Electronic grade hydrochloric acid purification method and purification system based on impurity characteristics

Abstract

The application provides a purification method and system for electronic-grade hydrochloric acid based on impurity characteristics, aiming to solve the technical problem that it is difficult to meet the purification requirements of complex impurity systems for electronic-grade hydrochloric acid. The purification method comprises the following steps: S100, obtaining a feature set; S200, establishing an optimization model of the pore size distribution of the separation medium; S300, modifying the surface of the separation medium according to the optimization model to obtain characteristic parameters; S400, selecting an extraction reagent according to the characteristic parameters; S500, capturing impurities and calculating the efficiency; S600, optimizing the process according to the efficiency, and if the expected result is not achieved, coordinating the pore size and polarity gradient to improve the purification stability; S700, performing multi-stage purification based on the stability index, and integrating data to evaluate the total removal rate and the separation effect. By integrating impurity characteristic analysis, customized design of separation medium, process dynamic control and stability forward-looking evaluation into an intelligent closed-loop system, the application aims to achieve efficient, stable and predictable ultra-high purification.

Description

Technical Field

[0001] This invention relates to the field of hydrochloric acid purification technology, and more specifically to an electronic-grade hydrochloric acid purification method and system based on impurity characteristics. Background Technology

[0002] Electronic-grade hydrochloric acid is a key basic chemical in high-end manufacturing such as semiconductors and photovoltaics, and its purity directly affects chip yield and device performance. Currently, the industry's requirements for removing organic impurities (such as nonpolar benzene molecules and polar alcohol molecules) from hydrochloric acid have reached the ppt (parts per trillion) level, posing a severe challenge to traditional purification technologies.

[0003] Existing technologies mainly rely on empirically selected adsorbent materials and immobilization processes, which have three core drawbacks: Insufficient material design: Traditional adsorbents lack precise design of pore size and surface chemical properties for impurity molecular size and polarity, resulting in poor selectivity for impurities with similar polarity and low separation efficiency. Rigidity of the process: The existing system cannot adaptively adjust according to changes in the real-time impurity spectrum, and lacks an effective online optimization mechanism when separation efficiency fluctuations occur; Lack of stability risk assessment: There is a lack of forward-looking prediction of the chemical degradation risk of separation media and reagents in strong acid environment, which can easily lead to secondary pollution and process interruption.

[0004] The essence of these problems lies in the fact that the existing methods are open-loop, static, discrete processes with each step disconnected from the others, making it difficult to meet the purification requirements of electronic-grade hydrochloric acid for complex impurity systems. Summary of the Invention

[0005] To address the technical problem of failing to meet the purification requirements of electronic-grade hydrochloric acid for complex impurity systems, this invention provides an electronic-grade hydrochloric acid purification method and system based on impurity characteristics. By integrating impurity characteristic analysis, customized design of separation media, dynamic process control, and predictive stability assessment into an intelligent closed-loop system, this invention aims to achieve ultra-high purification with high efficiency, stability, and predictability.

[0006] The technical solution of this invention is: An electronic-grade hydrochloric acid purification method based on impurity characteristics includes the following steps: S100. Analyze the size and polarity of impurity molecules to obtain a feature set; S200: Identify impurity types and establish an optimized model for the pore size distribution of the separation medium accordingly; S300. Modify the surface of the separation medium according to the optimization model, determine the group distribution through simulation, and obtain characteristic parameters; S400. Select extraction reagents based on characteristic parameters. If their acid stability is insufficient, assess the degradation risk and generate an enhancement scheme. S500, using an enhanced solution to build a multi-stage separation device, optimizes the flow state through fluid simulation to capture impurities and calculate efficiency; S600: Based on this efficiency feedback optimization process, if the expected results are not achieved, the pore size and polarity gradient will be synergistically adjusted to improve purification stability. The S700 performs multi-stage purification based on stability indicators and integrates data to evaluate the total removal rate and the type separation effect.

