A verification system and preparation process for a photoinitiator

Abstract

This invention provides a verification system and preparation process for photoinitiators, relating to the field of photoinitiator evaluation and verification technology. The verification system includes a sample analysis unit, an index acquisition unit, a purity assessment unit, and a standard comparison unit, which are sequentially connected. This invention can scientifically and objectively evaluate the purity of photoinitiators by comparing the evaluation results with pre-set evaluation standards. This standardized comparison method ensures the objectivity and consistency of the verification results. Through comparison, it can be clearly determined whether the photoinitiator meets the preparation requirements, providing a reliable basis for subsequent production and use.

Description

Technical Field

[0001] This invention relates to the field of photoinitiator evaluation and verification technology, and more particularly to a verification system and its preparation process in the photoinitiator preparation process. Background Technology

[0002] Photoinitiators are a special class of chemical substances that absorb light energy within a specific wavelength range (typically in the ultraviolet or visible light region) and initiate chemical reactions. These substances play a crucial role in photopolymerization technology, primarily by generating free radicals or cations under light irradiation, thereby stimulating the polymerization reaction of monomers or prepolymers. This process allows materials to rapidly transform from a liquid or semi-solid state to a solid state, achieving curing. The selection and application of photoinitiators are critical to the performance of photopolymerization systems, as their efficiency and spectral absorption characteristics directly affect the speed and quality of material curing. For example, in manufacturing processes, the use of photoinitiators allows for precise control of the curing process, which has significant industrial application value for the production of high-performance optoelectronic devices, coatings, adhesives, and 3D printing materials.

[0003] Furthermore, environmental stability, toxicity, and migration are critical factors that must be considered when selecting and applying photoinitiators. By optimizing the chemical structure and functional properties of these substances, safer and more efficient photoinitiation systems can be developed to meet the stringent requirements of modern industry and technology. Therefore, purity verification is particularly important during the preparation of photoinitiators. However, existing purity verification methods often focus only on single indicators, such as purity or impurity content, lacking a comprehensive evaluation of the overall performance of the photoinitiator. This may lead to the neglect of certain key performance indicators, thereby affecting the actual application effect of the product.

[0004] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0005] In view of this, and in response to the problems in the related technologies, the present invention provides a verification system and its preparation process for photoinitiator preparation, in order to solve the problem that the existing purity verification methods mentioned above often only focus on a single indicator.

[0006] To solve the above problems, the specific technical solution adopted by the present invention is as follows:

[0007] According to one aspect of the present invention, a verification system for the preparation process of a photoinitiator is provided. The verification system for the preparation process of a photoinitiator includes: a sample analysis unit, an index acquisition unit, a purity assessment unit, and a standard comparison unit, wherein the sample analysis unit, the index acquisition unit, the purity assessment unit, and the standard comparison unit are connected sequentially.

[0008] The sample analysis unit is used to acquire photoinitiator samples and obtain chromatographic data of the photoinitiator samples using chromatographic analysis.

[0009] The index acquisition unit is used to extract the purity index data of the photoinitiator based on the obtained chromatographic data using wavelet transform.

[0010] The purity assessment unit is used to evaluate and verify the purity of photoinitiators based on purity index data and a target decision evaluation method.

[0011] The standard comparison unit is used to compare the obtained evaluation results with the pre-set evaluation standards and determine whether the photoinitiator meets the preparation requirements.

[0012] The purity assessment unit includes: a judgment matrix construction module, an optimal weight determination module, a superiority matrix construction module, an assessment value calculation module, and a comprehensive assessment calculation module, and these modules are connected sequentially.

[0013] The judgment matrix construction module is used to compare the purity index data of photoinitiators pairwise based on the interval hierarchical analysis method, and construct the interval importance judgment matrix based on the comparison results.

[0014] The optimal weight determination module is used to establish a weight target optimization model based on the interval importance judgment matrix and determine the optimal weight value of each purity index through an improved radial shift algorithm.

[0015] The eugenics matrix construction module is used to determine the relative membership degree of each purity index based on the membership degree method, and to construct the relative eugenics matrix of each purity index.

