dosage difference coefficient, and the benefit comparison value includes: The production process is adjusted using the particle swarm optimization algorithm, and the algorithm update rule is as follows: In the formula, E represents the parameter adjustment result, F represents the parameter value at the current moment, vg represents the particle velocity, K represents the dynamic step size factor, and rand represents the perturbation result of the random function. When the parameter deviation coefficient is not negative, the adjustment object of the production process is the amount of phosphate rock added. In the algorithm update rule, F represents the amount of phosphate rock added at the current moment, K represents the difference coefficient of phosphate rock added, and E represents the amount of phosphate rock added at the next moment. When the parameter deviation coefficient is negative, the adjustment object of the production process is the sulfuric acid concentration and the amount of phosphate rock added. In the algorithm update rule, F represents the amount of phosphate rock added or the sulfuric acid concentration at the current moment, K represents the difference coefficient of phosphate rock added, and E represents the amount of phosphate rock added or the sulfuric acid concentration at the next moment. When the benefit comparison value is not greater than a constant 1, the iteration stops.

[0012] The present invention has the following beneficial effects: In this invention, obtaining an adaptive window range allows for the selection of a suitable data analysis range based on the fluctuation characteristics of phosphoric acid concentration, thereby improving the reliability of the calculated phosphoric acid production benefit value. Obtaining the phosphoric acid production benefit value helps determine the production quality of different phosphoric acid production processes; obtaining benefit reference values ​​helps identify phosphoric acid production processes with historically good production quality, thus providing a reference for adjusting the current production process. Obtaining benefit comparison values ​​helps determine the difference in production benefit between the current period and historical periods. Obtaining parameter deviation coefficients helps determine whether the sulfuric acid concentration in the current production process is normal; obtaining the phosphate rock dosage difference coefficient further helps determine whether the phosphate rock dosage and sulfuric acid concentration are appropriate in the current production process, thereby improving the accuracy of adjustments. Finally, based on the parameter deviation coefficients, phosphate rock dosage difference coefficients, and benefit comparison values, the sulfuric acid concentration or phosphate rock dosage at the current moment is iteratively adjusted, improving the quality of phosphoric acid production. Attached Figure Description

[0013] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 The flowchart illustrates a method for intelligent optimization management of phosphoric acid production process parameters, as provided in one embodiment of the present invention. Detailed Implementation

[0015] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method for intelligent optimization management of phosphoric acid production process parameters proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0017] The following description, in conjunction with the accompanying drawings, details a specific scheme for an intelligent optimization management method for phosphoric acid production process parameters provided by the present invention.

[0018] Please see Figure 1 The diagram illustrates a flowchart of an intelligent optimization management method for phosphoric acid production process parameters according to an embodiment of the present invention. The method includes the following steps: Step S1: Obtain the phosphoric acid concentration sequence, sulfuric acid concentration sequence, and phosphate rock feeding sequence for different batches of phosphoric acid production.

[0019] In this embodiment of the invention, the implementation scenario involves adjusting the production process of phosphoric acid prepared by the wet process to improve production stability and phosphoric acid quality. First, sequences of phosphoric acid concentration, sulfuric acid concentration, and phosphate rock dosage are obtained for different batches of phosphoric acid produced; the phosphate rock dosage refers to the weight of phosphate rock added to the reaction vessel at different times. The data collected during the production process is first denoised using a Kalman filter algorithm to avoid the influence of noise on data analysis. The data lengths of the phosphoric acid concentration sequence, sulfuric acid concentration sequence, and phosphate rock dosage sequence need to be consistent. For objects with low collection frequency during the production process, cubic spline interpolation is used for interpolation. In this embodiment of the invention, data from 50 historical batches of phosphoric acid production processes are collected for analysis; the implementer can determine the sample size according to the implementation scenario.