[0007] Optionally, step S100 includes: S110. Obtain the molecular size and polarity parameters of impurities through mass spectrometry analysis to form an initial feature set; S120. Principal component analysis is used to reduce the dimensionality of the initial feature set and extract the main feature vectors. S130. If the feature vector dimension is too high, perform cluster analysis to generate a subset of impurity features. S140. Based on feature subsets, and combining molecular interaction and environmental data, construct a feature correlation matrix; S150. Calculate the correlation between molecular parameters and acidity using the correlation matrix to determine the combination of key features; S160. Based on the combination of key features, construct a data representation model and output the final impurity feature set.

[0008] Optionally, in step S200, a classification algorithm is used to identify the type of impurities, and an optimization model for controlling the pore size distribution of the separation medium is established based on the classification results. In step S300, modified groups are introduced to treat the surface of the separation medium based on an optimization model, and the distribution law of the groups is determined by calculation method to obtain characteristic parameters; In step S500, the flow state of the acidic solution in the separation column is optimized by combining fluid dynamics simulation in order to capture impurities and calculate the impurity isolation efficiency.

[0009] Optionally, step S200 includes: S210. Use support vector machines to classify the impurity feature set and obtain the impurity type results; S220. Based on the impurity type results, cluster analysis is used to perform subset partitioning; S230. If the number of subsets exceeds the limit, the aperture distribution is adjusted through iterative optimization to obtain preliminary optimization parameters. S240. Combine the solution environment data to construct the correlation matrix between it and the pore size distribution parameters; S250. Principal component analysis is used to reduce the dimensionality of the correlation matrix and extract the main aperture feature vectors. S260. Based on the reduced pore size feature vector, an optimization model for the separation medium structure is constructed.

[0010] Optionally, step S300 includes: S310. Analyze the spatial distribution data of functional groups, and group them through cluster analysis to obtain subsets of functional group distributions; S320. If the uniformity of the group distribution is insufficient, the distribution parameters are adjusted through iterative optimization to obtain an optimized distribution scheme. S330. Based on the optimized scheme and combined with molecular interaction data, construct the interaction matrix; S340. Extract the main features of the interaction matrix through principal component analysis to obtain the dimensionality-reduced eigenvectors. S350. Calculate the surface chemical property parameters based on the eigenvector to obtain the property distribution; S360. If the stability of the property distribution is insufficient, the group distribution is recalculated through molecular dynamics simulation to obtain updated parameters. S370. Construct a surface chemical property optimization model based on the updated parameters and output the final property parameters.

[0011] Optionally, step S400 includes: S410. Based on surface chemical characteristic parameters, a classification algorithm is used to screen extraction reagents suitable for acidic environments to obtain a preliminary reagent set; S420. Evaluate the chemical stability of each reagent under acidic conditions and construct a stability evaluation matrix; S430. If the stability of a reagent in the matrix is ​​insufficient, the random forest algorithm is used to predict its degradation risk and generate a risk score. S440. Based on risk scoring, the molecular structure of high-risk reagents is optimized through molecular simulation to obtain improved molecular designs. S450. Based on the improved molecular design, reassess its stability and update the stability assessment matrix; S460. If the stability of all reagents in the updated matrix meets the standard, then a stability enhancement scheme is generated by weighted scoring and comprehensive performance. S470. Based on this plan, determine the final extraction reagent configuration and optimized application plan.

[0012] Optionally, step S500 includes: S510. Based on the stability enhancement scheme, determine the structural parameters of the multi-stage separation device, construct the device model, and determine the preliminary configuration; S520. Based on this configuration, a fluid dynamics model is used to simulate the flow of acidic solution in the separation column to obtain the solution flow distribution. S530. If there is flow deviation in the flow distribution, the flow can be optimized by adjusting the geometric parameters of the separation column to obtain an improved flow distribution. S540. Based on the improved flow distribution, the capture process of impurity molecules is simulated, and the molecule capture efficiency is calculated. S550. Construct an impurity isolation efficiency evaluation matrix based on the capture efficiency to evaluate the performance of each separation column; S560. If the evaluation matrix shows that the performance of a certain separation column is insufficient, then optimize it by adjusting its material properties to obtain an optimized separation column design. S570. Based on all optimized designs, recalculate the overall impurity isolation efficiency and determine the final configuration of the multi-stage separation unit.

