Anhydrous hydrogen fluoride prewash data deep analysis and optimization system

Abstract

The application discloses anhydrous hydrogen fluoride pre-washing data deep analysis and optimization system and relates to the technical field of gas pre-washing supervision.The system comprises the following modules: a pre-washing data analysis module, a pre-washing nozzle detection module and a pre-washing spraying optimization module.The application realizes real-time monitoring on the pre-washing link of the anhydrous hydrogen fluoride production process, determines the uniformity of spraying and atomizing to determine whether to adopt a nozzle optimization control strategy, then determines whether the nozzle is abnormal in the execution process of the spraying uniformity optimization control strategy and selects whether to generate a nozzle detection scheme, finally performs scheme detection compliance determination after the execution of the nozzle detection scheme to determine whether to perform corresponding nozzle optimization adjustment, improves the sufficiency degree of the correlation between the spraying effect analysis and the spraying system state in the pre-washing process, and solves the problem of insufficient correlation between the spraying effect analysis and the spraying system state in the pre-washing process in the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gas pre-washing supervision, in particular to a system for deep analysis and optimization of anhydrous hydrogen fluoride pre-washing data. BACKGROUND

[0002] In the prior art, the production process of anhydrous hydrogen fluoride generally includes raw material preparation, fluorination reaction stage, pre-washing link, cooling stage, rectification and purification, by-product treatment, tail gas treatment, and product storage and packaging; wherein, in the pre-washing link, first introduce the hydrogen fluoride gas into the spray pre-washing tower, the hydrogen fluoride gas enters from the bottom and flows upward along the tower (countercurrent operation), the washing liquid (water, dilute sulfuric acid or circulating hydrogen fluoride mother liquor) sprayed from the top of the tower fully contacts with the hydrogen fluoride gas to remove water-soluble impurities and small droplets in the gas, then a mist eliminator (wire mesh mist eliminator or corrugated plate) is arranged at the top of the tower to further remove small droplets entrained with the gas flow, preventing liquid from entering the subsequent condensation or rectification system with the gas, and finally the washing liquid circulation and discharge treatment are performed.

[0003] For example, the industrial park greenhouse gas emission flow optimization method and system disclosed in Chinese patent application No. CN116862069A includes: S1. Collecting greenhouse gas emission data of each enterprise in the industrial park, the emission data including emission flow and concentration; S2. Preprocessing the collected data; S3. Using data analysis methods to analyze and count the greenhouse gas emission data of the industrial park after preprocessing; S4. Based on the results of data analysis, establishing an emission trend prediction model of the greenhouse gas emission of the industrial park to predict the future emission trend; S5. According to the results of model prediction, formulating corresponding greenhouse gas emission control strategies and plans.

[0004] For example, the light-weight toxic and harmful gas distribution detection method based on deep learning disclosed in Chinese patent application No. CN111915069B includes: collecting the topography environmental parameters and toxic and harmful gas parameters of the measured site, constructing key features, preprocessing data, offline training, model quantization training, normalization exponential regression, and predicting the three-dimensional distribution results of the pollution site topography and toxic and harmful gas, thereby determining the danger level, and sending the predicted results to the cloud server platform to feedback the three-dimensional distribution of the toxic and harmful gas in the pollution site and the danger level results.

[0005] The above-mentioned technology at least has the following technical problems:

[0006] In actual production process, the nozzle for spraying is used for a long time in industrial environment, which may be affected by various industrial factors to cause problems of the nozzle and affect the spraying effect, for example, the nozzle angle is deviated or loses the centering due to vibration or impact in equipment operation, or the nozzle metal or plastic structure is corroded by hydrogen fluoride or sulfuric acid in the washing liquid to cause the nozzle caliber to expand or be eccentric, and the nozzle direction is slowly deviated due to deformation of the metal pipeline or nozzle support caused by repeated temperature changes in the tower, or the spacing is uneven, therefore, when the crude hydrogen fluoride gas is sprayed through the pre-washing tower, the spraying coverage may be insufficient due to the angle problem of the nozzle, there are many dead angles, or the nozzle spacing is deviated to cause the spraying to deviate from the center and be superimposed, which causes liquid enrichment in some areas and liquid scarcity in some areas, or the spraying angle is changed, the droplet is enlarged, and some local nozzles do not spray liquid or spray eccentrically, which causes uneven gas-liquid contact, directly affecting the absorption efficiency, liquid utilization rate and mass transfer effect in the tower, the existing technology solves the problem of uneven spraying of the nozzle by using electric valve and variable frequency control of spraying pressure, however, in actual application scenarios, there is still a problem that the spraying effect is not sufficiently associated with the state of the spraying system when the pre-washing process is analyzed. SUMMARY

[0007] In order to solve the technical problem that the existing technology does not sufficiently associate the spraying effect with the state of the spraying system when the pre-washing process is analyzed, the embodiments of the present application provide a anhydrous hydrogen fluoride pre-washing data deep analysis and optimization system. The technical scheme is as follows:

[0008] The anhydrous hydrogen fluoride pre-washing data deep analysis and optimization system comprises a pre-washing data analysis module, a pre-washing nozzle detection module and a pre-washing spraying optimization module; wherein the pre-washing data analysis module is used for real-time monitoring of the pre-washing link of the anhydrous hydrogen fluoride production process, and determining the uniformity of the spraying spray to determine whether to take corresponding nozzle optimization control strategy to maintain the uniformity of the nozzle spray of the spraying system of the spraying tower; the pre-washing nozzle detection module is used for determining whether the nozzle is abnormal during execution of the spraying uniformity optimization control strategy, and selecting whether to generate a nozzle detection scheme, the nozzle detection scheme indicating detection of the angle, spacing, blockage and deformation index of the nozzle; the pre-washing spraying optimization module is used for scheme detection compliance determination after sequentially executing the nozzle detection scheme to determine whether to perform corresponding nozzle optimization adjustment, the nozzle optimization adjustment being used for improving the eligibility of the spraying state of the nozzle.

[0009] The technical scheme provided by the embodiments of the present application has at least the following beneficial effects:

[0010] 1. The application realizes the judgment of whether the nozzle is abnormal by real-time monitoring the pre-washing link of the production process of anhydrous hydrogen fluoride, determining the uniformity of the spray and spray to determine whether to take the corresponding nozzle optimization control strategy, thereby ensuring the uniformity of the nozzle spray of the spray tower spray system, then determining whether the nozzle is abnormal during the execution of the spray uniformity optimization control strategy, and selecting whether to generate a nozzle detection scheme, realizing the judgment of whether the nozzle is abnormal to cause the consumption of the chemical agent for spraying to increase, finally performing scheme detection compliance determination after sequentially executing the nozzle detection scheme to determine whether to take the corresponding nozzle optimization adjustment, thereby timely eliminating the influence of the nozzle factor on the spraying effect, thereby reducing the consumption of the chemical agent for spraying, and improving the sufficiency of the correlation between the spraying effect analysis and the state of the spraying system during the pre-washing process.