[0016] The evaluation value calculation module is used to calculate the evaluation value of each purity index based on the optimal weight value and the relative superiority matrix of each purity index.

[0017] The comprehensive evaluation calculation module is used to comprehensively evaluate the purity of the photoinitiator based on the evaluation values ​​of each purity index, and obtain a comprehensive evaluation value.

[0018] Preferably, the sample analysis unit includes: a sample injection module, a chromatographic analysis module, and a signal acquisition and storage module, and the sample injection module, the chromatographic analysis module, and the signal acquisition and storage module are connected in sequence.

[0019] The sample injection module is used to dissolve and dilute the sample containing the photoinitiator, and then inject the dissolved and diluted sample into a pre-set chromatographic instrument.

[0020] The chromatography analysis module is used to perform chromatographic analysis on dissolved and diluted samples using a chromatographic instrument based on thin-layer chromatography.

[0021] The signal acquisition and storage module is used to acquire and store the detection signals of each component of the photoinitiator sample through a mass spectrometer detector.

[0022] Preferably, the indicator acquisition unit includes: a preprocessing module, a signal decomposition module, and a purity indicator determination module, and the preprocessing module, the signal decomposition module, and the purity indicator determination module are connected in sequence.

[0023] The preprocessing module is used to perform baseline correction and noise filtering on the obtained chromatographic data;

[0024] The signal decomposition module is used to perform scale analysis on the processed chromatographic data through wavelet transform and extract wavelet coefficients at each scale.

[0025] The purity index determination module is used to extract features from the peaks at each scale based on the extracted wavelet coefficients to determine the purity index of the photoinitiator.

[0026] Preferably, based on the extracted wavelet coefficients, feature extraction is performed on the peaks at each scale to determine the purity indicators of the photoinitiator, including:

[0027] Based on the extracted wavelet coefficients, peaks in chromatographic data are identified and their starting points, peak apex points, and ending points are located.

[0028] Based on the identification results, determine the characteristic parameters of the peaks in the chromatographic data;

[0029] By utilizing the extracted feature parameters and combining them with prior knowledge, the purity index of the photoinitiator is determined.

[0030] Preferably, based on the interval importance judgment matrix, an objective optimization model is established, and the optimal weight values ​​for each purity index data are determined through an improved radial shift algorithm, including:

[0031] For the interval numbers in the interval importance judgment matrix, the degree of separation between each interval number is calculated using the degree of separation theory;

[0032] The objective function is to minimize the sum of the discontinuities of the interval importance judgment matrix, and the objective optimization model is established with the constraints that the sum of the weights of each purity index data is 1 and the range of the weight values.

[0033] By using the objective function as the fitness function, and solving the objective optimization model through an improved radial shift algorithm, the optimal weight values ​​for each purity index data are obtained.

[0034] Preferably, the formula for calculating the degree of separation between the intervals using the separation theory is as follows:

[0035] ;

[0036] In the formula, G ( B ij , K ij ) represents the interval number B ij and K ij The degree of separation;

[0037] K ij Indicates the first i The purity index data and the first j The pairwise judgment range of purity index data when comparing importance;

[0038] ( x ij , y ij ) represents the interval number B ij The range of intervals;

[0039] n This indicates the number of purity indicators.

[0040] Preferably, the objective function is used as the fitness function, and the target optimization model is solved by an improved radial shift algorithm to obtain the optimal weight values ​​for each purity index data, including:

[0041] Initialize the parameters of the improved radial movement algorithm and randomly generate a population of weighted solutions;

[0042] The objective function is used as the fitness function, and the fitness function value for each weighted solution is calculated.

[0043] For each weighted solution, calculate its fitness function value and update the weighted solution;

[0044] The fitness evaluation and weight solution update are repeated through iterative optimization until a pre-set number of iterations is reached, and the optimal weight values ​​of each purity index data are output.

[0045] Preferably, the fitness function is calculated using the following formula:

[0046] ;

[0047] In the formula, f Represents the fitness function;

[0048] G ( B ij ,K ij ) represents the interval number B ij and K ij The degree of separation;

[0049] K ij Indicates the first i The purity index data and the first j The pairwise judgment range of purity index data when comparing importance;

[0050] ( x ij , y ij ) represents the interval number B ij The range of intervals;

[0051] n This indicates the number of purity indicators;

[0052] w i Indicates the first i The optimal weight value for each purity index data;

[0053] w j Indicates the first j The optimal weight value for each purity index data.