[0013] Optionally, step S600 includes: S610. Establish a pore size distribution model for the separation column and determine the pore size distribution parameters; S620. Based on pore size parameters, evaluate the effect of surface polarity gradient on molecular adsorption and obtain molecular adsorption distribution. S630. If the adsorption distribution is uneven, optimize it by adjusting the column structure parameters to determine the improved adsorption distribution. S640. Based on the improved adsorption distribution, the fluid dynamics within the separation column are simulated to obtain the fluid distribution characteristics. S650, combined with fluid characteristics, evaluates the impact of material properties on isolation efficiency and formulates material optimization schemes; S660. If the purification stability of the material solution is insufficient, it can be optimized by adjusting the surface polarity to obtain optimized material properties. S670. Based on the optimized material properties, the purification stability is recalculated to determine the final separation process configuration.

[0014] Optionally, step S700 includes: S710. Collect initial separation data of multi-stage purification process to determine the initial concentration distribution of various impurities; S720. Based on the initial data, a clustering algorithm is used to classify impurities and determine their distribution characteristics and classification results; S730. If there are unidentified types in the classification results, the data attributes are analyzed through feature extraction algorithms to update the classification results; S740. Based on the updated classification results, construct a multi-level purification dynamic adjustment model to determine the optimized process configuration; S750: Through optimized configuration, it simulates the removal dynamics of various impurities and calculates the impurity removal rate. S760. If the impurity removal rate is insufficient, regression analysis is used to adjust the process configuration parameters to obtain an improved removal rate. S770. Based on the improved removal rate, evaluate the separation effect of various impurities and the overall process performance to determine the final separation effect.

[0015] A purification system using the aforementioned electronic-grade hydrochloric acid purification method includes an intelligent analysis and decision-making unit, a process execution and optimization unit, and a performance evaluation and feedback unit.

[0016] Compared with the prior art, the beneficial effects of the present invention are: By acquiring the molecular size and polarity parameters of impurities, a feature set is constructed, and data processing algorithms are used to accurately classify impurity types. This allows for the control of the pore size distribution of the separation medium, forming an optimized model. Based on this, modified groups are introduced to treat the surface of the separation medium, the distribution of these groups is calculated, surface chemical properties are optimized, and extraction reagents suitable for acidic environments are selected. If the reagent stability is insufficient, a predictive algorithm is used to assess the degradation risk and develop a stability enhancement strategy.

[0017] A multi-stage separation device is used, combined with fluid dynamics simulation, to capture impurity molecules. The separation efficiency is optimized by controlling the pore size distribution and polarity gradient. Finally, the overall impurity removal rate is determined by data integration algorithm.

[0018] By combining impurity characteristic analysis, media structure optimization, and surface chemical modification, the separation efficiency and purification stability of different types of impurities in acidic solutions are significantly improved, making it suitable for high-precision purification needs in complex chemical environments.

[0019] By integrating impurity characterization, customized design of separation media, dynamic process control, and predictive stability assessment into an intelligent closed-loop system, the aim is to achieve efficient, stable, and predictable ultra-high purification. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0022] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0023] The following disclosure provides many different embodiments or examples for implementing various structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. In addition, examples of various specific processes and materials are provided in this invention, but those skilled in the art will recognize the application of other processes and / or the use of other materials.

[0024] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0025] Example 1:

[0026] See Figure 1 This embodiment discloses a method for purifying electronic-grade hydrochloric acid based on impurity characteristics, including the following steps: Step S100: Impurity Characteristic Analysis: Analyze the molecular size and polarity of impurities to obtain a feature set.

[0027] Specifically, step S100 includes: S110. Obtain sample data from the acidic solution, determine the molecular size and polarity parameters through mass spectrometry analysis, and obtain the initial feature set of impurities.

[0028] S120. Based on the initial feature set, the principal component analysis algorithm is used to extract the main features of molecular size and polarity parameters, and the dimensionality-reduced feature vector is obtained.

[0029] S130. If the dimension of the feature vector is higher than the preset threshold, the feature vector is grouped by cluster analysis to obtain a subset of impurity features after classification.