[0020] Step S2: Obtain the adaptive window range at different times based on the data change characteristics of the phosphoric acid concentration sequence; obtain the phosphoric acid production benefit value based on the distribution characteristics of phosphoric acid concentration within the adaptive window range; obtain the benefit reference value based on the difference characteristics of phosphoric acid production benefit values ​​at different times during the historical batch production process; obtain the benefit comparison value based on the difference characteristics between the phosphoric acid production benefit value at the current time and the benefit reference value during the current production process.

[0021] The wet process for producing phosphoric acid primarily involves the reaction of phosphate rock with sulfuric acid. To improve process stability, real-time monitoring and dynamic optimization of reactant parameters in the wet process are necessary to achieve adaptive control under complex operating conditions, thereby increasing phosphoric acid yield and quality while reducing energy consumption. Firstly, historical batch phosphoric acid production data is processed and analyzed. A sliding window method is used to extract phosphoric acid quality index characteristics for each batch at different time scales, identifying the key parameter combinations corresponding to the optimal production state. This serves as a benchmark to assess the degree of parameter deviation in the current production process. Due to fluctuations in phosphate rock composition, phosphoric acid concentration exhibits a certain degree of fluctuation over time. Therefore, the analysis requires windowing feature analysis based on the changes in phosphoric acid quality indicators at different production moments. The window features extracted over time not only characterize the changing trends of the production process but also enhance the stability and reliability of parameter optimization judgments. Firstly, the adaptive window range for different time moments is obtained based on the data change characteristics of the phosphoric acid concentration sequence.

[0022] Preferably, in this embodiment of the invention, the step of obtaining the adaptive window range includes: calculating the average interval duration of adjacent peaks of all phosphate concentration sequences during the historical batch production process to obtain the average duration; wherein the peaks are obtained through a peak detection algorithm, and the average duration characterizes the overall periodic value of phosphate concentration during the production process. The production process is segmented according to the average duration to obtain different sub-segments; the coefficient of variation of phosphate concentration within a sub-segment is calculated to obtain the degree of fluctuation; the larger the coefficient of variation, the more pronounced the fluctuation of phosphate concentration within that sub-segment, thus requiring a larger window to capture the phosphate production characteristics. The slope of the coefficient of variation change between the sub-segment and the previous sub-segment is calculated and positively correlated to obtain the fluctuation trend value; the larger the slope of the coefficient of variation change, the stronger the fluctuation characteristics of phosphate concentration, thus requiring a larger window. The product of the degree of fluctuation and the fluctuation trend value is calculated and normalized to obtain the window adjustment parameter for that sub-segment; the larger the window adjustment parameter, the larger the window required to analyze the phosphate production characteristics; in this embodiment of the invention, using Perform positive correlation mapping. To represent an exponential function with the natural constant as its base, use... Normalization is performed, where 'a' represents the function computation object. The difference between the preset maximum window length and the preset minimum window length is calculated to obtain the adjustment baseline; the preset maximum and minimum window lengths represent the upper and lower limits of the window, which can be determined by the implementer according to the implementation scenario. The adjustment amount is obtained by multiplying the adjustment baseline by the window adjustment parameter of the sub-segment at any given time; a larger adjustment amount means a larger window length is required. The sum of the preset minimum window length and the adjustment amount is calculated to obtain the adaptive window length at that given time; the range of adjacent adaptive window lengths before any given time is taken as the adaptive window range, with different adaptive window ranges corresponding to different times.