[0054] Preferably, determining the relative membership degree of each purity index based on the membership degree method and constructing the relative superiority matrix of each purity index includes:

[0055] An evaluation scale is set for each purity index, and the membership degree of each purity index on each evaluation scale is calculated using a membership function.

[0056] Compare the membership degree of each purity index on the same evaluation scale, and determine the membership degree of each index relative to other indexes to obtain the relative membership degree;

[0057] Based on the relative membership values ​​of each purity index relative to other indices, construct a relative superiority matrix for each purity index.

[0058] According to another aspect of the present invention, a photoinitiator preparation process is provided, the photoinitiator preparation process comprising the following steps:

[0059] S1. Acyl chloride, aluminum trichloride and solvent are reacted with benzene in a Friedel-Crafts reaction, and the resulting Friedel-Crafts reaction product is sent to a gas-liquid separator to separate hydrogen chloride gas and the first reaction liquid.

[0060] S2. Quench the reaction with hydrochloric acid in the first reaction solution, and allow it to stand and separate after quenching. Add the organic phase to a thin film evaporator to separate the solvent and obtain the Friedel-Crafts reaction intermediate.

[0061] S3. Mix the Friedel-Crafts reaction intermediate with chlorine gas to carry out a chlorination reaction, and separate the chlorination reaction products into a gas and a second reaction liquid.

[0062] S4. Mix sodium hydroxide solution with phase transfer catalyst evenly, and then hydrolyze the mixture with the second reaction solution. The hydrolysis product is then fed into a buffer tank and a phase separation tank in sequence to obtain a crude product organic phase. The photoinitiator is obtained by purifying the crude product organic phase.

[0063] S5. Obtain photoinitiator samples and use chromatographic analysis to obtain chromatographic data of the photoinitiator samples;

[0064] S6. Based on the obtained chromatographic data, extract the purity index data of the photoinitiator using wavelet transform.

[0065] S7. Based on the purity index data of the photoinitiator, evaluate and verify the purity of the photoinitiator using the target decision evaluation method.

[0066] S8. Compare the obtained evaluation results with the pre-set evaluation criteria and determine whether the photoinitiator meets the preparation requirements.

[0067] Compared with the prior art, the present invention provides a verification system and preparation process for photoinitiator preparation, which has the following beneficial effects:

[0068] (1) The present invention forms an automated process by sequentially connecting the sample analysis unit, the index acquisition unit, the purity evaluation unit and the standard comparison unit. This not only improves the efficiency of verification, but also reduces the error of human operation and ensures the accuracy of verification results. The purity of photoinitiator is evaluated and verified based on the target decision evaluation method. This evaluation method combines professional knowledge and practical experience, and can scientifically and objectively evaluate the purity of photoinitiator. The evaluation results are compared with the pre-set evaluation standards. This standardized comparison method ensures the objectivity and consistency of the verification results. Through comparison, it can be determined whether the photoinitiator meets the preparation requirements, providing a reliable basis for subsequent production and use.

[0069] (2) The present invention performs baseline correction and noise filtering on chromatographic data, which effectively improves the signal-to-noise ratio of the data and enhances the reliability and accuracy of the data. The processed chromatographic data is subjected to scale analysis by wavelet transform, and wavelet coefficients at each scale are extracted. This multi-scale analysis method can capture the characteristics of different frequency components in the chromatographic data, thereby achieving more refined feature extraction.

