Anhydrous hydrogen fluoride pre-washing data deep analysis and optimization system

Abstract

The invention discloses an 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. According to the method, the pre-washing link in the anhydrous hydrogen fluoride production process is monitored in real time, the spraying uniformity condition is judged to determine whether a nozzle optimization control strategy is adopted or not, then whether a nozzle is abnormal or not is judged in the execution process of the spraying uniformity optimization control strategy, and whether a nozzle detection scheme is generated or not is selected; finally, after the nozzle detection scheme is executed, scheme detection conformity judgment is carried out to determine whether corresponding nozzle optimization adjustment is carried out or not, so that the sufficient degree of association with the state of the spraying system when the spraying effect is analyzed in the pre-washing process is improved; the problem that in the prior art, when the spraying effect is analyzed in the pre-washing process, association with the state of a spraying system is insufficient is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of gas pre-washing supervision, and in particular to an anhydrous hydrogen fluoride pre-washing data in-depth analysis and optimization system. Background Art

[0002] In the prior art, the production process of anhydrous hydrogen fluoride generally includes raw material preparation, a fluorination reaction stage, a pre-washing step, a cooling stage, distillation and purification, by-product treatment, tail gas treatment, and finished product storage and packaging. In the pre-washing step, hydrogen fluoride gas is first introduced into a spray pre-washing tower. The hydrogen fluoride gas enters from the bottom of the tower and flows upward along the tower (countercurrent operation). The washing liquid (water, dilute sulfuric acid, or recycled hydrogen fluoride mother liquor) sprayed from the top of the tower fully contacts the hydrogen fluoride gas to remove water-soluble impurities and soluble impurities and small droplets in the gas. A demister (wire mesh demister or corrugated plate) is then installed at the top of the tower to further remove small droplets entrained in the airflow and prevent the liquid from entering the subsequent condensation or distillation system with the gas. Finally, the washing liquid is circulated and discharged.

[0003] For example, the Chinese invention patent with publication number CN116862069A discloses a method and system for optimizing greenhouse gas emission flow in an industrial park, including: 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 preprocessed greenhouse gas emission data of the industrial park; S4. Based on the results of data analysis, establishing an emission trend prediction model for greenhouse gas emissions in the industrial park to predict future emission trends; S5. Formulating corresponding greenhouse gas emission control strategies and plans based on the results of model prediction.

[0004] For example, the Chinese invention patent with announcement number: CN111915069B discloses a lightweight toxic and harmful gas distribution detection method based on deep learning, which includes: collecting the geomorphological environmental parameters and toxic and harmful gas parameters of the test site, constructing key features, preprocessing the data, offline training, model quantization training, normalized exponential regression, and predicting the three-dimensional distribution results of the contaminated site geomorphology and toxic and harmful gases, so as to determine the hazard level. The predicted results are sent to the cloud server platform, and the three-dimensional distribution of toxic and harmful gases at the contaminated site and the hazard level results are fed back.

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

[0006] In the actual production process, the nozzles used for spraying are used in industrial environments for a long time, and may be affected by a variety of industrial factors, causing problems with the nozzles and thus affecting the spraying effect. For example, due to vibration or impact during equipment operation, the nozzle angle may be offset or lose its centering, or the hydrogen fluoride or sulfuric acid contained in the washing liquid may corrode the metal or plastic structure of the nozzle, causing the nozzle diameter to expand or become eccentric. In addition, under long-term operation, the temperature in the tower changes repeatedly, and the metal pipe or nozzle bracket is deformed, causing the nozzle direction to slowly shift or even the spacing to be uneven. Therefore, when spraying crude hydrogen fluoride gas through the spray pre-wash tower, the nozzle may be uneven due to the nozzle. Angle problems cause insufficient spray coverage and many dead corners, or deviations in nozzle spacing cause the spray to deviate from the center and overlap and spray, resulting in liquid enrichment in some areas and liquid scarcity in some areas. There may also be changes in the spray angle, larger droplets, and local nozzles without liquid or deflected spray, resulting in uneven gas-liquid contact, which directly affects the absorption efficiency, liquid utilization rate and mass transfer effect in the tower. The existing technology solves the problem of uneven nozzle spraying by using electric valves and variable frequency control of spray pressure. However, in actual application scenarios, there is still a problem of insufficient correlation between the analysis of the spray effect during the pre-washing process and the status of the spray system. Summary of the Invention

[0007] In order to solve the technical problem in the prior art that the analysis of the spray effect during the pre-washing process is not sufficiently correlated with the spray system status, the embodiment of the present invention provides an in-depth analysis and optimization system for anhydrous hydrogen fluoride pre-washing data. The technical solution is as follows:

[0008] The anhydrous hydrogen fluoride pre-wash data in-depth analysis and optimization system includes a pre-wash data analysis module, a pre-wash nozzle detection module and a pre-wash spray optimization module; among them, the pre-wash data analysis module is used to monitor the pre-wash link of the anhydrous hydrogen fluoride production process in real time, and determine the uniformity of the spray spray to determine whether to adopt a corresponding nozzle optimization control strategy to maintain the uniformity of the nozzle spray of the spray tower's spray system; the pre-wash nozzle detection module is used to determine whether there is any abnormality in the nozzle during the execution of the spray uniformity optimization control strategy, and to select whether to generate a nozzle detection plan, which means detecting the angle, spacing, blockage and deformation indicators of the nozzle; the pre-wash spray optimization module is used to perform plan detection compliance judgment after executing the nozzle detection plan in sequence to determine whether to perform corresponding nozzle optimization adjustment, and the nozzle optimization adjustment is used to improve the qualification of the nozzle's spray state.