[0030] S140. For the classified subset of impurity features, obtain contextual data on intermolecular interactions and the solution environment, and construct the correlation matrix of the feature set.

[0031] S150. Using the correlation matrix, the correlation between molecular size and polarity parameters and solution acidity is calculated with the Pearson correlation coefficient to determine the key feature combination.

[0032] S160. Based on the combination of key features, construct a data representation model of the feature set to obtain the final impurity feature set.

[0033] Step S200, Medium structure modeling: Identify impurity types and establish an optimization model for the pore size distribution of the separation medium. Specifically, use a classification algorithm to identify impurity types and establish an optimization model for controlling the pore size distribution of the separation medium based on the classification results. Step S200 specifically includes: S210. Obtain the impurity feature set from the acidic solution sample, classify the feature set using the support vector machine algorithm, and obtain the impurity type result.

[0034] S220. Based on the impurity type results, cluster analysis is used to group the classification results to obtain subsets of impurity types.

[0035] S230. If the number of subsets exceeds a preset threshold, the pore size distribution of the separation medium is adjusted through an iterative optimization algorithm to obtain the preliminary optimized pore size distribution parameters.

[0036] S240. Based on the preliminarily optimized pore size distribution parameters, obtain solution environment data and construct the correlation matrix between pore size distribution and solution environment.

[0037] S250. Using the correlation matrix, the principal component analysis algorithm is used to extract the main features of the aperture distribution parameters, and the dimensionality-reduced aperture feature vector is obtained.

[0038] S260. Based on the reduced aperture feature vector, construct the separation medium structure model to obtain the optimized separation medium structure parameters.

[0039] Step S300, Surface Functionalization Design: Modify the surface of the separation medium according to the optimization model, determine the distribution of functional groups through simulation, and obtain characteristic parameters. Specifically, based on the optimization model, introduce modified functional groups to treat the surface of the separation medium, determine the distribution law of functional groups through calculation methods, and obtain characteristic parameters.

[0040] Step S300 specifically includes: S310. Obtain spatial distribution data of functional groups from the modified functional group distribution parameters, and group the functional group distributions using cluster analysis to obtain subsets of functional group distributions.

[0041] S320. If the uniformity of the group distribution subset is lower than a preset threshold, the group distribution parameters are adjusted through an iterative optimization algorithm to obtain an optimized group distribution scheme.

[0042] S330. Based on the optimized group distribution scheme, obtain molecular interaction data on the surface of the separation medium and construct a molecular interaction matrix.

[0043] S340. Using the molecular interaction matrix, principal component analysis is used to extract the main interaction features, resulting in a dimension-reduced interaction feature vector. S350. Based on the reduced-dimensional interaction eigenvectors, calculate the surface chemical property parameters to obtain the surface chemical property distribution.

[0044] S360. If the stability of the surface chemical property distribution is lower than the preset threshold, the group distribution is recalculated using molecular dynamics simulation to obtain the updated group distribution parameters.

[0045] S370. Based on the updated group distribution parameters, construct a surface chemical property optimization model to obtain the final surface chemical property parameters.

[0046] Step S400, Reagent stability assessment: Select extraction reagents based on characteristic parameters. If their acid stability is insufficient, assess the degradation risk and generate an enhancement scheme.

[0047] Step S400 specifically includes: S410. Obtain the reagent selection criteria from the surface chemical characteristic parameters, determine the extraction reagents suitable for the acidic environment through the classification algorithm, and obtain a preliminary reagent set.

[0048] S420. Based on the preliminary reagent set, obtain the chemical stability data of each reagent in an acidic environment and construct a stability assessment matrix.

[0049] S430. If the stability value of a reagent in the stability assessment matrix is ​​lower than a preset threshold, the degradation risk of the reagent is predicted by the random forest algorithm to obtain a degradation risk score.

[0050] S440. Based on the degradation risk score, obtain the molecular structure data of high-risk reagents, adjust the molecular structure parameters through molecular simulation tools, and obtain the improved reagent molecule design.