[0070] (3) This invention takes minimizing the sum of the disjointness of the interval importance judgment matrix as the objective function, which can ensure that the weight allocation is more reasonable, reduce the bias caused by subjective judgment, and help improve the accuracy and reliability of purity assessment. By improving the radial movement algorithm to solve the target optimization model, the optimal weight value of each purity index data can be found efficiently. Combining random and deterministic search strategies, it has a fast convergence speed and good global search capability. Based on the membership method, the relative superiority of each purity index is determined, which can clearly reflect the performance of each purity index on the evaluation scale. By calculating the disjointness and membership between interval numbers, the relationship and difference between purity indexes can be understood in depth, providing rich information for purity assessment, which helps to interpret the assessment results and guide subsequent optimization work. Attached Figure Description

[0071] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0072] Figure 1 This is a schematic diagram of a verification system for the photoinitiator preparation process according to an embodiment of the present invention;

[0073] Figure 2 This is a flowchart of the photoinitiator preparation process according to an embodiment of the present invention.

[0074] In the picture:

[0075] 1. Sample analysis unit; 2. Indicator acquisition unit; 3. Purity assessment unit; 4. Standard comparison unit. Detailed Implementation

[0076] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0077] According to embodiments of the present invention, a verification system and its preparation process for photoinitiator preparation are provided.

[0078] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1As shown, the verification system and preparation process of the photoinitiator preparation process according to an embodiment of the present invention include: a sample analysis unit 1, an index acquisition unit 2, a purity evaluation unit 3 and a standard comparison unit 4, and the sample analysis unit 1, the index acquisition unit 2, the purity evaluation unit 3 and the standard comparison unit 4 are connected in sequence.

[0079] Sample analysis unit 1 is used to acquire photoinitiator samples and obtain chromatographic data of the photoinitiator samples using chromatographic analysis.

[0080] In a preferred embodiment, the sample analysis unit 1 includes a sample injection module, a chromatographic analysis module, and a signal acquisition and storage module, and the sample injection module, the chromatographic analysis module, and the signal acquisition and storage module are connected in sequence.

[0081] The sample injection module is used to dissolve and dilute the sample containing the photoinitiator, and then inject the dissolved and diluted sample into a pre-set chromatographic instrument.

[0082] It should be noted that a suitable solvent should be selected based on the chemical properties of the photoinitiator. Commonly used solvents include methanol, acetonitrile, water, or mixtures thereof. Depending on the analytical requirements, the dissolved sample may need to be further diluted to achieve a concentration range suitable for chromatographic analysis. The diluted sample should be thoroughly mixed to ensure its homogeneity.

[0083] The chromatography analysis module is used to perform chromatographic analysis on dissolved and diluted samples using a chromatographic instrument based on thin-layer chromatography.

[0084] It should be noted that thin-layer chromatography (TLC) is a commonly used chromatographic technique, particularly suitable for separating and identifying compounds in complex mixtures. In TLC, samples are separated on a thin-layer plate coated with a stationary phase.

[0085] The signal acquisition and storage module is used to acquire and store the detection signals of each component of the photoinitiator sample through a mass spectrometer detector.

[0086] It's important to note that mass spectrometry detectors convert photoinitiator sample molecules into ions. This is typically achieved through an ionization source, such as electron bombardment ionization or chemical ionization. During ionization, sample molecules lose or gain electrons, forming charged ions. These ionized ions are then introduced into the analyzer of the mass spectrometer. The analyzer's primary function is to separate ions based on their mass and charge for subsequent detection. Within the analyzer, ions are deflected or separated by an electric or magnetic field. Ions of different masses experience different forces in the electric or magnetic field, causing them to move along different paths, thus achieving mass separation. The separated ions then enter the detector. The detector measures the relative abundance of ions (i.e., the number of ions) and generates a corresponding signal.

[0087] The index acquisition unit 2 is used to extract the purity index data of the photoinitiator based on the obtained chromatographic data using wavelet transform.

[0088] In a preferred embodiment, the index acquisition unit 2 includes a preprocessing module, a signal decomposition module, and a purity index determination module, and the preprocessing module, the signal decomposition module, and the purity index determination module are connected in sequence.

[0089] The preprocessing module is used to perform baseline correction and noise filtering on the obtained chromatographic data;

[0090] It should be noted that the purpose of baseline correction is to eliminate the influence of instrument offset and noise on the actual sample signal. Common baseline correction methods include zero-point correction, reference material method, and linear model method.

[0091] Noise removal aims to reduce random errors and interference signals in chromatographic data, thereby improving the signal-to-noise ratio. Common noise removal methods include data smoothing and Fourier filtering.