[0009] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0010] 1. By real-time monitoring of the pre-washing link of the anhydrous hydrogen fluoride production process and judging the uniformity of the spray spray, it is determined whether to adopt the corresponding nozzle optimization control strategy, thereby ensuring the uniformity of the nozzle spray of the spray tower's spray system. Then, during the execution of the spray uniformity optimization control strategy, it is determined whether there is any abnormality in the nozzle, and it is selected whether to generate a nozzle detection plan. It is realized to determine whether the abnormality of the nozzle leads to an increase in the consumption of the chemical agent used for spraying. Finally, after the nozzle detection plan is executed in sequence, the plan detection compliance is judged to determine whether the corresponding nozzle optimization adjustment is performed, thereby timely eliminating the influence of the nozzle factor on the spray effect, thereby reducing the consumption of the chemical agent used for spraying, and also improving the degree of correlation between the spray effect and the spray system status when analyzing the spray effect in the pre-washing process.

[0011] 2. The ratio of each abnormal spray area to the corresponding average reference area is quantified to obtain the spray process parameter quantification index, which achieves more accurate quantification of the degree of deviation between each abnormal spray area and the corresponding average reference area. Then, based on the spray process parameter quantification index and the corresponding spray uniformity influencing factor, a weighted operation is performed and coupled to obtain the spray uniformity analysis coefficient, thereby more accurately quantifying the spray uniformity degree of the nozzle in the current preset analysis period, thereby achieving accurate judgment of the spray uniformity and improving the efficiency of spray judgment.

[0012] 3. First, a 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. At the same time, the pre-washing parameters are normalized to help ensure that the dimensions of each data are consistent in subsequent processing. Then, the nozzle abnormality judgment analysis data is obtained from the preset database, and the pre-washing parameters and the corresponding pre-washing analysis factors are weighted and coupled to obtain the initial nozzle abnormality judgment value, which initially quantifies the degree of nozzle abnormality reflected in the pre-washing parameters. Finally, based on the initial nozzle abnormality judgment value, the chemical consumption deviation and the corresponding nozzle abnormality judgment analysis factor, a weighted operation is performed and coupled to obtain the nozzle abnormality judgment value, thereby realizing the comprehensive quantification of the nozzle abnormality degree, and then timely taking the corresponding nozzle optimization strategy, reducing the influence of the nozzle on the spraying effect during pre-washing. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 11 is a structural diagram of an anhydrous hydrogen fluoride pre-washing data in-depth analysis and optimization system provided by an embodiment of the present invention;

[0015] Figure 2 1 is a schematic diagram of a flow chart for determining the uniformity of a spray spray according to an embodiment of the present invention;

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

[0017] Figure 4 This is the second interface diagram of the pre-washing link of the anhydrous hydrogen fluoride intelligent monitoring system provided by an embodiment of the present invention;

[0018] Figure 5 This is the third interface diagram of the pre-washing link of the anhydrous hydrogen fluoride intelligent monitoring system provided by an embodiment of the present invention;

[0019] Figure 6 This is a flow chart of a method for determining compliance with a solution detection method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0021] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0022] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0023] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0024] The embodiment of the present invention provides an in-depth analysis and optimization system for anhydrous hydrogen fluoride pre-washing data. Figure 1 FIG. 1 is a structural diagram of an anhydrous hydrogen fluoride pre-washing data in-depth analysis and optimization system provided by an embodiment of the present invention. The processing flow of the system includes the following modules:

[0025] The pre-washing data analysis module is used to monitor the pre-washing process of the anhydrous hydrogen fluoride production process in real time and determine the uniformity of the spray spray to determine whether to adopt the corresponding nozzle optimization control strategy to maintain the uniformity of the nozzle spray of the spray tower's spray system; the pre-washing data analysis module determines whether to adopt the corresponding nozzle optimization control strategy to ensure that the nozzle spray of the spray tower always maintains uniformity.

[0026] The pre-wash nozzle detection module is used to determine whether there is any abnormality in the nozzle during the execution of the spray uniformity optimization control strategy, and to choose whether to generate a nozzle detection plan. The nozzle detection plan means detecting the angle, spacing, blockage and deformation indicators of the nozzle; through the pre-wash nozzle detection module, it is possible to more accurately judge whether there is an abnormality in the nozzle, which leads to an increase in the consumption of chemicals used for spraying, and then take corresponding optimization measures in time to reduce the consumption of chemicals.

[0027] The pre-wash spray optimization module is used to perform scheme detection compliance judgment after executing the nozzle detection scheme in sequence to determine whether to perform corresponding nozzle optimization adjustment. The scheme detection compliance judgment is used to judge whether the detection indicators of each nozzle meet the preset standards, and the nozzle optimization adjustment is used to improve the eligibility of the nozzle's spray state; determining whether to perform nozzle optimization adjustment through the pre-wash spray optimization module is not only conducive to timely eliminating the influence of nozzle factors on the spray effect, but also reduces the consumption of chemicals used for spraying during the pre-washing process.

[0028] In this embodiment, through the gradual execution and synergy of the pre-wash data analysis module, the pre-wash nozzle detection module and the pre-wash spray optimization module, not only can it be more efficiently determined whether there is an abnormality in the nozzle during the pre-wash process, thereby improving the pre-wash efficiency of the nozzle spray, but it also helps to improve the degree of correlation between the analysis of the spray effect during the pre-wash process and the status of the spray system.