[0011] 2. The application realizes more accurate quantification of the deviation degree of each spraying abnormal area and the corresponding average reference area by obtaining the spraying process parameter quantification index by ratio quantification of each spraying abnormal area and the corresponding average reference area, then coupling the spraying process parameter quantification index and the corresponding spraying spray uniformity influence factor after weighting operation to obtain the spraying spray uniformity analysis coefficient, thereby more accurately quantifying the spraying spray uniformity degree of the nozzle in the current preset analysis period, thereby realizing accurate determination of the spraying spray uniformity, and improving the determination efficiency of the spraying spray.

[0012] 3. The application first obtains the chemical agent consumption deviation by difference operation of the chemical agent consumption data and the corresponding reference consumption data, and performs data normalization processing, and simultaneously performs data normalization processing on the pre-washing parameters, which helps to ensure that the data is dimensionally consistent during subsequent processing, then obtains the nozzle abnormality determination analysis data from the preset database, and couples the pre-washing parameters and the corresponding pre-washing analysis factor after weighting operation to obtain the initial nozzle abnormality determination value, which initially quantifies the nozzle abnormality degree reflected by the pre-washing parameters, finally obtains the nozzle abnormality determination value based on the initial nozzle abnormality determination value, the chemical agent consumption deviation and the corresponding nozzle abnormality determination analysis factor after weighting operation, thereby realizing comprehensive quantification of the nozzle abnormality degree, and thereby timely taking the corresponding nozzle optimization strategy to reduce the influence of the nozzle on the spraying effect during pre-washing. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creating any creative labor.

[0014] Figure 1is a structural diagram of the anhydrous hydrogen fluoride pre-washing data deep analysis and optimization system provided by the embodiment of the present application;

[0015] Figure 2 is a flowchart for judging the uniformity of the spray;

[0016] Figure 3 is one of the interface diagrams of the pre-washing link of the anhydrous hydrogen fluoride intelligent monitoring system provided by the embodiment of the present application;

[0017] Figure 4 is one of the interface diagrams of the pre-washing link of the anhydrous hydrogen fluoride intelligent monitoring system provided by the embodiment of the present application;

[0018] Figure 5 is one of the interface diagrams of the pre-washing link of the anhydrous hydrogen fluoride intelligent monitoring system provided by the embodiment of the present application;

[0019] Figure 6 is a flowchart for performing scheme detection compliance judgment. DETAILED DESCRIPTION

[0020] The technical solutions in the present application will be described below with reference to the drawings.

[0021] In the embodiments of the present application, the words such as "example", "for example" are used to represent an example, illustration or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0022] In the embodiments of the present application, sometimes the subscript such as W1 may be written in the form of non-subscript such as W1, and when the difference is not emphasized, the meanings expressed are consistent.

[0023] To make the technical problems, technical solutions and advantages of the present application clearer, the following will be described in detail with reference to the drawings and specific embodiments.

[0024] The embodiments of the present application provide an anhydrous hydrogen fluoride pre-washing data deep analysis and optimization system. As shown in Figure 1 The structural diagram of the anhydrous hydrogen fluoride pre-washing data deep analysis and optimization system provided by the embodiment of the present application is shown in the figure, and the processing flow of the system includes the following modules:

[0025] The pre-washing data analysis module is used for real-time monitoring of the pre-washing link of the anhydrous hydrogen fluoride production process, and determining the uniformity of the spraying and spraying to determine whether to take corresponding nozzle optimization control strategy to maintain the uniformity of the nozzle spraying of the spraying system of the spraying tower; through the pre-washing data analysis module, it is determined whether to take the corresponding nozzle optimization control strategy, and the uniformity of the nozzle spraying of the spraying tower is ensured.

[0026] The pre-washing nozzle detection module is used to determine whether the nozzle is abnormal during the execution of the spraying uniformity optimization control strategy, and to select whether to generate a nozzle detection scheme, which represents the detection of the angle, spacing, blockage and deformation indicators of the nozzle; through the pre-washing nozzle detection module, it is more accurate to determine whether the nozzle is abnormal, so as to cause the consumption of the chemical agent for spraying to increase, and then take corresponding optimization measures in time to reduce the consumption of the chemical agent.

[0027] The pre-washing spraying optimization module is used to determine whether to perform corresponding nozzle optimization adjustment after sequentially executing the nozzle detection scheme, and the scheme detection compliance determination is used to determine whether each nozzle detection indicator meets the preset standard, and the nozzle optimization adjustment is used to improve the eligibility of the spraying state of the nozzle; through the pre-washing spraying optimization module, it is determined whether to perform nozzle optimization adjustment, which not only helps to timely eliminate the influence of nozzle factors on the spraying effect, but also reduces the consumption of the chemical agent for spraying in the pre-washing process.

[0028] In this embodiment, through the step-by-step execution and synergistic effect of the pre-washing data analysis module, the pre-washing nozzle detection module and the pre-washing spraying optimization module, it is not only more efficient to determine whether the nozzle is abnormal in the pre-washing process, thereby improving the pre-washing efficiency of the nozzle spraying, but also helps to improve the sufficiency of the analysis of the spraying effect in the pre-washing process in relation to the state of the spraying system.

[0029] It should be noted that before designing the data depth analysis and optimization system for pre-washing of anhydrous hydrogen fluoride, professional and technical personnel usually first construct a preset database for supporting the operation of various control strategies. The database integrates a plurality of key process parameter information, including the reference range of the spraying process parameter, the spraying and spraying uniformity influence factor and the average reference area, the spraying uniformity comparison value, the reference consumption data, the nozzle abnormality determination analysis data, the preset determination value, the preset nozzle angle, the initial spacing, the pressure difference fluctuation interval and the flow minimum limit, etc. These parameters are set in advance by personnel with professional technical background according to the analysis method and the on-site hardware configuration conditions. The preset database provides a core data basis for the subsequent data uploading, storage optimization and screening judgment and other automatic processes.