[0092] The signal decomposition module is used to perform scale analysis on the processed chromatographic data through wavelet transform and extract wavelet coefficients at each scale.

[0093] Specifically, performing scale analysis on the processed chromatographic data using wavelet transform and extracting wavelet coefficients at each scale includes the following steps:

[0094] Wavelet transform is performed on the processed chromatographic data using wavelet basis functions; wavelet transform decomposes the original signal into a series of wavelet coefficients, which describe the characteristics of the signal at different time-frequency scales.

[0095] After obtaining the wavelet coefficients, scaling analysis can be performed. Scaling analysis refers to studying the characteristics of a signal at different scales. For chromatographic data, this can be achieved by observing the wavelet coefficients at different scales.

[0096] Based on the results of the scale analysis, wavelet coefficients at each scale can be extracted, and these coefficients contain detailed information about the signal at different scales.

[0097] The purity index determination module is used to extract features from the peaks at each scale based on the extracted wavelet coefficients to determine the purity index of the photoinitiator.

[0098] As a preferred embodiment, based on the extracted wavelet coefficients, feature extraction is performed on the peaks at each scale to determine the purity index of the photoinitiator, including:

[0099] Based on the extracted wavelet coefficients, peaks in chromatographic data are identified and their starting points, peak apex points, and ending points are located.

[0100] It should be noted that by analyzing the extracted wavelet coefficients, chromatographic peaks are identified. Chromatographic peaks typically represent local maxima or minima of wavelet coefficients. The starting point, highest point (peak apex), and ending point of each peak are determined. This usually involves finding the inflection points in the coefficients, i.e., the points where the coefficients change from positive to negative or vice versa.

[0101] Based on the identification results, determine the characteristic parameters of the peaks in the chromatographic data;

[0102] It should be noted that, based on the position of each peak, characteristic parameters such as peak height (the amplitude of the peak coefficient), peak width (the distance between the starting point and the ending point) and peak area (the total area under the peak, which can be obtained through the integral coefficient) are extracted.

[0103] By utilizing the extracted feature parameters and combining them with prior knowledge, the purity index of the photoinitiator is determined.

[0104] It should be noted that the characteristic parameters of the chromatographic peaks identified and located through wavelet transform (such as start point, peak apex, end point, peak height, peak width, and peak area) are compiled. The importance of each parameter in determining purity indicators is evaluated. For example, peak area is usually directly related to component concentration, while peak shape and width can provide information about sample purity and component resolution.

[0105] Based on prior knowledge and considering the chemical properties of the photoinitiator, the most relevant index is selected from the characteristic parameters. For example:

[0106] If purity and concentration are of primary concern, peak area and peak height are likely the main parameters to consider.

[0107] If the focus is on component identification, retention time and peak shape parameters (such as tail factor or symmetry factor) become more important.

[0108] Quantitative chemical analysis methods, such as external standard method or internal standard method, are used to calculate the absolute or relative content of each component.

[0109] Purity assessment unit 3 is used to assess and verify the purity of photoinitiators based on purity index data and target decision evaluation methods.

[0110] In a preferred embodiment, the purity evaluation unit 3 includes: a judgment matrix construction module, an optimal weight determination module, a superiority matrix construction module, an evaluation value calculation module, and a comprehensive evaluation calculation module, and the judgment matrix construction module, the optimal weight determination module, the superiority matrix construction module, the evaluation value calculation module, and the comprehensive evaluation calculation module are connected sequentially.

[0111] The judgment matrix construction module is used to compare the purity index data of photoinitiators pairwise based on the interval hierarchical analysis method, and construct the interval importance judgment matrix based on the comparison results.

[0112] Specifically, based on the interval analytic hierarchy process (AHP), the purity index data of the photoinitiator are compared pairwise, and an interval importance judgment matrix is ​​constructed based on the comparison results. This includes the following steps:

[0113] For each purity metric, pairwise comparisons are needed to determine their relative importance. These comparisons are typically based on expert opinion or historical data; the result of each pairwise comparison is no longer a fixed value (such as a 1-9 scale), but rather a range. For example, if metric A is considered slightly more important than metric B, then a range such as [1.2, 1.5] is given, rather than a fixed number such as 1.3.