[0029] It should be noted that before designing a deep data analysis and optimization system for anhydrous hydrogen fluoride pre-washing, professional technicians usually first build a preset database to support the operation of various control strategies. This database integrates a number of key process parameter information, including the reference range of spray process parameters, spray spray uniformity influencing factors and average reference area, spray uniformity comparison value, reference consumption data, nozzle abnormality judgment analysis data, preset judgment value, preset nozzle angle, initial spacing, pressure difference fluctuation range and flow rate minimum limit. These parameters are all set in advance by personnel with professional and technical backgrounds based on the analysis method adopted and the on-site hardware configuration conditions. This preset database provides the core data foundation for subsequent automated processes such as data uploading, storage optimization, and screening and judgment.

[0030] like Figure 2 As shown, it is a flow chart of determining the uniformity of the spray spray provided by an embodiment of the present invention. The specific logic is: first, the spray process parameters are collected in real time, and the spray process parameters are visualized to obtain a spray process parameter curve chart; then, the reference range of the spray process parameters is marked in the corresponding spray process parameter curve chart; then, the image area exceeding the corresponding reference range in the spray process parameter curve chart is obtained to obtain the spray abnormal area; finally, the spray spray uniformity analysis coefficient is obtained based on the spray abnormal area, and the spray spray uniformity analysis coefficient is compared with the spray uniformity comparison value: if the spray spray uniformity analysis coefficient is less than the spray uniformity comparison value, the nozzle spray uniformity is determined to be qualified; otherwise, the nozzle spray uniformity is determined to be unqualified, and a corresponding nozzle optimization control strategy is adopted; through the above process, not only the spray spray uniformity degree of the nozzle in the current preset analysis period during the pre-washing process can be quantified more accurately, but also the corresponding optimization strategy can be adopted in time to improve the efficiency of the pre-washing process.

[0031] Furthermore, the uniformity of the spray is determined to determine whether to adopt the corresponding nozzle optimization control strategy. The specific steps are as follows:

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

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

[0034] A2. Visualize the spray process parameters to obtain a spray process parameter curve chart, making the spray system operation status more intuitive and transparent, and facilitating operators to grasp the dynamics of the spray system. The spray process parameter curve chart includes the tail gas hydrogen fluoride concentration curve chart, the spray area pressure curve chart, and the tower pressure curve chart. The curve chart is drawn by using software tools (such as Excel, Python, R, Tableau, etc.).

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

[0036] A4. Obtain the image area exceeding the corresponding reference range in the spray process parameter curve 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.

[0037] It should be added that the cumulative abnormal value is obtained by accumulating the values ​​of the spray process parameters that do not fall within the reference range at each moment, and the corresponding spray abnormal area is obtained by multiplying the cumulative abnormal value with the sampling time interval of the corresponding spray process parameter.

[0038] A5, based on the spray abnormal area, obtains the spray uniformity analysis coefficient, and determines whether to adopt the corresponding nozzle optimization control strategy according to the spray uniformity analysis coefficient. The spray uniformity analysis coefficient is used to quantify the spray uniformity degree of the nozzle in the current preset analysis period.

[0039] In this embodiment, by accurately identifying the uneven spray area and timely adjusting the nozzle configuration, the spray coverage efficiency in the spray tower involved in pre-washing is effectively improved, which not only reduces the waste of spray liquid used for pre-washing, but also improves the exhaust gas treatment effect after pre-washing; at the same time, by early identification of problems such as nozzle blockage, unreasonable distribution or abnormal pressure difference during the pre-washing process, it helps to avoid local failure of the spray system, thereby reducing the risk of excessive exhaust emissions and equipment corrosion; and the quantitative index system established based on the spray abnormal area and the spray uniformity analysis coefficient more accurately determines the uniformity of the spray in the spray area during the pre-washing process.

[0040] As a further solution, the spray uniformity analysis coefficient is obtained based on the spray abnormal area. The specific process is as follows:

[0041] Firstly, the spray uniformity influencing factors and the average reference area are extracted from the preset database. The spray uniformity influencing factors include the exhaust gas hydrogen fluoride concentration influencing factor, the spray area pressure influencing factor and the tower pressure influencing factor. The average reference area includes the exhaust gas hydrogen fluoride concentration reference area, the spray area pressure reference area and the tower pressure reference area.

[0042] It should be noted that the spray abnormality area is input into the trained spray analysis mapping model to output the corresponding spray uniformity influencing factor, which indicates the degree of influence of each spray abnormality area on the spray uniformity analysis coefficient. The model is constructed using a logistic regression algorithm and trained using the scikit-learn framework based on the cross-entropy loss criterion. The model training data includes the spray abnormality area obtained in the historical period and the spray uniformity influencing factor set by the preset staff according to the empirical rules, which is used to fit the mapping relationship between the spray abnormality area and the spray uniformity analysis coefficient; in addition, the average reference area is obtained by obtaining the spray abnormality area of ​​each preset analysis period in the historical time period and performing the mean operation. Therefore, the average reference area is not 0.

[0043] Next, the ratio of each abnormal spraying area to the corresponding average reference area is quantified to obtain a quantitative index of the spraying process parameter; wherein the ratio quantification means performing a ratio operation on each abnormal spraying area to the corresponding average reference area.

[0044] Finally, the spray uniformity analysis coefficient is obtained by coupling the weighted operation based on the quantitative indicators of the spray process parameters and the corresponding spray uniformity influencing factors.