[0030] AsFigure 2 The flowchart shown is a flowchart for determining the uniformity of the spraying spray provided by the embodiment of the present application, and the specific logic is as follows: first, real-time collection of the spraying process parameters, and curve visualization of the spraying process parameters to obtain a spraying process parameter curve graph, then marking the reference range of the spraying process parameters in the corresponding spraying process parameter curve graph, then obtaining the image area that exceeds the corresponding reference range in the spraying process parameter curve graph to obtain a spraying abnormal area, and finally obtaining a spraying spray uniformity analysis coefficient based on the spraying abnormal area, and comparing the spraying spray uniformity analysis coefficient with the spray uniformity comparison value: if the spraying spray uniformity analysis coefficient is less than the spray uniformity comparison value, it is determined that the nozzle spray uniformity is qualified, otherwise it is determined that the nozzle spray uniformity is unqualified, and a corresponding nozzle optimization control strategy is adopted; through the above process, not only the spraying spray uniformity degree of the nozzle in the current preset analysis period in the pre-washing process is more accurately quantified, but also a corresponding optimization strategy is timely adopted to improve the efficiency of the pre-washing process.

[0031] Further, the uniformity of the spraying spray is determined to determine whether a corresponding nozzle optimization control strategy is adopted, and the specific steps are as follows:

[0032] A1, real-time collection of the spraying process parameters of the preset analysis period, the spraying process parameters including the tail gas hydrogen fluoride concentration, the spraying zone pressure and the column pressure.

[0033] Specifically, the tail gas is monitored in real time by an online gas analyzer (such as an infrared absorption type hydrogen fluoride analyzer, an electrochemical sensor and a laser gas analyzer) installed in the tail gas discharge pipeline to obtain the tail gas hydrogen fluoride concentration; the gas pressure is measured by a pressure transmitter installed on the side wall of the pre-washing tower spraying section to obtain the spraying zone pressure; and the operating pressure of the entire column body is measured by a column top pressure transmitter to obtain the column pressure.

[0034] A2, curve visualization of the spraying process parameters to obtain a spraying process parameter curve graph, making the spraying system running state more intuitive and transparent, and facilitating the operator to master the spraying system dynamics, the spraying process parameter curve graph including a tail gas hydrogen fluoride concentration curve graph, a spraying zone pressure curve graph and a column pressure curve graph; wherein a software tool (such as Excel, Python, R, Tableau, etc.) is used to draw the curve graph.

[0035] A3, marking the reference range of the spraying process parameters extracted from the preset database in the corresponding spraying process parameter curve graph; wherein the reference range of the spraying process parameters is pre-set by the preset staff based on historical data, working experience and industrial requirements, and stored in the preset database.

[0036] A4, obtaining a spraying abnormal area by obtaining an image area exceeding a corresponding reference range in a spraying process parameter curve graph in a preset analysis period, the spraying abnormal area including a tail gas hydrogen fluoride concentration abnormal area, a spraying area pressure abnormal area, and a tower pressure abnormal area.

[0037] It should be noted that the cumulative abnormal value is obtained by accumulating the values of the spraying process parameters that do not belong to the reference range at each time point, and the corresponding spraying abnormal area is obtained by multiplying the cumulative abnormal value and the sampling time interval of the corresponding spraying process parameter.

[0038] A5, obtaining a spraying mist uniformity analysis coefficient based on the spraying abnormal area, and determining whether to adopt a corresponding nozzle optimization control strategy according to the spraying mist uniformity analysis coefficient, the spraying mist uniformity analysis coefficient being used to quantify the spraying mist uniformity degree of the nozzle in the current preset analysis period.

[0039] In the embodiment, by accurately identifying the uneven spraying area and timely adjusting the nozzle configuration, the spraying coverage efficiency in the pre-washing involved spraying tower is effectively improved, not only reducing the waste of spraying liquid for pre-washing, but also improving the tail gas treatment effect after pre-washing; at the same time, by early identifying the problems such as nozzle blockage, unreasonable distribution or pressure difference abnormality in the pre-washing process, it is helpful to avoid local failure of the spraying system, thereby reducing the risk of tail gas emission exceeding the standard and equipment corrosion; and the quantitative index system established based on the spraying abnormal area and the spraying mist uniformity analysis coefficient more accurately determines the uniformity of the spraying mist in the spraying area in the pre-washing process.

[0040] As a further scheme, the spraying mist uniformity analysis coefficient is obtained based on the spraying abnormal area, and the specific process is as follows:

[0041] First, the spraying mist uniformity influence factor and the average reference area are extracted from the preset database, the spraying mist uniformity influence factor including the tail gas hydrogen fluoride concentration influence factor, the spraying area pressure influence factor, and the tower pressure influence factor, and the average reference area including the tail gas hydrogen fluoride concentration reference area, the spraying area pressure reference area, and the tower pressure reference area.

[0042] It should be noted that the spraying abnormal area is input into the trained spraying analysis mapping model to output the corresponding spraying spray uniformity influence factor, which represents the influence degree of each spraying abnormal area on the spraying spray uniformity analysis coefficient. The model is constructed by using a logistic regression algorithm, and is trained based on a cross-entropy loss criterion through a scikit-learn framework. The model training data includes the spraying abnormal area obtained in the historical period and the spraying spray uniformity influence factor set by the pre-set staff according to the experience rule, which is used to fit the mapping relationship between the spraying abnormal area and the spraying spray uniformity analysis coefficient. In addition, the average reference area is obtained by obtaining the spraying abnormal area of each pre-set analysis period in the historical period and performing mean operation, so the average reference area is not 0.

[0043] Next, each spraying abnormal area and the corresponding average reference area are ratio-quantified to obtain a spraying process parameter quantitative index; wherein the ratio-quantification means that each spraying abnormal area and the corresponding average reference area are ratio-operated.

[0044] Finally, the spraying process parameter quantitative index and the corresponding spraying spray uniformity influence factor are coupled to obtain the spraying spray uniformity analysis coefficient after weighting operation.

[0045] The specific limit expression of the spraying spray uniformity analysis coefficient is as follows:

[0046] ;

[0047] In the formula, x represents the tail gas hydrogen fluoride concentration abnormal area, y represents the spraying area pressure abnormal area, z represents the tower pressure abnormal area, a represents the tail gas hydrogen fluoride concentration reference area, b represents the spraying area pressure reference area, c represents the tower pressure reference area, i x represents the tail gas hydrogen fluoride concentration influence factor, i y represents the spraying area pressure influence factor, i z represents the tower pressure influence factor, and λ represents the spraying spray uniformity analysis coefficient.