[0023] Furthermore, when the phosphoric acid concentration is high over a period of time, it means that the proportion of phosphoric acid relative to the total is large during that period, and the decomposition efficiency of phosphate rock during the reaction is high. Moreover, if the concentration trend continues to rise or remains stable during that period, it means that the reaction process is relatively stable, possibly approaching the optimal state for phosphoric acid preparation, thus indicating that the ratio between reactants during that period may be optimal. Therefore, the phosphoric acid production benefit value is obtained based on the distribution characteristics of phosphoric acid concentration within the adaptive window range. Preferably, in this embodiment of the invention, the step of obtaining the phosphoric acid production benefit value includes: calculating the average value of phosphoric acid concentration at all times within the adaptive window range during any batch production process to obtain the average phosphoric acid concentration; the higher the average phosphoric acid concentration, the higher the proportion of phosphoric acid content within the adaptive window range, and the better the phosphoric acid production benefit. The average value of the slope of the change in phosphoric acid concentration between any time and the previous time within the adaptive window range is calculated and positively correlated to obtain the trend value; the slope characterizes the trend of phosphoric acid concentration change, and the larger the slope, the higher the phosphoric acid concentration; therefore, the larger the trend value, the more phosphoric acid is continuously produced during that period, and the better its production benefit. The product of the trend value and the average phosphoric acid concentration is calculated to obtain the phosphoric acid production efficiency value within the adaptive window range. A higher phosphoric acid production efficiency value means better phosphoric acid preparation and better control of reactant parameter ratios within the adaptive window range. Furthermore, a reference value for efficiency can be obtained based on the differences in phosphoric acid production efficiency values ​​at different times during historical batch production.

[0024] Preferably, in this embodiment of the invention, the step of obtaining the benefit reference value includes: clustering the maximum value of phosphoric acid production benefit during each historical batch production process to obtain two clusters; in this embodiment of the invention, the K-means clustering algorithm is used for clustering, which is an existing technology, and the specific steps will not be elaborated here. The median of the cluster with the largest average value of phosphoric acid production benefit within the cluster is taken as the benefit reference value; the benefit reference value characterizes the phosphoric acid production benefit value when the phosphoric acid preparation effect is relatively good during the historical batch production process. Furthermore, the quality of the current phosphoric acid production process can be judged by comparing the phosphoric acid production benefit value of the current production process with the benefit reference value. Therefore, the benefit comparison value is obtained based on the difference between the phosphoric acid production benefit value at the current moment and the benefit reference value in the current production process; preferably, in this embodiment of the invention, the step of obtaining the benefit comparison value includes: calculating the ratio of the benefit reference value to the phosphoric acid production benefit value at the current moment in the current production process as the benefit comparison value; it should be noted that the calculation process and data calculation range of the phosphoric acid production benefit value at the current moment are the same as those of the same moment in the historical batch, and the specific calculation steps will not be elaborated here. The larger the benefit comparison value, the worse the current phosphoric acid production efficiency is, and the more necessary it is to adjust and optimize the current production process.

[0025] Step S3: Determine whether the production process needs adjustment based on the benefit comparison value; obtain the parameter deviation coefficient based on the difference characteristics of sulfuric acid concentration between the production process corresponding to the benefit reference value and the current production process; obtain the phosphate rock input difference coefficient based on the parameter deviation coefficient and the benefit comparison value.

[0026] When the benefit comparison value is greater than a constant 1, it means that the current phosphoric acid production benefit value is smaller, and the current production process needs to be adjusted and optimized. Therefore, judging whether the production process needs to be adjusted based on the benefit comparison value specifically includes: when the benefit comparison value is greater than a constant 1, adjusting the current production process. If it is not greater than a constant 1, it means that the current phosphoric acid production benefit is already high, and no further adjustment steps are needed. If adjustment is required, it needs to be determined whether the poor production benefit is due to the low content of effective components in the phosphate rock or insufficient sulfuric acid concentration. If the content of effective components in the phosphate rock is low, the phosphate rock feed rate needs to be increased; if the sulfuric acid concentration is insufficient, the sulfuric acid concentration needs to be increased. Therefore, the parameter deviation coefficient can be obtained based on the difference in sulfuric acid concentration between the production process corresponding to the benefit reference value and the current production process. Preferably, in this embodiment of the invention, the step of obtaining the parameter deviation coefficient includes:

