An intelligent optimization management method for process parameters of phosphoric acid production

Abstract

The present application relates to the technical field of phosphoric acid production, and particularly relates to an intelligent optimization management method for process parameters of phosphoric acid production; an adaptive window range at different time is obtained according to data variation characteristics of a phosphoric acid concentration sequence; a phosphoric acid production benefit value is obtained according to the phosphoric acid concentration in the adaptive window range; a benefit reference value is obtained according to the phosphoric acid production benefit value in a historical batch production process; a benefit comparison value is obtained according to the phosphoric acid production benefit value at a current time and the benefit reference value; whether the production process is adjusted is determined according to the benefit comparison value; a parameter deviation coefficient is obtained according to a difference characteristic of the sulfuric acid concentration of the production process corresponding to the benefit reference value and a current production process; and a phosphorus ore feeding amount difference coefficient is obtained according to the parameter deviation coefficient and the benefit comparison value. According to the parameter deviation coefficient, the phosphorus ore feeding amount difference coefficient and the benefit comparison value, the sulfuric acid concentration or the phosphorus ore feeding amount at the current time 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, 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:

[0004] Obtain the phosphoric acid concentration sequence, sulfuric acid concentration sequence, and phosphate rock feed sequence during different batches of phosphoric acid production;

[0005] 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.

[0006] 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.

[0007] 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.

[0008] Further, the step of obtaining the adaptive window range at different time according to the data variation characteristics of the phosphoric acid concentration sequence comprises:

[0009] The average value of the interval length of adjacent peak values of all phosphoric acid concentration sequences in the historical batch production process is calculated to obtain an average length; the production process is segmented according to the average length to obtain different sub-sections; the coefficient of variation of the phosphoric acid concentration in the sub-section is calculated to obtain a fluctuation degree; the change slope of the coefficient of variation of the sub-section and the previous sub-section 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 a window adjustment parameter of the sub-section; the difference between the preset maximum window length and the preset minimum window length is calculated to obtain an adjustment reference; the product of the adjustment reference and the window adjustment parameter of the sub-section where the arbitrary time is located is calculated to obtain an adjustment amount; the sum of the preset minimum window length and the adjustment amount is calculated to obtain the adaptive window length at the arbitrary time; and the range of the adaptive window length adjacent to the arbitrary time is taken as the adaptive window range.

[0010] Further, the step of obtaining the phosphoric acid production benefit value according to the distribution characteristics of the phosphoric acid concentration in the adaptive window range comprises:

[0011] The average value of the phosphoric acid concentration at all times in the adaptive window range in the arbitrary batch production process is calculated to obtain an average phosphoric acid concentration; the average value of the change slope of the phosphoric acid concentration at the arbitrary time and the previous time in the adaptive window range is calculated and positively correlated to obtain a change trend value; and the product of the change trend value and the average phosphoric acid concentration is calculated to obtain the phosphoric acid production benefit value of the adaptive window range.

[0012] Further, the step of obtaining the benefit reference value according to the difference characteristics of the phosphoric acid production benefit values at different times in the historical batch production process comprises:

[0013] The maximum value of the phosphoric acid production benefit value in each historical batch production process is clustered to obtain two clusters; and the median in the cluster with the maximum average value of the phosphoric acid production benefit value in the cluster is taken as the benefit reference value.

[0014] Further, the step of obtaining the benefit comparison value according to the difference characteristics of the phosphoric acid production benefit value at the current time in the current production process and the benefit reference value comprises:

[0015] The ratio of the benefit reference value to the phosphoric acid production benefit value at the current time in the current production process is calculated as the benefit comparison value.

[0016] Further, the step of judging whether the production process is adjusted according to the benefit comparison value comprises:

[0017] When the benefit comparison value is greater than constant 1, the current production process is adjusted.

[0018] Further, the step of obtaining a parameter deviation coefficient according to the difference between the sulfuric acid concentration of the production process corresponding to the benefit reference value and the current production process comprises:

[0019] In the formula, W represents the parameter deviation coefficient, X represents the average value of the sulfuric acid concentration in the adaptive window range at the current time in the current production process, and Y represents the average value of the sulfuric acid concentration in the adaptive window range at the same current time in the production process corresponding to the benefit reference value.

[0020] Further, the step of obtaining a phosphorus ore feeding amount difference coefficient according to the parameter deviation coefficient and the benefit comparison value comprises:

[0021] In the formula, R represents the phosphorus ore feeding amount difference coefficient, T represents the benefit comparison value, and W represents the parameter deviation coefficient; when the parameter deviation coefficient is not negative, W is 0.