[0048] In the embodiment, the algorithm combines the spraying abnormal area, the average reference area, and the corresponding spraying spray uniformity influence factor to analyze to obtain a spraying spray uniformity analysis coefficient, wherein, the greater the ratio of each spraying abnormal area to the corresponding average reference area, the higher the deviation degree of the corresponding spraying process parameter, however, the greater the spraying process parameter quantitative index, the greater the spraying abnormal area, and the greater the corresponding spraying spray uniformity analysis coefficient, indicating that the spraying spray uniformity degree may be lower, and vice versa, indicating that the spraying spray uniformity degree may be higher. Through the analysis of the spraying spray uniformity analysis coefficient, the spraying spray uniformity effect of the nozzle in the spraying tower during the pre-washing of hydrogen fluoride can be more accurately analyzed, and the corresponding spraying spray control strategy can be timely taken to maintain the spraying spray uniformity effect, so that the pre-washing efficiency of hydrogen fluoride is more stable, and the sufficiency degree of the spraying effect analysis in the pre-washing process and the spraying system state is further improved.

[0049] As Figure 3 shown, it is one of the interface diagrams of the pre-washing link of the anhydrous hydrogen fluoride intelligent monitoring system provided by the embodiment, the left side of the diagram is the side navigation bar part of the system, including real-time monitoring, parameter analysis, alarm management, data management, running daily report, inspection record, equipment management, safety and environmental protection module, user permission and system setting, and there is also a process flow progress display on the top of the interface diagram, including raw material preparation, fluorination reaction stage, pre-washing link, cooling stage, rectification and purification, by-product treatment, tail gas treatment and product storage and packaging, the current interface display process is in the pre-washing link, the middle of the interface is the display interface of real-time parameters, including feed flow, outlet gas flow, temperature, pressure, washing liquid level, nozzle angle correction quantity and nozzle spacing correction quantity, and the related display of the pre-washing state and temperature trend is embodied in Figure 4 , Figure 4 the second interface diagram of the pre-washing link of the anhydrous hydrogen fluoride intelligent monitoring system provided by the embodiment, Figure 5 the display of the equipment running state and the spray uniformity index indication area is shown in the interface diagram, Figure 5 the third interface diagram of the pre-washing link of the anhydrous hydrogen fluoride intelligent monitoring system provided by the embodiment, Figure 3 , Figure 4 and Figure 5 comprehensively display the real-time state of the parameters and the equipment in the pre-washing link, and the uniformity state of the spray in the washing tower and the corresponding nozzle state, through the above interface diagrams, the anhydrous hydrogen fluoride production personnel responsible for monitoring the pre-washing link can better understand the specific conditions of the equipment during pre-washing, and can timely assist the equipment in optimizing the corresponding nozzle, thereby ensuring the efficiency of the anhydrous hydrogen fluoride pre-washing link.

[0050] Further, whether to adopt the corresponding nozzle optimization control strategy is determined according to the spray mist uniformity analysis coefficient, and the specific process is as follows: the spray mist uniformity analysis coefficient is compared with the spray mist uniformity comparison value obtained from the preset database: if the spray mist uniformity analysis coefficient is less than the spray mist uniformity comparison value, it is determined that the nozzle spray mist uniformity is qualified, otherwise it is determined that the nozzle spray mist uniformity is unqualified, and the corresponding nozzle optimization control strategy is adopted.

[0051] It needs to be understood that by comparing the spray mist uniformity analysis coefficient with the spray mist uniformity comparison value in the preset database, it is helpful to more accurately determine whether the spray mist uniformity of the nozzle used for pre-washing meets the requirements, so as to realize intelligent start of the nozzle optimization control strategy, and thereby improve the efficiency of hydrogen fluoride pre-washing.

[0052] Specifically, the spray mist uniformity comparison value is extracted from the preset database, and specifically, after the corresponding spray mist abnormal area is obtained by the preset staff based on the spray process parameters in the historical data, it is input into the specific limit expression of the spray mist uniformity analysis coefficient, the data set corresponding to the spray mist uniformity analysis coefficient is obtained, and the mean value operation is performed to obtain the value, which is recorded as the spray mist uniformity comparison value.

[0053] The specific implementation steps of the nozzle optimization control strategy are as follows:

[0054] Step one, adopt a preset control strategy to ensure that the nozzle spray remains uniform, thereby helping to improve the overall spray efficiency, and the preset control strategy means that the nozzle spray is regulated by an electric valve combined with frequency conversion control of the spray pressure.

[0055] It needs to be explained that the preset control strategy is specifically an "electric valve + frequency conversion control of spray pressure" control mode, which is the most practical and mature solution for the nozzle system to realize automatic regulation of spray uniformity and intensity. Essentially, the frequency conversion pump adjusts the liquid supply flow / pressure, the electric regulating valve finely controls the liquid inflow of each region or nozzle group, the sensor real-time monitors the pressure, flow, tail gas concentration, temperature, etc., and the PLC (Programmable Logic Controller System) automatically adjusts the pump frequency or electric valve opening degree according to the feedback data, finally realizing dynamic control of the liquid atomization state and spray uniformity.

[0056] Step two, obtain the chemical agent consumption data in the preset control strategy execution process through the flow meter, and the chemical agent consumption data includes hydrogen fluoride absorbent consumption, neutralizing agent consumption, cleaning liquid consumption and solvent consumption. Specifically, the flow meter specifically includes a hydrogen fluoride absorbent flow meter, a neutralizing agent flow meter, a cleaning liquid flow meter and a solvent flow meter.

[0057] Step three, comparing the chemical agent consumption data with the reference consumption data obtained from the preset database to determine whether the nozzle is abnormal, the reference consumption data including reference hydrogen fluoride absorbent consumption, reference neutralizing agent consumption, reference cleaning liquid consumption and reference solvent consumption; wherein the reference consumption data is specifically set by the preset staff based on the requirements of industrial production and stored in the preset database for automatic extraction when used.

[0058] In this embodiment, by comparing the chemical agent consumption data with the corresponding reference data, the abnormality of the nozzle caused by blockage, wear and other reasons is found early, and appropriate optimization measures are taken to avoid uneven spraying causing loss of pre-washing efficiency or damage to the equipment used for pre-washing, ensuring long-term stable operation of the equipment; at the same time, through accurate control and optimization of chemical agent consumption, unnecessary excessive consumption of the spraying system in the pre-washing process can be avoided, effectively reducing the raw material consumption rate of anhydrous hydrogen fluoride, and also reducing the burden on the environment; moreover, the chemical agent consumption data and nozzle performance feedback collected by the system can be used as an important data source for subsequent optimization and performance evaluation, promoting continuous optimization and improvement of the spraying system, and thus improving the overall work efficiency of anhydrous hydrogen fluoride production; in addition, by obtaining the chemical agent consumption data (such as hydrogen fluoride absorbent, neutralizing agent, cleaning liquid and solvent consumption) in real time and comparing it with the reference consumption data, it is beneficial to master the material consumption of the spraying process in the pre-washing process in real time, and to judge whether there is nozzle performance abnormality, and then to replace it in time to improve the pre-washing efficiency of hydrogen fluoride.