[0027] In the formula, W represents the parameter deviation coefficient, X represents the average sulfuric acid concentration within the adaptive window range at the current moment in the current production process, and Y represents the average sulfuric acid concentration within the same adaptive window range at the current moment in the production process corresponding to the benefit reference value. A larger parameter deviation coefficient indicates that the sulfuric acid concentration in the current production process is higher than that of batches with better historical production benefits, reflecting a lower effective component in the current phosphate rock and insufficient reaction with sulfuric acid. Conversely, a smaller parameter deviation coefficient indicates that the sulfuric acid concentration in the current production process is lower than that of batches with better historical production benefits, indicating insufficient sulfuric acid concentration for sufficient reaction with the phosphate rock.

[0028] Furthermore, when the parameter deviation coefficient is negative, it is possible for both low effective component content and low sulfuric acid concentration in phosphate rock to occur simultaneously. Therefore, the phosphate rock application difference coefficient is obtained based on the parameter deviation coefficient and the benefit comparison value. Preferably, in this embodiment of the invention, the step of obtaining the phosphate rock application difference coefficient includes: In the formula, R represents the coefficient of difference in phosphate rock input, T represents the benefit comparison value, and W represents the parameter deviation coefficient. This value represents the difference between the current production efficiency and the state of better production efficiency. A larger value indicates worse current production efficiency. W represents the degree of deviation in sulfuric acid content. A larger coefficient of variation in phosphate rock input indicates that the current insufficient sulfuric acid concentration is unlikely to cause the poor production efficiency, and also suggests a lower content of effective components in the phosphate rock. Conversely, a smaller coefficient of variation in phosphate rock input indicates that the poor production efficiency is mainly due to insufficient sulfuric acid concentration. It should be noted that W is 0 when the parameter deviation coefficient is not negative.

[0029] Step S4: Iteratively adjust the sulfuric acid concentration or phosphate rock dosage at the current moment based on the parameter deviation coefficient, the phosphate rock dosage difference coefficient, and the benefit comparison value.

[0030] After obtaining the parameter deviation coefficient, phosphate rock dosage difference coefficient, and benefit comparison value, the sulfuric acid concentration or phosphate rock dosage at the current moment can be iteratively adjusted. In this embodiment of the invention, parameter adjustment is performed using a particle swarm optimization algorithm. This algorithm can use different reactant parameters as a dimension of particles in the search space to construct an optimization model with the phosphoric acid production benefit value as the objective function. During the iteration process, the phosphate rock dosage difference coefficient is introduced to adaptively adjust the particle update formula, so that the update amplitude of parameters with greater deviation is larger, and the update amplitude of parameters with smaller deviation is automatically reduced, thereby ensuring that the optimization process has both rapid convergence capability and avoids excessive perturbation of parameters that have approached the optimal state. Preferably, in this embodiment of the invention, the iterative adjustment step includes: adjusting the production process using a particle swarm optimization algorithm, wherein the algorithm update rule is as follows: In the formula, E represents the parameter adjustment result, F represents the parameter value at the current moment, vg represents the particle velocity, K represents the dynamic step size factor, and rand represents the perturbation result of the random function. rand will randomly generate any value between 0 and 1.

[0031] When the parameter deviation coefficient is not negative, it means that the sulfuric acid concentration is too high and the phosphate rock is insufficient. Therefore, the adjustment target of the production process is the amount of phosphate rock added. In the algorithm update rule, F represents the amount of phosphate rock added at the current moment, K represents the difference coefficient of phosphate rock added, and E represents the amount of phosphate rock added at the next moment. When the parameter deviation coefficient is negative, it means that there may be insufficient sulfuric acid concentration or insufficient phosphate rock added. Therefore, the adjustment target of the production process is the sulfuric acid concentration and the amount of phosphate rock added. In the algorithm update rule, F represents the amount of phosphate rock added or the sulfuric acid concentration at the current moment, K represents the difference coefficient of phosphate rock added, and E represents the amount of phosphate rock added or the sulfuric acid concentration at the next moment. Based on the parameters that need to be updated at the next moment, the reactants are adjusted, and the benefit comparison value is calculated again until the benefit comparison value is no greater than the constant 1, which means that the production benefit is good and the iteration can be stopped. It should be noted that the particle swarm optimization algorithm is an existing technology, and the specific steps will not be described in detail. By adjusting the reactant parameters in the phosphoric acid preparation process, the phosphoric acid preparation effect can be ensured to reach the optimal state in a timely and accurate manner, thereby improving the yield and quality of phosphoric acid.