[0022] Further, the step of iteratively adjusting the sulfuric acid concentration or the phosphorus ore feeding amount at the current time according to the parameter deviation coefficient, the phosphorus ore feeding amount difference coefficient and the benefit comparison value comprises:

[0023] The production process is adjusted by using a particle swarm optimization algorithm, and the algorithm update rule is In the formula, E represents the parameter adjustment result, F represents the parameter value at the current time, vg represents the particle velocity, K represents the dynamic step factor, and rand represents the disturbance result of a random function.

[0024] When the parameter deviation coefficient is not negative, the adjustment object of the production process is the phosphorus ore feeding amount, F in the algorithm update rule represents the phosphorus ore feeding amount at the current time, K represents the phosphorus ore feeding amount difference coefficient, and E represents the phosphorus ore feeding amount at the next time; when the parameter deviation coefficient is negative, the adjustment objects of the production process are the sulfuric acid concentration and the phosphorus ore feeding amount, F in the algorithm update rule respectively represents the phosphorus ore feeding amount or the sulfuric acid concentration at the current time, K represents the phosphorus ore feeding amount difference coefficient, and E respectively represents the phosphorus ore feeding amount or the sulfuric acid concentration at the next time; when the benefit comparison value is not greater than constant 1, the iteration is stopped.

[0025] The present application has the following beneficial effects:

[0026] In the present application, the adaptive window range is obtained, which can select a suitable data analysis range according to the fluctuation characteristics of the phosphoric acid concentration, thereby improving the calculation reliability of the phosphoric acid production benefit value. The phosphoric acid production benefit value is obtained, which can determine the production quality of different phosphoric acid production processes. The benefit reference value is obtained, which can determine the phosphoric acid production process with good historical production quality, thereby providing adjustment control for the current production process. The benefit comparison value is obtained, which can determine the production benefit difference between the current period and the historical period. The parameter deviation coefficient is obtained, which can determine whether the sulfuric acid concentration of the current production process is normal. The phosphorus ore feeding amount difference coefficient is obtained, which can further determine whether the phosphorus ore feeding amount and the sulfuric acid concentration in the current production process are suitable, thereby improving the adjustment accuracy. Finally, the sulfuric acid concentration or the phosphorus ore feeding amount at the current time is iteratively adjusted according to the parameter deviation coefficient, the phosphorus ore feeding amount difference coefficient and the benefit comparison value, thereby improving the phosphoric acid production quality. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0028] Figure 1 A flow chart of a phosphoric acid production process parameter intelligent optimization management method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following will combine the drawings and the preferred embodiments to specifically describe the specific implementation, structure, features and effects of the phosphoric acid production process parameter intelligent optimization management method according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0030] 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 the present application belongs.

[0031] The specific scheme of the phosphoric acid production process parameter intelligent optimization management method provided by the present application will be specifically described below in combination with the drawings.

[0032] Please refer to Figure 1 which shows a flow chart of a phosphoric acid production process parameter intelligent optimization management method provided by an embodiment of the present application. The method includes the following steps:

[0033] Step S1, obtain the phosphoric acid concentration sequence, the sulfuric acid concentration sequence, and the phosphorite feeding amount sequence in the production process of different batches of phosphoric acid.

[0034] In the embodiment of the present application, the implementation scenario is to adjust the production process of preparing phosphoric acid by wet process, and to improve the production stability and the quality of phosphoric acid. First, the phosphoric acid concentration sequence, the sulfuric acid concentration sequence, and the phosphorite feeding amount sequence in the production process of different batches of phosphoric acid are obtained. The phosphorite feeding amount refers to the weight of phosphorite fed into the reaction container at different time periods. The data collected in the production process is first denoised by the Kalman filtering algorithm to avoid the influence of noise on data analysis. The data length of the phosphoric acid concentration sequence, the sulfuric acid concentration sequence, and the phosphorite feeding amount sequence needs to be consistent. The objects with low collection frequency in the production process are processed by the cubic spline interpolation method. In the embodiment of the present application, the data of 50 batches of phosphoric acid production process are collected for analysis. The implementer can determine the sample amount collected according to the implementation scenario.

[0035] Step S2, obtaining the adaptive window range at different time periods according to the data variation characteristics of the phosphoric acid concentration sequence; obtaining the phosphoric acid production benefit value according to the distribution characteristics of the phosphoric acid concentration in the adaptive window range; obtaining the benefit reference value according to the difference characteristics of the phosphoric acid production benefit value at different time periods in the historical batch production process; and obtaining the benefit comparison value according to the difference characteristics of the phosphoric acid production benefit value at the current time period and the benefit reference value in the current production process.