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

[0046] ;

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

[0048] In this embodiment, the algorithm combines the spray abnormality area, the average reference area, and the corresponding spray uniformity influencing factor for analysis to obtain a spray uniformity analysis coefficient. In the formula, when the ratio of each spray abnormality area to the corresponding average reference area is larger, the deviation degree of the corresponding spray process parameter is higher. However, as the quantitative index of the spray process parameter increases, the larger the spray abnormality area is, the larger the corresponding spray uniformity analysis coefficient is, indicating that the uniformity degree of the spray spray is likely to be lower, and vice versa, the uniformity degree of the spray spray is likely to be higher. By analyzing the spray uniformity analysis coefficient, it is helpful to more accurately analyze the spray uniformity effect of the nozzle in the spray tower during the pre-washing of hydrogen fluoride, and then promptly adopt the corresponding spray spray control strategy to maintain the uniformity effect of the spray spray, so that the pre-washing efficiency of hydrogen fluoride is more stable, and further improve the degree of correlation between the analysis of the spray effect and the state of the spray system during the pre-washing process.

[0049] like Figure 3 As shown, it is one of the interface schematic diagrams of the pre-washing link of the anhydrous hydrogen fluoride intelligent monitoring system provided by an embodiment of the present invention. The left side of the figure is the side navigation bar part of the system, including real-time monitoring, parameter analysis, alarm management, data management, daily operation, inspection records, equipment management, safety and environmental protection modules, user permissions and system settings. There is also a process progress display above the interface diagram, including raw material preparation, fluorination reaction stage, pre-washing link, cooling stage, distillation and purification, by-product treatment, tail gas treatment and finished product storage and packaging. The current interface display process is in the pre-washing link. In 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 amount and nozzle spacing correction amount, etc., and the relevant display of pre-washing status and temperature trend is in Figure 4 It is reflected in Figure 4 This is the second interface diagram of the pre-washing link of the anhydrous hydrogen fluoride intelligent monitoring system provided by the embodiment of the present invention. The equipment operation status and spray uniformity index indicator area are displayed in Figure 5 In the interface diagram shown, Figure 5 This is the third interface diagram of the pre-washing link of the anhydrous hydrogen fluoride intelligent monitoring system provided by the embodiment of the present invention. Figure 3 、 Figure 4 as well as Figure 5 The interface comprehensively displays the parameters of the pre-washing process, the real-time status of the equipment, the uniformity of the spray in the washing tower, and the corresponding nozzle status. The interface diagram helps the anhydrous hydrogen fluoride production personnel responsible for monitoring the pre-washing process to better understand the specific status of the equipment during pre-washing, and can promptly assist the equipment in corresponding nozzle optimization, thereby ensuring the efficiency of the anhydrous hydrogen fluoride pre-washing process.

[0050] Furthermore, whether to adopt the corresponding nozzle optimization control strategy is determined according to the spray uniformity analysis coefficient. The specific process is as follows: compare the spray uniformity analysis coefficient with the spray uniformity comparison value obtained from the preset database: if the spray uniformity analysis coefficient is less than the spray uniformity comparison value, the nozzle spray uniformity is judged to be qualified; otherwise, the nozzle spray uniformity is judged to be unqualified, and the corresponding nozzle optimization control strategy is adopted.

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

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

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

[0054] Step 1: A preset control strategy is adopted to ensure that the nozzle spray remains uniform, which helps to improve the overall spray efficiency. The preset control strategy means that the nozzle spray is regulated by an electric valve combined with variable frequency control of the spray pressure.

[0055] It should be explained that the preset control strategy is specifically the "electric valve + variable frequency control spray pressure" control method, which is currently the most practical and mature solution for the nozzle system to achieve automatic control of spray uniformity and intensity. This method essentially uses a variable frequency pump to adjust the liquid supply flow / pressure, and the electric regulating valve finely controls the liquid inlet flow of each area or nozzle group. The sensor monitors the pressure, flow, exhaust gas concentration, temperature, etc. in real time. The PLC (Programmable Logic Controller System) automatically adjusts the pump frequency or electric valve opening according to the feedback data, ultimately achieving dynamic control of the liquid atomization state and spray uniformity.

[0056] Step 2: Flow meters are used to obtain chemical consumption data during the execution of the preset control strategy. The chemical consumption data includes hydrogen fluoride absorption liquid consumption, neutralizer consumption, cleaning liquid consumption, and solvent consumption. Specifically, the flow meters include hydrogen fluoride absorption liquid flow meters, neutralizer flow meters, cleaning liquid flow meters, and solvent flow meters.

[0057] Step three: Compare the chemical 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 reference hydrogen fluoride absorption liquid consumption, reference neutralizer consumption, reference cleaning liquid consumption, and reference solvent consumption. Among them, the reference consumption data is specifically set by the preset staff based on industrial production requirements and stored in the preset database for automatic extraction when used.

[0058] In this embodiment, by comparing chemical consumption data with corresponding reference data, nozzle anomalies caused by clogging, wear, etc. are detected early, and corresponding optimization measures can be taken. This avoids pre-washing efficiency loss or damage to pre-washing equipment caused by uneven spraying, thereby ensuring long-term stable operation of the equipment. Furthermore, precise control and optimization of chemical consumption helps avoid unnecessary excessive consumption of the spray system during the pre-washing process, effectively reducing the raw material consumption rate of anhydrous hydrogen fluoride and the burden on the environment. Furthermore, the chemical consumption data and nozzle performance feedback collected by the system can serve as an important data source for subsequent optimization and performance evaluation, promoting the continuous optimization and improvement of the spray system, thereby improving the overall efficiency of anhydrous hydrogen fluoride production. Furthermore, by acquiring real-time chemical consumption data (such as the consumption of hydrogen fluoride absorption liquid, neutralizer, cleaning liquid, and solvent) and comparing it with reference consumption data, it is beneficial to monitor the material consumption of the spray process during the pre-washing process in real time, determine whether there are any nozzle performance anomalies, and then promptly replace them to improve the hydrogen fluoride pre-washing efficiency.