[0051] S450. Based on the improved reagent molecule design, recalculate the stability parameters of the reagent in an acidic environment and construct an updated stability assessment matrix.

[0052] S460. If the stability of all reagents in the updated stability assessment matrix is ​​higher than the preset threshold, then the stability enhancement scheme is obtained by combining the reagent performance through a weighted scoring method.

[0053] S470. Based on the stability enhancement scheme, generate the final extraction reagent configuration parameters and determine the optimized reagent application scheme.

[0054] Step S500, Separation process simulation: An enhanced scheme is applied to construct a multi-stage separation device. Fluid simulation is used to optimize the flow state to capture impurities and calculate the efficiency. Specifically, fluid dynamics simulation is combined to optimize the flow state of the acidic solution in the separation column to capture impurities and calculate the impurity isolation efficiency.

[0055] Step S500 specifically includes: S510. Obtain the structural parameters of the multi-stage separation device through a stability enhancement scheme, construct the device design model, and obtain the preliminary separation device configuration.

[0056] S520. Based on the configuration of the preliminary separation device, the flow trajectory of the acidic solution in the separation column is calculated using a fluid dynamics model to obtain the solution flow distribution.

[0057] S530. If there are flow deviation regions in the solution flow distribution, the flow trajectory can be optimized by adjusting the geometric parameters of the separation column to obtain an improved flow distribution.

[0058] S540. Based on the improved flow distribution, simulate the capture process of impurity molecules in the separation column to obtain the molecule capture efficiency.

[0059] S550: By constructing an impurity isolation efficiency evaluation matrix based on molecular capture efficiency, the performance parameters of each separation column are determined.

[0060] S560. If the performance of a separation column in the impurity isolation efficiency evaluation matrix is ​​lower than the preset threshold, the material properties of the separation column are adjusted through molecular dynamics simulation to obtain an optimized separation column design.

[0061] S570. Based on the optimized separation column design, recalculate the impurity isolation efficiency parameters and determine the final configuration of the multi-stage separation unit.

[0062] Step S600, Separation efficiency control: Based on this efficiency feedback, the process is optimized. If the expected results are not achieved, the pore size and polarity gradient are synergistically controlled to improve purification stability. Step S600 specifically includes: S610. Construct a pore size distribution model for the separation column using pore size distribution data, and obtain pore size distribution parameters; S620. Based on the pore size distribution parameters, calculate the effect of the surface polarity gradient on molecular adsorption to obtain the molecular adsorption distribution.

[0063] S630. If there are non-uniform regions in the molecular adsorption distribution, the adsorption distribution is optimized by adjusting the column structure parameters to determine the improved adsorption distribution.

[0064] S640. Based on the improved adsorption distribution, simulate the dynamic changes of fluid distribution in the separation column to obtain fluid distribution characteristics.

[0065] S650. By assessing the fluid distribution characteristics, evaluate the impact of material properties on impurity isolation efficiency and determine the material optimization scheme.

[0066] S660. If the purification stability of a certain material property in the material optimization scheme is lower than the preset threshold, the surface polarity is adjusted through the regulation mechanism to obtain the optimized material property.

[0067] S670. Based on the optimized material properties, recalculate the purification stability parameters and determine the final separation process configuration.

[0068] Step S700, Purification Efficiency Verification: Perform multi-stage purification based on stability indicators, and integrate data to evaluate the total removal rate and the type separation effect.

[0069] Step S700 specifically includes: S710. Initial separation data is obtained through a multi-stage purification process. The initial concentration distribution of various impurities is determined by a preset data acquisition mechanism to obtain the initial separation data.

[0070] S720. Based on the initial separation data, a clustering algorithm is used to classify the impurity types, determine the distribution characteristics of each type of impurity, and determine the impurity classification results.

[0071] S730. If there are unidentified impurity types in the impurity classification results, the attribute features of the separated data are analyzed by feature extraction algorithm to obtain updated impurity classification results.

[0072] S740. Based on the updated impurity classification results, construct a dynamic adjustment model for the multi-level purification process, obtain process configuration parameters, and determine the optimized process configuration.

[0073] S750: Through optimized process configuration, the removal dynamics of various impurities in a multi-stage purification process are simulated to obtain the impurity removal rate.