[0114] The results of pairwise comparisons are combined into a matrix, namely the interval importance judgment matrix. Each row of this matrix represents an indicator, and each column also represents an indicator. The elements in the matrix represent the interval importance of the row indicator relative to the column indicator.

[0115] Suppose we have three purity indices A, B, and C, and the results of pairwise comparisons are as follows:

[0116] A is slightly more important than B: [1.2, 1.5];

[0117] A is significantly more important than C: [2.0, 2.5];

[0118] B is slightly less important than A (i.e., the reciprocal of A being slightly more important than B): [1 / 1.5, 1 / 1.2] = [0.67, 0.83];

[0119] The importance of B relative to C is between that of A and C: [1.5, 2.0] (hypothesis);

[0120] The importance of C relative to A is the reciprocal of the importance of A relative to C: [1 / 2.5, 1 / 2.0] = [0.4, 0.5];

[0121] The importance of C relative to B is the reciprocal of the importance of B relative to C: [1 / 2.0, 1 / 1.5] = [0.5, 0.67].

[0122] The optimal weight determination module is used to establish a weight target optimization model based on the interval importance judgment matrix and determine the optimal weight value of each purity index through an improved radial shift algorithm.

[0123] As a preferred implementation, based on the interval importance judgment matrix, a target optimization model is established, and the optimal weight values ​​of each purity index data are determined through an improved radial shift algorithm, including:

[0124] For the interval numbers in the interval importance judgment matrix, the degree of separation between each interval number is calculated using the degree of separation theory;

[0125] Specifically, the formula for calculating the degree of separation between the numbers in each interval using the separation theory is as follows:

[0126] ;

[0127] In the formula, G ( B ij , K ij ) represents the interval number B ij and K ij The degree of separation;

[0128] K ij Indicates the first i The purity index data and the first j The pairwise judgment range of purity index data when comparing importance;

[0129] ( x ij , y ij ) represents the interval number B ij The range of intervals;

[0130] n This indicates the number of purity indicators.

[0131] The objective function is to minimize the sum of the discontinuities of the interval importance judgment matrix, and the objective optimization model is established with the constraints that the sum of the weights of each purity index data is 1 and the range of the weight values.

[0132] By using the objective function as the fitness function, and solving the objective optimization model through an improved radial shift algorithm, the optimal weight values ​​for each purity index data are obtained.

[0133] In a preferred embodiment, the objective function is used as the fitness function, and the target optimization model is solved by an improved radial shift algorithm to obtain the optimal weight values ​​for each purity index data, including:

[0134] Initialize the parameters of the improved radial movement algorithm and randomly generate a population of weighted solutions;

[0135] It should be noted that the parameters for initializing the improved radial movement algorithm include population size, number of iterations, mutation rate, etc. A population of weighted solutions is randomly generated, and each weighted solution represents a possible combination of weights, where the weight values ​​should satisfy the normalization condition (the sum of the weights is 1).

[0136] The objective function is used as the fitness function, and the fitness function value for each weighted solution is calculated.

[0137] Specifically, the formula for calculating the fitness function is:

[0138] ;

[0139] In the formula, f Represents the fitness function;

[0140] G ( B ij , K ij ) represents the interval number B ij and K ij The degree of separation;

[0141] K ij Indicates the first i The purity index data and the first j The pairwise judgment range of purity index data when comparing importance;

[0142] ( x ij , y ij ) represents the interval number B ij The range of intervals;

[0143] n This indicates the number of purity indicators;

[0144] w i Indicates the first i The optimal weight value for each purity index data;

[0145] w j Indicates the first j The optimal weight value for each purity index data;

[0146] When the i The purity index data and the first j When all purity index data have optimal weights, ( B ij , wi / w j ) represents a point w i / w j to interval number B ij The degree of separation.