[0059] Furthermore, the specific process of determining whether the nozzle is abnormal is as follows, including three types of situations:

[0060] In the first case, if there is a single chemical agent consumption data that is greater than the corresponding reference consumption data, the corresponding nozzle abnormality judgment value is obtained in combination with the obtained pre-washing parameters to determine whether to generate a nozzle detection plan. Further judgment is made by combining the pre-washing parameters to make the judgment result more convincing. The pre-washing parameters include spray pressure, tower outlet temperature and total flow rate of spray liquid.

[0061] Specifically, the spray pressure is measured by a pressure transmitter installed on the spray main pipeline in front of the nozzle, the corresponding tower outlet temperature is obtained by a thermocouple installed on the main pipeline of the spray tower outlet gas, and the total spray liquid flow is obtained by measuring the overall spray liquid flow rate with a flow meter installed on the spray pump outlet pipeline.

[0062] In the second case, if there is more than one chemical consumption data that is greater than the corresponding reference consumption data, since the chemical consumption data can be cross-verified, the nozzle is determined to be abnormal and a nozzle detection plan is generated.

[0063] In the third case, if the consumption data of each chemical agent is not greater than the corresponding reference consumption data, it means that there is no abnormality in the nozzle, and a spray failure warning is issued to prompt the preset staff to inspect the spray tower before the next preset analysis cycle.

[0064] In this embodiment, by comparing chemical consumption data with reference consumption, combined with pre-wash parameters such as spray pressure, tower outlet temperature, and spray liquid flow rate, it is beneficial to more accurately determine whether there is an abnormality in the nozzle, avoid the subjectivity of traditional empirical judgment methods, and improve the intelligent level of fault detection in the hydrogen fluoride pre-washing process. If there is a single chemical consumption data that is greater than the reference consumption, the pre-washing parameters are combined to determine whether to generate a nozzle detection plan. When multiple consumption data are abnormal, a detection plan is directly generated, achieving hierarchical and step-by-step diagnosis of nozzle problems and avoiding unnecessary over-detection. At the same time, the use of multiple judgment conditions (comparison of single and multiple consumption data) helps to reduce the false alarm rate and missed alarm rate of the pre-washing link, thereby improving the pre-washing efficiency and ensuring that only nozzles that are truly abnormal are detected accordingly, thereby avoiding excessive intervention and waste of resources in the pre-washing link.

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

[0066] In the first step, a 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. At the same time, the pre-washing parameters are normalized to ensure that the dimensions of each data are unified when processing. Among them, the difference operation means performing a difference operation on the chemical consumption data and the corresponding reference consumption data.

[0067] The second step is to obtain nozzle abnormality judgment analysis data from the preset database, which specifically includes nozzle abnormality judgment analysis factors and pre-washing analysis factors. The nozzle abnormality judgment analysis factors include chemical 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 spray liquid total flow analysis factors.

[0068] Specifically, the pre-wash parameters are input into a trained pre-wash mapping model to output corresponding pre-wash analysis factors, representing the degree of influence of the pre-wash parameters on the initial nozzle abnormality determination value. This model is constructed using a logistic regression algorithm and trained using the statsmodels framework based on the least squares criterion. The model training data includes pre-wash parameters collected over a historical period and pre-wash analysis factors set by staff based on empirical rules. This is used to fit the mapping relationship between the pre-wash parameters and the initial nozzle abnormality determination value.

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

[0070] In the third step, the pre-washing parameters are weighted and coupled with the corresponding pre-washing analysis factors to obtain the initial nozzle abnormality judgment value.

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

[0072] ;

[0073] Where D represents the initial nozzle abnormality judgment value, D1 represents the spray pressure, D2 represents the tower outlet temperature, D3 represents the total flow rate of the spray liquid, μ1 represents the spray pressure analysis factor, μ2 represents the tower outlet temperature analysis factor, and μ3 represents the total flow rate analysis factor of the spray liquid.

[0074] In the fourth step, a weighted operation is performed based on the initial nozzle abnormality judgment value, the chemical consumption deviation and the corresponding nozzle abnormality judgment analysis factor, and then coupled to obtain a nozzle abnormality judgment value. The nozzle abnormality judgment value is used to determine whether the corresponding nozzle has an abnormality.

[0075] The specific limiting expression of the nozzle abnormality judgment value is as follows:

[0076] ;

[0077] Where H represents the chemical consumption deviation, α represents the chemical consumption analysis factor, β represents the pre-wash parameter analysis factor, and η represents the nozzle abnormality judgment value.

[0078] In this embodiment, the algorithm combines chemical consumption deviation, pre-wash parameters, and nozzle abnormality determination analysis data to analyze and determine a nozzle abnormality determination value. Here, as the tower outlet temperature increases, it indicates that the spray coverage may be uneven and the absorption reaction may be incomplete (e.g., hydrogen fluoride is not fully absorbed and heat is not removed by the absorption liquid). The corresponding nozzle abnormality probability increases, and the nozzle abnormality determination value also increases accordingly. As the spray pressure decreases, it indicates that the nozzle may be clogged or partially blocked, and the nozzle flow rate may be unstable. The probability of nozzle abnormality increases, and the corresponding nozzle abnormality determination value also increases. Similarly, as the total spray liquid flow rate decreases, it indicates that the nozzle may be clogged, and the probability of nozzle abnormality increases, and the corresponding nozzle abnormality determination value also increases. Furthermore, as the chemical consumption deviation increases, it indicates abnormal chemical consumption, and the probability of a nozzle problem also increases, and the corresponding nozzle abnormality determination value also increases. Analyzing the nozzle abnormality determination value helps to more accurately determine whether a nozzle abnormality occurs during the pre-wash process, thereby enabling timely detection and implementing corresponding nozzle optimization strategies, thereby improving the spray uniformity during the pre-wash process.