[0036] The wet process for preparing phosphoric acid mainly obtains phosphoric acid through the reaction of phosphorite and sulfuric acid. In order to improve the process stability, the reactant parameters in the wet process industry need to be monitored and dynamically optimized in real time, so as to realize the adaptive regulation and control of complex working conditions, improve the phosphoric acid yield and quality, and reduce the energy consumption. First, the phosphoric acid data of the historical batch production process are processed and analyzed. The sliding window method is used to extract the phosphoric acid quality index characteristics at different time scales of each batch, so as to identify the key parameter combination corresponding to the optimal production state, which is used as a control reference and to judge the deviation degree of the parameters in the current production process. Due to the fluctuation of the composition of phosphorite and other factors, the phosphoric acid concentration will fluctuate to a certain extent in time sequence. Therefore, in the analysis process, the window characteristics of the phosphoric acid quality index at different production time periods need to be analyzed. The window characteristics extracted in time sequence can not only represent the change trend of the production process, but also enhance the stability and reliability of the parameter optimization judgment. First, the adaptive window range at different time periods is obtained according to the data variation characteristics of the phosphoric acid concentration sequence.

[0037] Preferably, in the embodiments of the present application, the step of obtaining the adaptive window range comprises: calculating the average value of interval durations of adjacent peak values of all phosphoric acid concentration sequences in the historical batch production process to obtain an average duration; wherein the peak values are obtained by a peak detection algorithm, and the average duration represents the overall cycle value of the phosphoric acid concentration in the production process. The production process is segmented according to the average duration to obtain different sub-sections; the coefficient of variation of the phosphoric acid concentration in the sub-sections is calculated to obtain a fluctuation degree; the greater the coefficient of variation, the more obvious the fluctuation of the phosphoric acid concentration in the sub-section, and thus the greater the window required to obtain the phosphoric acid production characteristics. The change slope of the coefficient of variation of the sub-section and the previous sub-section is calculated and positively correlated to obtain a fluctuation trend value; the greater the change slope of the coefficient of variation, the more enhanced the fluctuation characteristics of the phosphoric acid concentration, and thus the greater the window required. The product of the fluctuation degree and the fluctuation trend value is calculated and normalized to obtain a window adjustment parameter of the sub-section; the greater the window adjustment parameter, the greater the window required to analyze the phosphoric acid production characteristics; in the embodiments of the present application, the window adjustment parameter is calculated by using positively correlated, represents an exponential function with a natural constant as the base, and the function is normalized by using , and a represents the calculation object of the function. The difference between a preset maximum window length and a preset minimum window length is calculated to obtain an adjustment reference; the preset maximum window length and the preset minimum window length represent the upper limit and the lower limit of the window, and the implementer can determine them according to the implementation scene. The product of the adjustment reference and the window adjustment parameter of the sub-section in which the arbitrary time point is located is calculated to obtain an adjustment amount; the greater the adjustment amount, the greater the window length required. The sum of the preset minimum window length and the adjustment amount is calculated to obtain the adaptive window length at the arbitrary time point; the range of the adaptive window length adjacent to the arbitrary time point is taken as the adaptive window range, and different adaptive window ranges correspond to different time points.

[0038] Further, when the phosphoric acid concentration is high in a period, it means that the proportion of phosphoric acid in the total is large in the period, and the decomposition efficiency of phosphate rock in the reaction process is high; and if the concentration change trend continues to rise or remains stable in the period, it means that the reaction process is relatively stable, and it may be close to the best state of the phosphoric acid preparation reaction, and thus it can be explained that the proportion relationship between the reactants in the period may be the best. Therefore, the phosphoric acid production benefit value is obtained according to the distribution characteristics of the phosphoric acid concentration in the adaptive window range; preferably, in the embodiment of the present application, the step of obtaining the phosphoric acid production benefit value comprises: calculating the average value of the phosphoric acid concentration at all times in the adaptive window range in any batch production process to obtain the average phosphoric acid concentration; the larger the average phosphoric acid concentration, the higher the proportion of phosphoric acid content in the adaptive window range, and the better the phosphoric acid production benefit. The average value of the change slope of the phosphoric acid concentration at any time and the previous time in the adaptive window range is calculated and positively correlated to obtain a change trend value; the change slope represents the change trend of the phosphoric acid concentration, and the larger the change slope, the higher the phosphoric acid concentration; therefore, the larger the change trend value, the more continuous the production of phosphoric acid in the period, and the better the production benefit. The product of the change trend value and the average phosphoric acid concentration is calculated to obtain the phosphoric acid production benefit value of the adaptive window range. The larger the phosphoric acid production benefit value, the better the preparation effect of phosphoric acid in the period of the adaptive window range, and the better the proportion control of the reactant parameters; and then the benefit reference value can be obtained according to the difference characteristics of the phosphoric acid production benefit value at different times in the historical batch production process.