[0032] In summary, this invention provides an intelligent optimization management method for phosphoric acid production process parameters. It obtains an adaptive window range for different time periods based on the data variation characteristics of the phosphoric acid concentration sequence; obtains a phosphoric acid production benefit value based on the phosphoric acid concentration within the adaptive window range; obtains a benefit reference value based on the phosphoric acid production benefit values ​​during historical batch production; obtains a benefit comparison value based on the current phosphoric acid production benefit value and the benefit reference value; determines whether the production process needs adjustment based on the benefit comparison value; obtains a parameter deviation coefficient based on the difference in sulfuric acid concentration between the production process corresponding to the benefit reference value and the current production process; and obtains a phosphate rock dosage difference coefficient based on the parameter deviation coefficient and the benefit comparison value. This invention iteratively adjusts the sulfuric acid concentration or phosphate rock dosage at the current time based on the parameter deviation coefficient, the phosphate rock dosage difference coefficient, and the benefit comparison value, thereby improving the quality of the phosphoric acid production process.

[0033] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0034] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for intelligent optimization and management of phosphoric acid production process parameters, characterized in that, The method includes the following steps: Obtain the phosphoric acid concentration sequence, sulfuric acid concentration sequence, and phosphate rock feed sequence during different batches of phosphoric acid production; Based on the data variation characteristics of the phosphoric acid concentration sequence, an adaptive window range is obtained for different time periods; based on the distribution characteristics of phosphoric acid concentration within the adaptive window range, a phosphoric acid production benefit value is obtained; based on the difference characteristics of phosphoric acid production benefit values ​​at different time periods during historical batch production, a benefit reference value is obtained; based on the difference characteristics between the phosphoric acid production benefit value at the current time period and the benefit reference value during the current production process, a benefit comparison value is obtained. The production process is adjusted based on the benefit comparison value; the parameter deviation coefficient is obtained based on the difference in sulfuric acid concentration between the production process corresponding to the benefit reference value and the current production process; the phosphate rock addition difference coefficient is obtained based on the parameter deviation coefficient and the benefit comparison value. The sulfuric acid concentration or phosphate rock dosage is iteratively adjusted based on the parameter deviation coefficient, the phosphate rock dosage difference coefficient, and the benefit comparison value.

2. The intelligent optimization management method for phosphoric acid production process parameters according to claim 1, characterized in that, The step of obtaining the adaptive window range at different times based on the data change characteristics of the phosphate concentration sequence includes: The process involves calculating the average duration of the interval between adjacent peaks of all phosphate concentration sequences during historical batch production. The production process is then segmented based on this average duration to obtain different sub-segments. The coefficient of variation (COP) of phosphate concentration within each sub-segment is calculated to determine the degree of fluctuation. The slope of the COP change between the sub-segment and the previous sub-segment is calculated and positively correlated to obtain a fluctuation trend value. The product of the fluctuation degree and the fluctuation trend value is calculated and normalized to obtain the window adjustment parameter for each sub-segment. The difference between the preset maximum window length and the preset minimum window length is calculated to obtain the adjustment benchmark. The product of the adjustment benchmark and the window adjustment parameter of the sub-segment at any given time is calculated to obtain the adjustment amount. The sum of the preset minimum window length and the adjustment amount is calculated to obtain the adaptive window length at any given time. The range of adjacent adaptive window lengths before any given time is taken as the adaptive window range.