[0079] Furthermore, the specific process for determining whether to generate a nozzle detection plan is as follows: extract a preset judgment value from a preset database and compare it with the nozzle abnormality judgment value: if the nozzle abnormality judgment value is greater than the preset judgment value, it is determined that the nozzle is abnormal and a nozzle detection plan is generated, otherwise a chemical consumption abnormality warning is issued to prompt the preset staff to inspect the spray tower; the nozzle detection plan includes nozzle installation correction, nozzle spacing verification, nozzle blockage detection and nozzle deformation observation warning.

[0080] It can be understood that by comparing the nozzle abnormality judgment value with the preset judgment value, the system can more accurately determine whether it is necessary to generate a corresponding nozzle detection plan. This automated process reduces manual judgment errors and ensures that the detection program can be started in time when the nozzle performance is abnormal.

[0081] Specifically, the preset judgment value is pre-set by the preset staff and stored in the preset database. According to the pre-washing parameters and the corresponding chemical consumption in the historical data, the specific restriction expression of the nozzle abnormality judgment value is substituted and processed to obtain the corresponding data set, that is, the mean of the data set is the preset judgment value.

[0082] Among them, after executing the nozzle detection scheme in sequence, the scheme detection compliance is determined. The specific steps are as follows:

[0083] F1, real-time acquisition of each nozzle angle, and compared with the preset nozzle angle obtained from the preset database, based on the nozzle angle comparison results to select the corresponding nozzle angle optimization measures, 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 spacing between each nozzle and matches it with the initial spacing set in the preset database. If the nozzle spacing does not match the initial spacing, the preset staff will be prompted to adjust the spacing in the next preset analysis cycle. Otherwise, continue to detect the next nozzle spacing to prevent spray overlap or insufficient coverage due to too small or too large spacing, thereby improving the process efficiency of pre-washing hydrogen fluoride. The nozzle spacing is measured by a displacement sensor, and the initial spacing is the spacing between the nozzles before the spray tower is used.

[0085] F3, obtains the nozzle blockage parameters to determine whether to replace the corresponding nozzle. The nozzle blockage parameters include inlet pressure, outlet pressure and spray branch flow rate, avoiding misjudgment due to fluctuations in a single parameter, improving judgment accuracy and the necessity of nozzle replacement. Among them, the inlet pressure is detected by the pressure transmitter installed in the branch pipe in front of the nozzle. The outlet is a free jet, 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 flowmeter installed in each spray branch.

[0086] F4 issues a nozzle deformation warning to prompt the preset staff to check the nozzle after the current preset analysis cycle is completed. If the nozzle is deformed, it should be replaced. Otherwise, the equipment structure other than the nozzle should be inspected.

[0087] In summary, the inspection process is executed in sequence through steps F1-F4, realizing closed-loop management of the entire process from angle optimization, spacing adjustment, blockage judgment to deformation warning. Each link has clear input parameters and judgment criteria, ensuring the scientific and systematic nature of the nozzle inspection process.

[0088] like Figure 6As shown, it is a flow chart of the scheme detection compliance determination provided by an embodiment of the present invention, and the specific logic is: first, each nozzle angle is obtained in real time, and compared with the preset nozzle angle. If there are nozzle angles exceeding the preset number that are not within the corresponding preset nozzle angle range, the preset staff is prompted to perform spray tower structure detection to eliminate factors that cause abnormal chemical consumption; if there are nozzle angles less than the preset number 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 each nozzle angle is 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 there is a nozzle spacing that does not match the initial spacing, the preset staff is prompted to adjust the spacing in the next preset analysis cycle, otherwise continue to detect the next nozzle spacing; then obtain the nozzle blockage parameter for judgment, and the inlet pressure of each nozzle is The inlet and outlet pressure differences are obtained by performing a difference operation with the outlet pressure, and the inlet and outlet pressure differences are compared with the pressure difference fluctuation range. If the inlet and outlet pressure differences do not fall within the pressure difference fluctuation range, the nozzle is determined to be blocked and the preset staff is prompted to replace the corresponding nozzle. Otherwise, the spray branch flow of each nozzle is judged against the minimum flow limit. If the spray branch flow is less than the minimum flow limit, the nozzle is determined to be blocked and the preset staff is prompted to replace the corresponding nozzle, otherwise a nozzle deformation warning is issued; finally, a nozzle deformation warning is issued to prompt the preset staff to check the nozzle after the current preset analysis cycle is completed. If the nozzle is deformed, it is replaced, otherwise the equipment structure other than the nozzle is inspected; through the above process, it is not only helpful to promptly determine whether there is an abnormality in the nozzle and perform timely optimization processing, but also to improve the internal reaction efficiency of the spray tower, and ensure that the spray effect is fully associated with the spray system status when analyzing the pre-washing process.