[0059] Further, the specific process of determining whether the nozzle is abnormal is as follows, including three cases:

[0060] The first case, if there is a single chemical agent consumption data greater than the corresponding reference consumption data, the corresponding nozzle abnormality determination value is obtained by combining the obtained pre-washing parameters to determine whether to generate a nozzle detection scheme, which further determines by combining the pre-washing parameters, so that the determination result is more convincing, the pre-washing parameters including spraying pressure, tower outlet temperature and total spraying liquid flow.

[0061] Specifically, the spraying pressure is measured by the pressure transmitter installed on the spraying main pipeline in front of the nozzle, the corresponding tower outlet temperature is obtained by the thermocouple installed on the main pipeline of the tower outlet gas, and the total spraying liquid flow is obtained by the flowmeter installed on the total spraying pump outlet pipeline.

[0062] The second case, if there is more than one chemical agent consumption data greater than the corresponding reference consumption data, since each chemical agent consumption data can be cross-validated, it is determined that the nozzle is abnormal and a nozzle detection scheme is generated.

[0063] In the third case, if each chemical consumption data is not greater than the corresponding reference consumption data, it indicates that the nozzle is normal, and a spraying fault warning is issued to prompt the preset worker to maintain the spray tower before the next preset analysis period.

[0064] In the embodiment, by comparing the chemical consumption data with the reference consumption, in combination with the spraying pressure, the tower outlet temperature and the pre-washing parameters of the spraying liquid flow, it is beneficial to more accurately judge whether the nozzle is abnormal, avoid the subjectivity of the traditional experience method, and improve the intelligent level of the fault detection of the hydrogen fluoride pre-washing process. If there is a single chemical consumption data greater than the reference consumption, in combination with the pre-washing parameters, it is judged whether to generate a nozzle detection scheme, and when multiple consumption data are abnormal, a detection scheme is directly generated, which realizes hierarchical and step-by-step diagnosis of nozzle problems, avoids unnecessary over-detection, and at the same time, through multiple judgment conditions (comparison of single and multiple consumption data), it helps to reduce the false positive rate and false negative rate of the pre-washing link, thereby improving the pre-washing efficiency and ensuring that only the nozzle that is truly abnormal can be detected, thereby avoiding excessive intervention and resource waste in the pre-washing link.

[0065] Further, the specific acquisition process of the nozzle abnormality judgment value is as follows:

[0066] Firstly, difference operation is performed on the chemical consumption data and the corresponding reference consumption data to obtain the chemical consumption deviation and perform data normalization processing, and at the same time, the pre-washing parameters are subjected to data normalization processing, so as to ensure that the dimensions of each data are unified during processing, wherein the difference operation represents difference operation on the chemical consumption data and the corresponding reference consumption data.

[0067] Secondly, nozzle abnormality judgment analysis data is obtained from the preset database, specifically including nozzle abnormality judgment analysis factors and pre-washing analysis factors, the nozzle abnormality judgment analysis factors including chemical consumption analysis factors and pre-washing parameter analysis factors, and the pre-washing analysis factors including spraying pressure analysis factors, tower outlet temperature analysis factors and total flow of spraying liquid analysis factors.

[0068] Specifically, the pre-washing parameters are input into the pre-washing mapping model which has been trained to output the corresponding pre-washing analysis factors, indicating the influence degree of the pre-washing parameters on the initial nozzle abnormality judgment value. The model is constructed by using a logistic regression algorithm, and is trained based on the least square method criterion through the statsmodels framework. The model training data includes the pre-washing parameters collected in the historical period and the pre-washing analysis factors set by the preset worker according to the experience rules, which are used to fit the mapping relationship between the pre-washing parameters and the initial nozzle abnormality judgment value.

[0069] Similarly, the initial nozzle abnormality determination value and the chemical agent consumption deviation are input into the trained nozzle abnormality analysis mapping model to output the corresponding nozzle abnormality determination analysis factor, which represents the influence degree of the initial nozzle abnormality determination value and the chemical agent consumption deviation on the nozzle abnormality determination value. The model is constructed using a logistic regression algorithm and is trained based on a cross-entropy loss criterion through a scikit-learn framework. The model training data includes the initial nozzle abnormality determination value, the chemical agent consumption deviation, and the nozzle abnormality determination analysis factor set by the preset staff according to the experience rule collected in the historical period, which is used to fit the mapping relationship between the initial nozzle abnormality determination value, the chemical agent consumption deviation, and the nozzle abnormality determination value.

[0070] In the third step, the pre-washing parameters and the corresponding pre-washing analysis factors are coupled after weighting operation to obtain the initial nozzle abnormality determination value.

[0071] Specifically, the expression of the initial nozzle abnormality determination value is as follows:

[0072] ;

[0073] wherein D represents the initial nozzle abnormality determination value, D1 represents the spraying pressure, D2 represents the tower outlet temperature, D3 represents the total flow of spraying liquid, μ1 represents the spraying pressure analysis factor, μ2 represents the tower outlet temperature analysis factor, μ3 represents the total flow of spraying liquid analysis factor,

[0074] In the fourth step, the initial nozzle abnormality determination value, the chemical agent consumption deviation, and the corresponding nozzle abnormality determination analysis factor are coupled after weighting operation to obtain the nozzle abnormality determination value, which is used to determine whether the corresponding nozzle is abnormal.

[0075] The specific limit expression of the nozzle abnormality determination value is as follows:

[0076] ;

[0077] wherein H represents the chemical agent consumption deviation, α represents the chemical agent consumption analysis factor, β represents the pre-washing parameter analysis factor, and η represents the nozzle abnormality determination value.