[0039] Preferably, in the embodiment of the present application, the step of obtaining the benefit reference value comprises: clustering the maximum value of the phosphoric acid production benefit value in each historical batch production process to obtain two clusters; in the embodiment of the present application, the K-means clustering algorithm is used for clustering, which belongs to the prior art, and the specific steps will not be repeated. The median in the cluster with the largest average value of the phosphoric acid production benefit value in the cluster is taken as the benefit reference value; the benefit reference value represents the phosphoric acid production benefit value when the phosphoric acid preparation effect is relatively good in the historical batch production process. Then the phosphoric acid production benefit value of the current production process can be compared with the benefit reference value to judge the quality of the current phosphoric acid production process, and therefore the benefit comparison value is obtained according to the difference characteristics of the phosphoric acid production benefit value at the current time in the current production process and the benefit reference value; preferably, in the embodiment of the present application, the step of obtaining the benefit comparison value comprises: calculating the ratio of the benefit reference value to the phosphoric acid production benefit value at the current time 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 time and the phosphoric acid production benefit value at the same time in the historical batch are the same, and the specific calculation steps will not be repeated. The larger the benefit comparison value, the worse the phosphoric acid production benefit of the current production process, and the more it needs to be adjusted and optimized.

[0040] Step S3, judging whether to adjust the production process according to the benefit comparison value; obtaining a parameter deviation coefficient according to the difference characteristics of the sulfuric acid concentration between the production process corresponding to the benefit reference value and the current production process; and obtaining a phosphate ore feeding amount difference coefficient according to the parameter deviation coefficient and the benefit comparison value.

[0041] When the benefit comparison value is greater than the constant 1, it means that the current phosphate production benefit value is smaller, and the current production process needs to be adjusted and optimized. Therefore, whether to adjust the production process is judged according to the benefit comparison value, which specifically includes: when the benefit comparison value is greater than the constant 1, the current production process is adjusted. If it is not greater than the constant 1, it means that the current phosphate production benefit is already high, and no subsequent adjustment step is needed. If adjustment is needed, it needs to be judged whether the effective component content in the phosphate ore is low or the sulfuric acid concentration is insufficient, which leads to poor production benefit. If the effective component content in the phosphate ore is low, the phosphate ore feeding flow 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 according to the difference characteristics of the sulfuric acid concentration between the production process corresponding to the benefit reference value and the current production process. Preferably, in the embodiment of the present application, the step of obtaining the parameter deviation coefficient includes:

[0042]

[0043] In the formula, W represents the parameter deviation coefficient, X represents the average value of the sulfuric acid concentration in the current adaptive window range at the current time in the current production process, and Y represents the average value of the sulfuric acid concentration in the adaptive window range at the same time as the current time in the production process corresponding to the benefit reference value. When the parameter deviation coefficient is greater, it means that the sulfuric acid concentration in the current production process is more than that in the batch with good historical production benefit, so as to reflect that the effective component in the current phosphate ore is low and cannot fully react with sulfuric acid. When the parameter deviation coefficient is smaller, it means that the sulfuric acid concentration in the current production process is lower than that in the batch with good historical production benefit, and the sulfuric acid concentration for fully reacting with the phosphate ore is insufficient.

[0044] Further, when the parameter deviation coefficient is negative, the situation that the effective component content in the phosphate ore is low and the sulfuric acid concentration is low at the same time may occur. Therefore, the phosphate ore feeding amount difference coefficient is obtained according to the parameter deviation coefficient and the benefit comparison value. Preferably, in the embodiment of the present application, the step of obtaining the phosphate ore feeding amount difference coefficient includes: In the formula, R represents the phosphate ore feeding amount difference coefficient, T represents the benefit comparison value, and W represents the parameter deviation coefficient. The difference between the current production benefit and the better production benefit is characterized, and the greater the value, the worse the current production benefit. W represents the deviation degree of the sulfuric acid content. When the phosphorite feeding amount difference coefficient is greater, it means that the current sulfuric acid concentration is insufficient to cause the current production benefit to be poor, and there is also a situation that the effective component content in the phosphorite is low. When the phosphorite feeding amount difference coefficient is smaller, it means that the main reason for the poor production benefit is the insufficient sulfuric acid concentration. It should be noted that when the parameter deviation coefficient is not negative, the value of W is 0.