[0089] It needs to be explained that the specific contents of selecting the corresponding nozzle angle optimization measures based on the nozzle angle comparison results are as follows: if there are nozzle angles exceeding the preset number and not within the corresponding preset nozzle angle range, the preset staff will be prompted to perform spray tower structure inspection. The spray tower structure inspection is used to inspect the components other than the spray tower nozzles to eliminate factors that cause abnormal chemical consumption; if there are nozzle angles less than the preset number and not within the corresponding preset nozzle angle range, the nozzle angles will be automatically corrected to within the preset nozzle angle range; if all nozzle angles are within the corresponding preset nozzle angle range, the nozzle spacing verification will be performed; the above method responds in a graded manner according to the number of nozzle angle deviations (exceeding the preset number / below the preset number), which can not only deal with large-scale structural abnormalities, but also intelligently correct small-scale deviations, avoid chemical spraying misunderstandings or overlapping spraying, and improve the reaction efficiency inside the spray tower.

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

[0091] It should be added that the specific process of choosing whether to replace the corresponding nozzle is: perform the difference calculation on the inlet pressure and outlet pressure of each nozzle to obtain the inlet and outlet pressure difference, and compare the inlet and outlet pressure difference 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 staff is prompted to replace the corresponding nozzle. Otherwise, the spray branch flow of each nozzle is compared with the minimum flow limit stored in the preset database. If the spray branch flow is less than the minimum flow limit, the nozzle is determined to be blocked and the preset staff is prompted to replace the corresponding nozzle. Otherwise, a nozzle deformation warning is issued.

[0092] In the above process, the inlet and outlet pressure difference reflects the degree of nozzle unobstructedness, and the branch flow reflects the spray output capacity. The combination of the two helps to judge the nozzle status more comprehensively and three-dimensionally, thereby improving the system's recognition rate of complex blockage situations.

[0093] Specifically, the pressure difference fluctuation range and the minimum flow rate limit are pre-set by the preset staff based on industrial production requirements and stored in the preset database.

[0094] In this embodiment, if the nozzle abnormality does not reach the threshold for triggering the detection scheme, the system will issue an abnormal chemical consumption warning, prompting the staff to check the condition of the spray tower. Through this early warning mechanism, potential nozzle failure problems in the pre-wash link can be discovered in advance, reducing the downtime of the spray system and improving the pre-wash efficiency. The automatically generated nozzle detection scheme includes a number of important inspections (such as nozzle installation correction, spacing verification, etc.), and checks the nozzles from multiple aspects to ensure that all links of the spray system are in optimal working condition, improving the efficiency of the pre-wash process and reducing 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 nozzle problems can be identified and resolved in a timely manner, avoiding single failure points from going undetected, improving the stability and spraying effect of the spray system, and ensuring the correlation between the spraying effect and the spray system status when analyzing the pre-washing process.

[0095] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0096] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0097] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0098] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0099] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians 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 invention.

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

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

[0102] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0103] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0104] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0105] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. Anhydrous hydrogen fluoride pre-washing data in-depth analysis and optimization system, characterized by: 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 process of the anhydrous hydrogen fluoride production process in real time and determine the uniformity of the spray 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 any abnormality in the nozzle during the execution of the spray uniformity optimization control strategy, and to select whether to generate a nozzle detection plan, which detects the angle, spacing, blockage and deformation indicators of the nozzle; The pre-wash spray optimization module is used to perform scheme detection compliance judgment after executing the nozzle detection scheme in sequence to determine whether to perform corresponding nozzle optimization adjustment. The nozzle optimization adjustment is used to improve the eligibility of the nozzle's spray state.

2. The anhydrous hydrogen fluoride pre-wash data in-depth analysis and optimization system according to claim 1, characterized in that: The determination of the uniformity of the spray spray to determine whether to adopt the corresponding nozzle optimization control strategy is carried out in the following specific steps: A1, real-time collection of spray process parameters of a preset analysis period, wherein the spray process parameters include tail gas hydrogen fluoride concentration, spray zone pressure, and tower pressure; A2, visualizing the spray process parameters to obtain a spray process parameter curve graph, wherein the spray process parameter curve graph includes a tail gas hydrogen fluoride concentration curve graph, a spray zone pressure curve graph, and a tower pressure curve graph; A3, marking the reference range of the spray process parameters extracted from the preset database on the corresponding spray process parameter curve graph; A4, obtaining the image area exceeding the corresponding reference range in the spray process parameter curve graph within the preset analysis period to obtain the spray abnormal area, wherein 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, based on the spray abnormal area, obtains the spray uniformity analysis coefficient, and determines whether to adopt the corresponding nozzle optimization control strategy according to the spray uniformity analysis coefficient. The spray uniformity analysis coefficient is used to quantify the spray uniformity degree of the nozzle in the current preset analysis period.

3. The anhydrous hydrogen fluoride pre-wash data in-depth analysis and optimization system according to claim 2, characterized in that: The spray uniformity analysis coefficient is obtained based on the abnormal spray area. The specific process is as follows: Extracting spray uniformity influencing factors and average reference areas from a preset database, wherein the spray uniformity influencing factors include tail gas hydrogen fluoride concentration influencing factors, spray area pressure influencing factors, and tower pressure influencing factors, and the average reference area includes tail gas hydrogen fluoride concentration reference area, spray area pressure reference area, and tower pressure reference area; The spray process parameter quantitative index is obtained by quantifying the ratio of each abnormal spray area to the corresponding average reference area; The spray uniformity analysis coefficient is obtained by coupling the weighted operation based on the quantitative indicators of spray process parameters and the corresponding spray uniformity influencing factors.