[0074] S760. If the impurity removal rate is lower than the preset threshold, a regression algorithm is used to analyze the correlation between the process configuration parameters and the removal rate, and the process configuration parameters are adjusted to obtain an improved removal rate.

[0075] S770. Based on the improved removal rate, evaluate the separation effect of various impurities, judge the performance of the final purification process, and determine the final separation effect.

[0076] In this embodiment, a feature set is constructed by obtaining the molecular size and polarity parameters of impurities, and data processing algorithms are used to accurately classify impurity types, thereby controlling the pore size distribution of the separation medium and forming an optimization model. Based on this, modified groups are introduced to treat the surface of the separation medium, the distribution law of the groups is calculated, the surface chemical properties are optimized, and extraction reagents suitable for acidic environments are selected. If the reagent stability is insufficient, a prediction algorithm is used to assess the degradation risk and formulate a stability enhancement scheme.

[0077] A multi-stage separation device is used, combined with fluid dynamics simulation, to capture impurity molecules. The separation efficiency is optimized by controlling the pore size distribution and polarity gradient. Finally, the overall impurity removal rate is determined by data integration algorithm.

[0078] By combining impurity characteristic analysis, media structure optimization, and surface chemical modification, the separation efficiency and purification stability of different types of impurities in acidic solutions are significantly improved, making it suitable for high-precision purification needs in complex chemical environments.

[0079] By integrating impurity characterization, customized design of separation media, dynamic process control, and predictive stability assessment into an intelligent closed-loop system, the aim is to achieve efficient, stable, and predictable ultra-high purification.

[0080] Example 2:

[0081] See Figure 2 This embodiment discloses a purification system using an electronic-grade hydrochloric acid purification method, including an intelligent analysis and decision-making unit, a process execution and optimization unit, and a performance evaluation and feedback unit.

[0082] Specifically, the configuration of the intelligent analysis and decision-making unit is as follows: Perform impurity characterization analysis to obtain the molecular size and polarity parameters of the impurities; Based on the impurity characteristics, an optimization model for the separation medium structure is constructed; Determine the chemical properties of the separation medium surface and screen extraction reagents accordingly; Assess the degradation risk of the extraction reagent and develop a stability enhancement strategy.

[0083] The process execution and optimization unit is configured as follows: The stability enhancement scheme described above is applied to construct and control a multi-stage separation device; Simulate the fluid dynamics of acidic solutions in a separation device to capture impurities and obtain isolation efficiency indicators; Based on the isolation efficiency index, the separation process is optimized by synergistically controlling the pore size distribution and surface polarity gradient of the separation medium.

[0084] The performance evaluation and feedback unit is configured as follows: Perform a multi-stage purification process and integrate the separation data to determine the overall impurity removal rate; The separation effect on different types of impurities is evaluated, and the evaluation results are fed back to the intelligent analysis and decision-making unit and the process execution and optimization unit to form a closed-loop optimization control system.

[0085] The intelligent analysis and decision-making unit is the brain of the system, comprising an impurity characteristic analysis module, a separation medium design module, and a reagent stability assessment module. The impurity characteristic set, separation medium structure optimization model, surface chemical property parameters, and stability enhancement schemes generated by this unit are directly transmitted to the process execution and optimization unit as core instructions and parameters.

[0086] The process execution and optimization unit is the backbone of the system, comprising a multi-level separation control module and a process dynamic regulation module. Upon receiving the aforementioned instructions, it constructs the multi-level separation device and executes the purification process, while simultaneously uploading the real-time monitored impurity isolation efficiency and purification stability indicators to the performance evaluation and feedback unit.

[0087] The performance evaluation and feedback unit is the system's diagnostic center, including a purification efficiency verification module. It analyzes the received performance data and calculates the overall impurity removal rate and type separation effect.

[0088] This unit compares the evaluation results with the preset targets. If the targets are not met, it generates two types of instructions: material design optimization and process parameter optimization, and transmits them in reverse. The material design optimization instructions are fed back to the intelligent analysis and decision-making unit, triggering it to re-optimize the separation medium model or surface chemical properties. The process parameter optimization instructions are fed back to the process execution and optimization unit, directly guiding it to adjust the current operating parameters.