[0045] In step S4, the sulfuric acid concentration or the phosphorite feeding amount at the current time is iteratively adjusted according to the parameter deviation coefficient, the phosphorite feeding amount difference coefficient, and the benefit comparison value.

[0046] After obtaining the parameter deviation coefficient, the phosphorite feeding amount difference coefficient, and the benefit comparison value, the sulfuric acid concentration or the phosphorite feeding amount at the current time can be iteratively adjusted. In the embodiment of the present application, the particle swarm optimization algorithm is used for parameter adjustment. This algorithm can regard different reactant parameters as a dimension of a particle in the search space, construct an optimization model with the phosphoric acid production benefit value as the objective function, and in the iteration process, introduce the phosphorite feeding amount difference coefficient to adaptively adjust the particle update formula, so that the greater the deviation degree of the parameter, the greater the parameter update amplitude, and the smaller the deviation degree of the parameter, the automatic contraction of the parameter update amplitude, thereby ensuring that the optimization process has both fast convergence ability and avoids excessive disturbance to the parameters that have approached the optimal state. Preferably, in the embodiment of the present application, the step of iteratively adjusting includes: adjusting the production process by the particle swarm optimization algorithm, and the algorithm update rule is , wherein E represents the parameter adjustment result, F represents the parameter value at the current time, vg represents the particle velocity, K represents the dynamic step factor, and rand represents the disturbance result of the random function. The rand randomly generates any value between 0 and 1.

[0047] When the parameter deviation coefficient is not negative, it means that the sulfuric acid concentration is too high and the phosphate ore is insufficient, so the adjustment object of the production process is the phosphate ore feeding amount, F in the algorithm update rule represents the phosphate ore feeding amount at the current time, K represents the phosphate ore feeding amount difference coefficient, and E represents the phosphate ore feeding amount at the next time; when the parameter deviation coefficient is negative, it means that the sulfuric acid concentration or the phosphate ore feeding amount may be insufficient, so the adjustment object of the production process is the sulfuric acid concentration and the phosphate ore feeding amount, F in the algorithm update rule represents the phosphate ore feeding amount or the sulfuric acid concentration at the current time, K represents the phosphate ore feeding amount difference coefficient, and E represents the phosphate ore feeding amount or the sulfuric acid concentration at the next time. Based on the reactants that need to be updated at the next time, the benefit comparison value is continuously calculated until the benefit comparison value is not greater than a 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 prior art, and the specific steps will not be described again. Through the adjustment of the reactant parameters in the phosphoric acid preparation process, the phosphoric acid preparation effect can be timely and accurately ensured to reach the best state, thereby improving the yield and quality of phosphoric acid.

[0048] To sum up, the embodiment of the present application provides an intelligent optimization management method for phosphoric acid production process parameters; the adaptive window range at different times is obtained according to the data variation characteristics of the phosphoric acid concentration sequence; the phosphoric acid production benefit value is obtained according to the phosphoric acid concentration in the adaptive window range; the benefit reference value is obtained according to the phosphoric acid production benefit value in the historical batch production process; the benefit comparison value is obtained according to the phosphoric acid production benefit value at the current time and the benefit reference value; whether the production process is adjusted is determined according to the benefit comparison value; the parameter deviation coefficient is obtained according to the difference characteristics of the sulfuric acid concentration of the production process corresponding to the benefit reference value and the current production process; and the phosphate ore feeding amount difference coefficient is obtained according to the parameter deviation coefficient and the benefit comparison value. The sulfuric acid concentration or the phosphate ore feeding amount at the current time is iteratively adjusted according to the parameter deviation coefficient, the phosphate ore feeding amount difference coefficient and the benefit comparison value, thereby improving the quality of the phosphoric acid production process.

[0049] It should be noted that the above-mentioned embodiment of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or may be advantageous.

[0050] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment mainly describes the difference 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 at the current moment. 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 used as the adaptive window range. 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 efficiency value within the adaptive window range. 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: Cluster the maximum values ​​of phosphoric acid production efficiency during each historical batch production process to obtain two clusters; use the median of the cluster with the largest average value of phosphoric acid production efficiency within the cluster as the efficiency reference value. 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. 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.

2. 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.

3. 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.

4. 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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