4. The anhydrous hydrogen fluoride pre-washing data in-depth analysis and optimization system according to claim 3, characterized in that: The specific process of determining whether to adopt the corresponding nozzle optimization control strategy based on the spray uniformity analysis coefficient is as follows: Compare the shower spray uniformity analysis coefficient with the spray uniformity comparison value obtained from the preset database: If the spray uniformity analysis coefficient is less than the spray uniformity comparison value, the nozzle spray uniformity is judged to be qualified, otherwise the nozzle spray uniformity is judged to be unqualified, and the corresponding nozzle optimization control strategy is adopted; The specific implementation steps of the nozzle optimization control strategy are as follows: Step 1: adopting a preset control strategy, wherein the preset control strategy is to regulate the nozzle spray through an electric valve combined with a frequency conversion control spray pressure; Step 2: obtaining chemical agent consumption data during the execution of the preset control strategy, wherein the chemical agent consumption data includes hydrogen fluoride absorption liquid consumption, neutralizer consumption, cleaning liquid consumption, and solvent consumption; Step three: Compare the chemical consumption data with reference consumption data obtained from a preset database to determine whether there is any abnormality in the nozzle. The reference consumption data includes reference hydrogen fluoride absorption liquid consumption, reference neutralizer consumption, reference cleaning liquid consumption, and reference solvent consumption.

5. The anhydrous hydrogen fluoride pre-washing data in-depth analysis and optimization system according to claim 4, characterized in that: The specific process of determining whether the nozzle is abnormal is as follows: If there is a single chemical agent consumption data that is greater than the corresponding reference consumption data, then the corresponding nozzle abnormality judgment value is obtained in combination with the obtained pre-wash parameters to determine whether to generate a nozzle detection plan. The pre-wash parameters include spray pressure, tower outlet temperature, and total spray liquid flow rate; If there is more than one chemical agent consumption data greater than the corresponding reference consumption data, it is determined that the nozzle is 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 spray failure warning is issued to prompt the preset staff to inspect the spray tower before the next preset analysis cycle.

6. The anhydrous hydrogen fluoride pre-wash data in-depth analysis and optimization system according to claim 5, characterized in that: The specific method of obtaining the nozzle abnormality judgment value is as follows: Perform difference calculation on chemical consumption data and corresponding reference consumption data to obtain chemical consumption deviation and perform data normalization. At the same time, perform data normalization on pre-wash parameters. Obtaining nozzle abnormality determination analysis data from a preset database, specifically including nozzle abnormality determination analysis factors and pre-wash analysis factors, wherein the nozzle abnormality determination analysis factors include chemical agent consumption analysis factors and pre-wash parameter analysis factors, and the pre-wash analysis factors include spray pressure analysis factors, tower outlet temperature analysis factors, and spray liquid total flow analysis factors; The pre-washing parameters are weighted and coupled with the corresponding pre-washing analysis factors to obtain the initial nozzle abnormality judgment value; The nozzle abnormality judgment value is obtained by coupling the initial nozzle abnormality judgment value, the chemical agent consumption deviation and the corresponding nozzle abnormality judgment analysis factor after weighted calculation.

7. The anhydrous hydrogen fluoride pre-wash data in-depth analysis and optimization system according to claim 5, characterized in that: The specific process of determining whether to generate a nozzle detection solution is as follows: Extract the preset judgment value from the preset database and compare it with the nozzle abnormality judgment value: 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 staff to inspect the spray tower; The nozzle detection solution includes nozzle installation correction, nozzle spacing verification, nozzle blockage detection and nozzle deformation observation and early warning.

8. The anhydrous hydrogen fluoride pre-wash data in-depth analysis and optimization system according to claim 1, characterized in that: After executing the nozzle detection scheme in sequence, the scheme detection compliance determination is performed, and the specific steps are as follows: F1, real-time acquisition of each nozzle angle, and comparison with the preset nozzle angle obtained from the preset database, and selection of corresponding nozzle angle optimization measures based on the nozzle angle comparison results; F2: Detect the spacing between nozzles and match it with the initial spacing set in the preset database. If there is a mismatch between the nozzle spacing and the initial spacing, the preset staff will be prompted to adjust the spacing in the next preset analysis cycle. Otherwise, continue to detect the next nozzle spacing; F3, obtain the nozzle blockage parameters to determine whether to replace the corresponding nozzle. The nozzle blockage parameters include inlet pressure, outlet pressure and spray branch flow rate: F4 issues a nozzle deformation warning to prompt the preset staff to check the nozzle after the current preset analysis cycle is completed. If the nozzle is deformed, it should be replaced. Otherwise, the equipment structure other than the nozzle should be inspected.

9. The anhydrous hydrogen fluoride pre-wash data in-depth analysis and optimization system according to claim 8, characterized in that: The specific content of selecting the corresponding nozzle angle optimization measure based on the nozzle angle comparison result is as follows: If there are more than the preset number of nozzle angles that are not within the corresponding preset nozzle angle range, the preset staff will be prompted to conduct a spray tower structure inspection to eliminate the factors that lead to abnormal chemical consumption; If there are fewer than a preset number of nozzle angles that are not within the corresponding preset nozzle angle range, the nozzle angles are automatically corrected to be within the preset nozzle angle range; If the angles of each nozzle are within the corresponding preset nozzle angle range, the nozzle spacing verification is performed.

10. The anhydrous hydrogen fluoride pre-washing data in-depth analysis and optimization system according to claim 8, characterized in that: The specific process of selecting whether to replace the corresponding nozzle is as follows: The inlet and outlet pressures of each nozzle are differentially calculated to obtain the inlet and outlet pressure difference, and the inlet and outlet pressure difference is 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 clogged and the preset staff is prompted to replace the corresponding nozzle. Otherwise, the spray branch flow of each nozzle is compared with the minimum flow limit stored in the preset database. If the spray branch flow is less than the minimum flow limit, the nozzle is determined to be clogged and the preset staff is prompted to replace the corresponding nozzle. Otherwise, a nozzle deformation warning is issued.

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