[0078] In the embodiment, the algorithm combines the chemical consumption deviation, the pre-washing parameters and the nozzle abnormality determination analysis data to analyze the nozzle abnormality determination value. As the tower outlet temperature increases, it indicates that the spray may not be evenly covered, and the absorption reaction may not be complete (such as insufficient absorption of hydrogen fluoride, and heat not taken away by the absorption liquid), so the possibility of nozzle abnormality is higher, and the nozzle abnormality determination value also increases. As the spraying pressure decreases, it indicates that the nozzle may be clogged or partially blocked, and the nozzle flow may be unstable, so the possibility of nozzle abnormality is higher, and the corresponding nozzle abnormality determination value also increases. Similarly, as the total flow of the spraying liquid decreases, it indicates that the nozzle may be clogged, so the possibility of nozzle abnormality is higher, and the corresponding nozzle abnormality determination value is higher. In addition, as the chemical consumption deviation increases, it indicates that the consumption of the chemical is abnormal, and the possibility of nozzle abnormality also increases, so the corresponding nozzle abnormality determination value also increases. By analyzing the nozzle abnormality determination value, it is helpful to more accurately determine whether the nozzle is abnormal during the pre-washing process, so as to timely detect and take corresponding nozzle optimization strategy, thereby improving the effect of uniformity of the spraying spray during the pre-washing process.

[0079] Further, the specific process of determining whether to generate a nozzle detection scheme is as follows: extracting a preset determination value from a preset database and comparing it with the nozzle abnormality determination value: if the nozzle abnormality determination value is greater than the preset determination value, it is determined that the nozzle is abnormal and a nozzle detection scheme is generated, otherwise a chemical consumption abnormality warning is given to prompt the preset worker to overhaul the spraying tower; the nozzle detection scheme includes nozzle installation correction, nozzle spacing verification, nozzle clogging detection and nozzle deformation observation warning.

[0080] It can be understood that by comparing the nozzle abnormality determination value with the preset determination value, the system can more accurately determine whether a corresponding nozzle detection scheme needs to be generated, thereby reducing the manual judgment error through this automatic process and ensuring that the detection program can be started in time when the nozzle performance is abnormal.

[0081] Specifically, the preset determination value is preset by the preset worker and stored in the preset database. According to the pre-washing parameters and the corresponding chemical consumption in the historical data, and by substituting them into the specific limit expression of the nozzle abnormality determination value, the corresponding data set is obtained, that is, the mean value of the data set is the preset determination value.

[0082] Among them, after sequentially executing the nozzle detection scheme, the scheme detection compliance is determined, and the specific steps are as follows:

[0083] F1 acquires the angle of each nozzle in real time and compares it with the preset nozzle angle obtained from the preset database. Based on the nozzle angle comparison results, the corresponding nozzle angle optimization measures are selected, which helps to improve the spray coverage efficiency and reduce the risk of cleaning dead corners. The nozzle angle is measured by an angle sensor.

[0084] F2 detects the distance between each nozzle and matches it with the initial distance set in the preset database. If there is a mismatch between the nozzle distance and the initial distance, the preset operator is prompted to adjust the distance in the next preset analysis cycle. Otherwise, the distance between the nozzles is detected again to prevent spray overlap or insufficient coverage due to the distance being too small or too large, thereby improving the process efficiency of pre-washing hydrogen fluoride. The nozzle distance is measured by a displacement sensor, and the initial distance is the distance between each nozzle before the spray tower is used.

[0085] F3 acquires nozzle clogging parameters to determine whether to replace the corresponding nozzle. The nozzle clogging parameters include inlet pressure, outlet pressure, and spray branch flow rate, avoiding misjudgment caused by fluctuations in a single parameter, improving the accuracy of judgment and the necessity of nozzle replacement. The inlet pressure is detected by a pressure transmitter installed on the branch pipe before the nozzle. The outlet is a free spray port, so the outlet pressure is close to the ambient pressure and is usually regarded as atmospheric pressure. The flow rate of each spray branch is measured by a small-diameter electromagnetic flow meter installed in each spray branch.

[0086] F4 issues a nozzle deformation warning, prompting the operator to inspect the nozzle after the current preset analysis cycle ends. If the nozzle is deformed, it should be replaced; otherwise, the equipment structure other than the nozzle should be inspected.

[0087] In summary, by executing the detection process sequentially through steps F1-F4, a closed-loop management system is achieved, encompassing the entire process from angle optimization, spacing adjustment, blockage detection to deformation warning. Each step has clearly defined input parameters and judgment criteria, ensuring the scientific and systematic nature of the nozzle detection process.

[0088] like Figure 6As shown, the flowchart is provided by the embodiment of the present application for performing the scheme detection compliance judgment, and the specific logic is as follows: first, the nozzle angle is acquired in real time, and compared with the preset nozzle angle, if there are more than a preset number of nozzle angles that are not within the corresponding preset nozzle angle range, the preset worker is prompted to perform spray tower structure detection to exclude factors causing abnormal chemical agent consumption; if there are less than a preset number of nozzle angles that are not within the corresponding preset nozzle angle range, the nozzle angle is automatically corrected to be within the preset nozzle angle range; if all the nozzle angles are within the corresponding preset nozzle angle range, the nozzle spacing verification is performed; then the nozzle spacing is detected, and matched with the initial spacing, if the nozzle spacing and the initial spacing are not matched, the preset worker is prompted to adjust the spacing in the next preset analysis period, otherwise the next nozzle spacing is continuously detected; then the nozzle clogging parameter is acquired for judgment, the inlet pressure and the outlet pressure of each nozzle are subjected to difference operation to obtain the inlet and outlet pressure difference, and the inlet and outlet pressure difference is compared with the pressure difference fluctuation interval, if the inlet and outlet pressure difference does not belong to the pressure difference fluctuation interval, it is judged that the nozzle is clogged and the corresponding nozzle is prompted to be replaced, otherwise the spray branch flow of each nozzle is judged with the flow minimum limit value, if the spray branch flow is less than the flow minimum limit value, it is judged that the nozzle is clogged and the corresponding nozzle is prompted to be replaced, otherwise the nozzle deformation warning is issued; finally, the nozzle deformation warning is issued, which is used to prompt the preset worker to check the nozzle after the current preset analysis period ends, if the nozzle is deformed, it is replaced, otherwise the equipment structure other than the nozzle is detected; through the above process, not only the abnormal situation of the nozzle can be timely and effectively determined and optimized, but also the internal reaction efficiency of the spray tower can be improved, and the sufficiency of the correlation between the spray effect and the spray system state during the analysis of the pre-washing process can be ensured.