[0089] The fundamental principle of this system is to transform the traditional experience-based "trial and error" purification process into an "intelligent organism" driven by data and models, capable of predicting the future, adjusting in real time, and continuously learning, thereby ensuring the high efficiency, precision, and ultra-high stability of the electronic-grade hydrochloric acid purification process.

[0090] The embodiments described above are merely illustrative of specific implementations of the present invention, and while the descriptions are detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for purifying electronic-grade hydrochloric acid based on impurity characteristics, characterized in that, Includes the following steps: S100. Analyze the size and polarity of impurity molecules to obtain a feature set; S200: Identify impurity types and establish an optimized model for the pore size distribution of the separation medium accordingly; S300. Modify the surface of the separation medium according to the optimization model, determine the group distribution through simulation, and obtain characteristic parameters; S400. Select extraction reagents based on characteristic parameters. If their acid stability is insufficient, assess the degradation risk and generate an enhancement scheme. S500, using an enhanced solution to build a multi-stage separation device, optimizes the flow state through fluid simulation to capture impurities and calculate efficiency; S600: Based on this efficiency feedback optimization process, if the expected results are not achieved, the pore size and polarity gradient will be synergistically adjusted to improve purification stability. The S700 performs multi-stage purification based on stability indicators and integrates data to evaluate the total removal rate and the type separation effect.

2. The method for purifying electronic-grade hydrochloric acid according to claim 1, characterized in that, Step S100 includes: S110. Obtain the molecular size and polarity parameters of impurities through mass spectrometry analysis to form an initial feature set; S120. Principal component analysis is used to reduce the dimensionality of the initial feature set and extract the main feature vectors. S130. If the feature vector dimension is too high, perform cluster analysis to generate a subset of impurity features. S140. Based on feature subsets, and combining molecular interaction and environmental data, construct a feature correlation matrix; S150. Calculate the correlation between molecular parameters and acidity using the correlation matrix to determine the combination of key features; S160. Based on the combination of key features, construct a data representation model and output the final impurity feature set.

3. The method for purifying electronic-grade hydrochloric acid according to claim 1, characterized in that: In step S200, a classification algorithm is used to identify the type of impurities, and an optimization model for controlling the pore size distribution of the separation medium is established based on the classification results. In step S300, modified groups are introduced to treat the surface of the separation medium based on an optimization model, and the distribution law of the groups is determined by calculation method to obtain characteristic parameters; In step S500, the flow state of the acidic solution in the separation column is optimized by combining fluid dynamics simulation in order to capture impurities and calculate the impurity isolation efficiency.

4. The method for purifying electronic-grade hydrochloric acid according to claim 3, characterized in that, Step S200 includes: S210. Use support vector machines to classify the impurity feature set and obtain the impurity type results; S220. Based on the impurity type results, cluster analysis is used to perform subset partitioning; S230. If the number of subsets exceeds the limit, the aperture distribution is adjusted through iterative optimization to obtain preliminary optimization parameters. S240. Combine the solution environment data to construct the correlation matrix between it and the pore size distribution parameters; S250. Principal component analysis is used to reduce the dimensionality of the correlation matrix and extract the main aperture feature vectors. S260. Based on the reduced pore size feature vector, an optimization model for the separation medium structure is constructed.

5. The method for purifying electronic-grade hydrochloric acid according to claim 3, characterized in that, Step S300 includes: S310. Analyze the spatial distribution data of functional groups, and group them through cluster analysis to obtain subsets of functional group distributions; S320. If the uniformity of the group distribution is insufficient, the distribution parameters are adjusted through iterative optimization to obtain an optimized distribution scheme. S330. Based on the optimized scheme and combined with molecular interaction data, construct the interaction matrix; S340. Extract the main features of the interaction matrix through principal component analysis to obtain the dimensionality-reduced eigenvectors. S350. Calculate the surface chemical property parameters based on the eigenvector to obtain the property distribution; S360. If the stability of the property distribution is insufficient, the group distribution is recalculated through molecular dynamics simulation to obtain updated parameters. S370. Construct a surface chemical property optimization model based on the updated parameters and output the final property parameters.