3. The intelligent optimization management method for phosphoric acid production process parameters according to claim 1, characterized in that, The step of obtaining the phosphoric acid production benefit value based on the distribution characteristics of phosphoric acid concentration within the adaptive window range includes: Calculate the average phosphoric acid concentration at all times within the adaptive window range during any batch production process to obtain the average phosphoric acid concentration; calculate the average slope of the change in phosphoric acid concentration between any time and the previous time within the adaptive window range and perform a positive correlation mapping to obtain the trend value; calculate the product of the trend value and the average phosphoric acid concentration to obtain the phosphoric acid production benefit value within the adaptive window range.

4. The intelligent optimization management method for phosphoric acid production process parameters according to claim 1, characterized in that, The step of obtaining the benefit reference value based on the differences in phosphoric acid production benefit values ​​at different times during historical batch production includes: The maximum values ​​of phosphoric acid production benefits in each historical batch production process are clustered to obtain two clusters; the median of the cluster with the largest average value of phosphoric acid production benefits within the cluster is used as the benefit reference value.

5. The intelligent optimization management method for phosphoric acid production process parameters according to claim 1, characterized in that, The step of obtaining the benefit comparison value based on the difference between the current phosphoric acid production benefit value and the benefit reference value in the current production process includes: The ratio of the aforementioned benefit reference value to the phosphoric acid production benefit value at the current moment in the current production process is calculated as the benefit comparison value.

6. The intelligent optimization management method for phosphoric acid production process parameters according to claim 1, characterized in that, The step of determining whether to adjust the production process based on the benefit comparison value includes: When the benefit comparison value is greater than a constant 1, the current production process is adjusted.

7. The intelligent optimization management method for phosphoric acid production process parameters according to claim 1, characterized in that, The step of obtaining the parameter deviation coefficient based on the difference in sulfuric acid concentration between the production process corresponding to the benefit reference value and the current production process includes: In the formula, W represents the parameter deviation coefficient, X represents the average sulfuric acid concentration within the adaptive window range at the current moment in the current production process, and Y represents the average sulfuric acid concentration within the same adaptive window range in the production process corresponding to the benefit reference value at the current moment.

8. The intelligent optimization management method for phosphoric acid production process parameters according to claim 1, characterized in that, The step of obtaining the phosphate rock application difference coefficient based on the parameter deviation coefficient and the benefit comparison value includes: In the formula, R represents the coefficient of difference in phosphate rock input, T represents the benefit comparison value, and W represents the parameter deviation coefficient; when the parameter deviation coefficient is not negative, W takes the value of 0.

9. The intelligent optimization management method for phosphoric acid production process parameters according to claim 1, characterized in that, The step of iteratively adjusting the sulfuric acid concentration or phosphate rock dosage at the current moment based on the parameter deviation coefficient, the phosphate rock dosage difference coefficient, and the benefit comparison value includes: The production process is adjusted using the particle swarm optimization algorithm, and the algorithm update rule is as follows: In the formula, E represents the parameter adjustment result, F represents the parameter value at the current moment, vg represents the particle velocity, K represents the dynamic step size factor, and rand represents the perturbation result of the random function. When the parameter deviation coefficient is not negative, the adjustment object of the production process is the amount of phosphate rock added. In the algorithm update rule, F represents the amount of phosphate rock added at the current moment, K represents the difference coefficient of phosphate rock added, and E represents the amount of phosphate rock added at the next moment. When the parameter deviation coefficient is negative, the adjustment object of the production process is the sulfuric acid concentration and the amount of phosphate rock added. In the algorithm update rule, F represents the amount of phosphate rock added or the sulfuric acid concentration at the current moment, K represents the difference coefficient of phosphate rock added, and E represents the amount of phosphate rock added or the sulfuric acid concentration at the next moment. When the benefit comparison value is not greater than a constant 1, the iteration stops.

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