[0089] It should be explained that the specific content of selecting the corresponding nozzle angle optimization measure based on the comparison result of the nozzle angle is as follows: if there are more than a preset number of nozzle angles that are not within the corresponding preset nozzle angle range, the preset worker is prompted to perform spray tower structure detection, the spray tower structure detection is used to detect the component structure other than the nozzle of the spray tower to exclude factors causing abnormal chemical agent consumption; if there are less than a preset number of nozzle angles that are not within the corresponding preset nozzle angle range, the nozzle angle is automatically corrected to be within the preset nozzle angle range; if all the nozzle angles are within the corresponding preset nozzle angle range, the nozzle spacing verification is performed; the above method responds to the number of nozzle angle deviations (more than a preset number / less than a preset number) in stages, which can not only cope with large-scale structural abnormalities, but also intelligently correct small-scale deviations, avoid chemical agent spraying errors or overlapping spraying, and improve the internal reaction efficiency of the spray tower.

[0090] Specifically, the preset nozzle angle range and the preset number are preset by a preset worker based on industrial production requirements and stored in a preset database.

[0091] It should be noted that the specific process of selecting whether to replace the corresponding nozzle is as follows: the inlet pressure and the outlet pressure of each nozzle are subjected to difference operation to obtain an inlet-outlet pressure difference, and the inlet-outlet pressure difference is compared with a pressure difference fluctuation interval stored in the preset database. If the inlet-outlet pressure difference does not belong to the pressure difference fluctuation interval, it is determined that the nozzle is blocked and the preset worker is prompted to replace the corresponding nozzle. Otherwise, the spray branch flow of each nozzle is compared with a preset database stored flow minimum limit value. If the spray branch flow is less than the flow minimum limit value, it is determined that the nozzle is blocked and the preset worker is prompted to replace the corresponding nozzle. Otherwise, a nozzle deformation warning is issued.

[0092] In the above process, the inlet-outlet pressure difference reflects the degree of unobstructedness of the nozzle, and the branch flow reflects the spray output capacity. The combination of the two is helpful to more comprehensively and stereoscopically judge the nozzle state, thereby improving the recognition rate of the system for complex blockage conditions.

[0093] Specifically, the pressure difference fluctuation interval and the flow minimum limit value are preset by a preset worker based on industrial production requirements and stored in a preset database.

[0094] In the present embodiment, if the nozzle anomaly does not reach the threshold value of triggering the detection scheme, the system will issue a chemical agent consumption anomaly warning, prompting the worker to check the situation of the spray tower. Through this early warning mechanism, potential nozzle failure problems in the pre-washing link can be found in advance, the downtime of the spray system is reduced, and the pre-washing efficiency is improved. The automatically generated nozzle detection scheme includes multiple important checks (such as nozzle installation correction, spacing verification, etc.), which checks the nozzle from multiple aspects to ensure that each link of the spray system is in the best working state, improves the efficiency of the pre-washing process and reduces the consumption of chemicals. At the same time, the comprehensiveness of the nozzle detection scheme (including installation correction, spacing verification, blockage detection, etc.) ensures that various common problems of the nozzle can be identified and solved in time, avoiding the situation that a single fault point is not detected, improving the stability of the spray system and the spraying effect, and ensuring the correlation between the analysis of the spraying effect and the state of the spray system during the pre-washing process.

[0095] The above-described embodiments can be implemented in whole or in part by software, hardware (such as a circuit), firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through a wired (for example, infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0096] It should be understood that the term "and / or" herein merely describes an association relationship of associated objects, which means that there can be three relationships, for example, A and / or B can represent three cases of A alone, A and B together, and B alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects, but can also represent an "and / or" relationship, which can be understood in the context.

[0097] In the present application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or the like means any combination of the items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0098] It should be understood that in various embodiments of the present application, the size of the sequence number of the above-described processes does not mean the order of execution, and the execution order of the processes should be determined by their functions and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0099] Those skilled in the art can clearly understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0100] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the devices, apparatuses and units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0101] In several embodiments provided by the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely schematic, for example, the division of units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0102] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0103] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit.

[0104] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0105] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A system for in-depth analysis and optimization of anhydrous hydrogen fluoride pre-washing data, characterized in that, The system includes a pre-wash data analysis module, a pre-wash nozzle detection module, and a pre-wash spray optimization module. The pre-washing data analysis module is used to monitor the pre-washing stage of the anhydrous hydrogen fluoride production process in real time and determine the uniformity of the spray to determine whether to adopt the corresponding nozzle optimization control strategy to maintain the uniformity of the nozzle spray of the spray tower spray system. The pre-wash nozzle detection module is used to determine whether there is an abnormality in the nozzle during the execution of the spray uniformity optimization control strategy, and to select whether to generate a nozzle detection scheme. The nozzle detection scheme means detecting the nozzle angle, spacing, blockage and deformation index. The pre-washing spray optimization module is used to determine the compliance of the nozzle detection scheme after executing the nozzle detection scheme in sequence, so as to determine whether to perform the corresponding nozzle optimization adjustment. The nozzle optimization adjustment is used to improve the qualification of the nozzle spray state. The steps for determining the uniformity of the spray pattern to decide whether to adopt a corresponding nozzle optimization control strategy are as follows: A1, Real-time acquisition of spray process parameters for a preset analysis cycle, including tail gas hydrogen fluoride concentration, spray zone pressure, and tower pressure; A2, The spray process parameters are visualized to obtain a spray process parameter curve, which includes a tail gas hydrogen fluoride concentration curve, a spray zone pressure curve, and a tower pressure curve. A3 marks the reference range of the spraying process parameters extracted from the preset database in the corresponding spraying process parameter curve graph; A4. Obtain the area of ​​the spray process parameter curve that exceeds the corresponding reference range within the preset analysis period to obtain the spray abnormal area. The spray abnormal area includes the abnormal area of ​​tail gas hydrogen fluoride concentration, the abnormal area of ​​spray zone pressure, and the abnormal area of ​​tower pressure. A5. The spray uniformity analysis coefficient is obtained based on the abnormal area of ​​the spray, and the corresponding nozzle optimization control strategy is determined according to the spray uniformity analysis coefficient. The spray uniformity analysis coefficient is used to quantify the spray uniformity of the nozzle within the current preset analysis period.

2. The anhydrous hydrogen fluoride pre-washing data depth analysis and optimization system according to claim 1, characterized in that, The specific process for obtaining the spray uniformity analysis coefficient based on the abnormal spray area is as follows: Extract the spray uniformity influencing factors and average reference area from the preset database. The spray uniformity influencing factors include the exhaust gas hydrogen fluoride concentration influencing factor, the spray zone pressure influencing factor and the tower pressure influencing factor. The average reference area includes the exhaust gas hydrogen fluoride concentration reference area, the spray zone pressure reference area and the tower pressure reference area. The spraying process parameters are quantified by quantifying the ratio of each abnormal spraying area to the corresponding average reference area. The spray uniformity analysis coefficient is obtained by coupling the quantitative index of spray process parameters with the corresponding spray uniformity influencing factors through weighted calculation.