6. The method for purifying electronic-grade hydrochloric acid according to claim 1, characterized in that, Step S400 includes: S410. Based on surface chemical characteristic parameters, a classification algorithm is used to screen extraction reagents suitable for acidic environments to obtain a preliminary reagent set; S420. Evaluate the chemical stability of each reagent under acidic conditions and construct a stability evaluation matrix; S430. If the stability of a reagent in the matrix is ​​insufficient, the random forest algorithm is used to predict its degradation risk and generate a risk score. S440. Based on risk scoring, the molecular structure of high-risk reagents is optimized through molecular simulation to obtain improved molecular designs. S450. Based on the improved molecular design, reassess its stability and update the stability assessment matrix; S460. If the stability of all reagents in the updated matrix meets the standard, then a stability enhancement scheme is generated by weighted scoring and comprehensive performance. S470. Based on this plan, determine the final extraction reagent configuration and optimized application plan.

7. The method for purifying electronic-grade hydrochloric acid according to claim 3, characterized in that, Step S500 includes: S510. Based on the stability enhancement scheme, determine the structural parameters of the multi-stage separation device, construct the device model, and determine the preliminary configuration; S520. Based on this configuration, a fluid dynamics model is used to simulate the flow of acidic solution in the separation column to obtain the solution flow distribution. S530. If there is flow deviation in the flow distribution, the flow can be optimized by adjusting the geometric parameters of the separation column to obtain an improved flow distribution. S540. Based on the improved flow distribution, the capture process of impurity molecules is simulated, and the molecule capture efficiency is calculated. S550. Construct an impurity isolation efficiency evaluation matrix based on the capture efficiency to evaluate the performance of each separation column; S560. If the evaluation matrix shows that the performance of a certain separation column is insufficient, then optimize it by adjusting its material properties to obtain an optimized separation column design. S570. Based on all optimized designs, recalculate the overall impurity isolation efficiency and determine the final configuration of the multi-stage separation unit.

8. The method for purifying electronic-grade hydrochloric acid according to claim 1, characterized in that, Step S600 includes: S610. Establish a pore size distribution model for the separation column and determine the pore size distribution parameters; S620. Based on pore size parameters, evaluate the effect of surface polarity gradient on molecular adsorption and obtain molecular adsorption distribution. S630. If the adsorption distribution is uneven, optimize it by adjusting the column structure parameters to determine the improved adsorption distribution. S640. Based on the improved adsorption distribution, the fluid dynamics within the separation column are simulated to obtain the fluid distribution characteristics. S650, combined with fluid characteristics, evaluates the impact of material properties on isolation efficiency and formulates material optimization schemes; S660. If the purification stability of the material solution is insufficient, it can be optimized by adjusting the surface polarity to obtain optimized material properties. S670. Based on the optimized material properties, the purification stability is recalculated to determine the final separation process configuration.

9. The method for purifying electronic-grade hydrochloric acid according to claim 1, characterized in that, Step S700 includes: S710. Collect initial separation data of multi-stage purification process to determine the initial concentration distribution of various impurities; S720. Based on the initial data, a clustering algorithm is used to classify impurities and determine their distribution characteristics and classification results; S730. If there are unidentified types in the classification results, the data attributes are analyzed through feature extraction algorithms to update the classification results; S740. Based on the updated classification results, construct a multi-level purification dynamic adjustment model to determine the optimized process configuration; S750: Through optimized configuration, it simulates the removal dynamics of various impurities and calculates the impurity removal rate. S760. If the impurity removal rate is insufficient, regression analysis is used to adjust the process configuration parameters to obtain an improved removal rate. S770. Based on the improved removal rate, evaluate the separation effect of various impurities and the overall process performance to determine the final separation effect.

10. A purification system employing the electronic-grade hydrochloric acid purification method according to any one of claims 1-9, characterized in that, It includes an intelligent analysis and decision-making unit, a process execution and optimization unit, and a performance evaluation and feedback unit.

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