3. The anhydrous hydrogen fluoride pre-washing data depth analysis and optimization system according to claim 2, characterized in that, The process of determining whether to adopt a corresponding nozzle optimization control strategy based on the spray uniformity analysis coefficient is as follows: The spray uniformity analysis coefficient was compared with the spray uniformity comparison value obtained from a preset database: If the spray uniformity analysis coefficient is less than the spray uniformity comparison value, the nozzle spray uniformity is deemed to be qualified; otherwise, the nozzle spray uniformity is deemed to be unqualified, and a corresponding nozzle optimization control strategy is adopted. The specific implementation steps of the nozzle optimization control strategy are as follows: Step 1: Adopt a preset control strategy, which means adjusting the nozzle spray by using an electric valve in combination with frequency conversion to control the spray pressure. Step 2: Obtain chemical agent consumption data during the execution of the preset control strategy. The chemical agent consumption data includes the consumption of hydrogen fluoride absorbent, neutralizer, cleaning solution, and solvent. Step 3: Compare the chemical agent consumption data with the reference consumption data obtained from the preset database to determine whether there is any abnormality in the nozzle. The reference consumption data includes the reference hydrogen fluoride absorbent consumption, reference neutralizer consumption, reference cleaning fluid consumption, and reference solvent consumption.

4. The anhydrous hydrogen fluoride pre-washing data depth analysis and optimization system according to claim 3, characterized in that, The specific process for determining whether the nozzle is abnormal is as follows: If a single chemical agent consumption data exceeds the corresponding reference consumption data, the corresponding nozzle anomaly judgment value is obtained by combining the acquired pre-washing parameters to determine whether to generate a nozzle detection scheme. The pre-washing parameters include spray pressure, tower outlet temperature, and total spray liquid flow rate. If more than one chemical agent consumption data exceeds the corresponding reference consumption data, the nozzle is determined to be abnormal and a nozzle detection plan is generated. If the consumption data of each chemical agent is not greater than the corresponding reference consumption data, a spraying fault warning will be issued to remind the pre-set personnel to inspect the spraying tower before the next pre-set analysis cycle.

5. The anhydrous hydrogen fluoride pre-washing data depth analysis and optimization system according to claim 4, characterized in that, The specific method for obtaining the nozzle anomaly determination value is as follows: The chemical agent consumption data and the corresponding reference consumption data are compared to obtain the chemical agent consumption deviation and then the data is normalized. At the same time, the pre-washing parameters are also normalized. The nozzle anomaly determination and analysis data are obtained from the preset database, specifically including nozzle anomaly determination and analysis factors and pre-washing analysis factors. The nozzle anomaly determination and analysis factors include chemical agent consumption analysis factors and pre-washing parameter analysis factors. The pre-washing analysis factors include spray pressure analysis factors, tower outlet temperature analysis factors and total spray liquid flow analysis factors. The pre-washing parameters are weighted and coupled with the corresponding pre-washing analysis factors to obtain the initial nozzle anomaly judgment value. The nozzle anomaly determination value is obtained by coupling the initial nozzle anomaly determination value, chemical consumption deviation and corresponding nozzle anomaly determination analysis factor after weighting operation.

6. The anhydrous hydrogen fluoride pre-washing data depth analysis and optimization system according to claim 4, characterized in that, The specific process for determining whether to generate a nozzle detection scheme is as follows: Retrieve preset judgment values ​​from the preset database and compare them with nozzle anomaly judgment values: If the nozzle abnormality judgment value is greater than the preset judgment value, the nozzle is judged to be abnormal and a nozzle detection plan is generated; otherwise, an abnormal chemical consumption warning is issued to prompt the preset personnel to inspect and repair the spray tower. The nozzle detection scheme includes nozzle installation and correction, nozzle spacing verification, nozzle blockage detection, and nozzle deformation observation and early warning.

7. The anhydrous hydrogen fluoride pre-washing data depth analysis and optimization system according to claim 1, characterized in that, The procedure for determining compliance with the nozzle testing scheme after sequential execution is as follows: F1: Real-time acquisition of each nozzle angle, comparison with preset nozzle angles obtained from a preset database, and selection of corresponding nozzle angle optimization measures based on the nozzle angle comparison results; F2 detects the distance between each nozzle and matches it with the initial distance set in the preset database. If there is a mismatch between the nozzle distance and the initial distance, the preset staff will be prompted to adjust the distance in the next preset analysis cycle; otherwise, the detection of the next nozzle distance will continue. F3, obtain nozzle clogging parameters for determination to select whether to replace the corresponding nozzle. The nozzle clogging parameters include inlet pressure, outlet pressure, and spray branch flow rate. F4 issues a nozzle deformation warning, prompting the operator to inspect the nozzle after the current preset analysis cycle ends. If the nozzle is deformed, it should be replaced; otherwise, the equipment structure other than the nozzle should be inspected.

8. The anhydrous hydrogen fluoride pre-washing data depth analysis and optimization system according to claim 7, characterized in that, The specific details of selecting the corresponding nozzle angle optimization measures based on the nozzle angle comparison results are as follows: If there are more nozzles than the preset number whose angles are outside the corresponding preset nozzle angle range, the operator will be prompted to perform a structural inspection of the spray tower to eliminate factors that may cause abnormal chemical consumption. If there are fewer than the preset number of nozzle angles that are outside the corresponding preset nozzle angle range, the nozzle angles will be automatically corrected to be within the preset nozzle angle range. If the angle of each nozzle is within the corresponding preset nozzle angle range, then the nozzle spacing is verified.

9. The anhydrous hydrogen fluoride pre-washing data depth analysis and optimization system according to claim 7, characterized in that, The specific procedure for selecting whether to replace the corresponding nozzle is as follows: The inlet and outlet pressures of each nozzle are calculated to obtain the inlet and outlet pressure difference. The inlet and outlet pressure difference is then compared with the pressure difference fluctuation range stored in the preset database. If the inlet and outlet pressure difference does not fall within the pressure difference fluctuation range, the nozzle is determined to be blocked and the preset personnel are prompted to replace the corresponding nozzle. Otherwise, the flow rate of each nozzle's spray branch is compared with the minimum flow rate stored in the preset database. If the flow rate of the spray branch is less than the minimum flow rate, the nozzle is determined to be blocked and the preset personnel are prompted to replace the corresponding nozzle. Otherwise, a nozzle deformation warning is issued.

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