[0147] For each weighted solution, calculate its fitness function value and update the weighted solution;

[0148] Specifically, you can evaluate the quality of each weighted solution based on the fitness function value. A smaller fitness function value generally indicates a better solution, and based on the evaluation results, you can use different strategies to update the weighted solutions. This typically involves the following steps:

[0149] Selection: Select some "good" weighted solutions from the current population. These solutions will be used to generate new weighted solutions. The selection strategy can be based on the ranking of fitness function values ​​(such as roulette wheel selection) or other more complex selection methods.

[0150] Crossover (or recombination): Selected weighted solutions are paired and their information is exchanged in some way (such as crossover operation) to generate new weighted solutions.

[0151] Mutation: Making small, random modifications to the new weighted solutions (such as changing a weight value) to increase population diversity and explore new search spaces. Mutation also helps prevent the algorithm from prematurely converging to a local optimum.

[0152] The newly generated weighted solutions (which may also include some unmodified "good" weighted solutions) are added to the population, and some weighted solutions with lower fitness may be removed to keep the population size unchanged.

[0153] The fitness evaluation and weight solution update are repeated through iterative optimization until a pre-set number of iterations is reached, and the optimal weight values ​​of each purity index data are output.

[0154] The eugenics matrix construction module is used to determine the relative membership degree of each purity index based on the membership degree method, and to construct the relative eugenics matrix of each purity index.

[0155] As a preferred implementation, determining the relative membership degree of each purity index based on the membership degree method and constructing the relative membership degree matrix of each purity index includes:

[0156] An evaluation scale is set for each purity index, and the membership degree of each purity index on each evaluation scale is calculated using a membership function.

[0157] It should be noted that one or more evaluation scales are set for each purity index, such as very high, high, medium, low and very low. These scales will be used for subsequent membership calculations.

[0158] For each evaluation scale, determine its central value (i.e., the most typical purity index value at that scale) and standard deviation. The central value typically corresponds to the midpoint or representative value of the evaluation scale, while the standard deviation is determined based on your desired dispersion of the membership function. For each purity index value and each evaluation scale, use a Gaussian membership function to calculate its membership degree.

[0159] Compare the membership degree of each purity index on the same evaluation scale, and determine the membership degree of each index relative to other indexes to obtain the relative membership degree;

[0160] It should be noted that for each evaluation scale, the membership values ​​of each indicator are compared. For example, for the evaluation scale "high", the membership values ​​of all indicators on this scale are collected.

[0161] For each indicator, calculate the ratio of its membership degree on each evaluation scale to the membership degree of all other indicators on the same scale to obtain the relative membership degree.

[0162] Based on the relative membership values ​​of each purity index relative to other indices, construct a relative superiority matrix for each purity index.

[0163] The evaluation value calculation module is used to calculate the evaluation value of each purity index based on the optimal weight value and the relative superiority matrix of each purity index.

[0164] It should be noted that the evaluation value of each purity index is obtained by multiplying the optimal weight value of each purity index with the relative superiority matrix.

[0165] The comprehensive evaluation calculation module is used to comprehensively evaluate the purity of the photoinitiator based on the evaluation values ​​of each purity index, and obtain a comprehensive evaluation value.

[0166] It should be noted that the purity of the photoinitiator can be comprehensively evaluated based on the evaluation values ​​of each purity index, and a comprehensive evaluation value can be obtained by calculating the weighted average of these evaluation values.

[0167] Standard comparison unit 4 is used to compare the obtained evaluation results with the pre-set evaluation standards and determine whether the photoinitiator meets the preparation requirements.

[0168] Specifically, comparing the obtained evaluation results with pre-set evaluation criteria and determining whether the photoinitiator meets the preparation requirements includes the following steps:

[0169] Evaluation criteria are determined based on previous research, industry standards, or specific application requirements; for example, the overall evaluation value of a photoinitiator may need to reach 0.75 or higher to be considered to meet the preparation requirements.

[0170] The calculated comprehensive evaluation value of the photoinitiator is compared with the preset standard.

[0171] Based on the comparison results, determine whether the photoinitiator meets the preparation requirements. If the assessed value is lower than the standard, it may be necessary to adjust the production process or formulation to meet the requirements.

[0172] If the photoinitiator does not meet the requirements, analyze which purity indicators are insufficient and explore possible improvement measures. These may include changing the proportions of certain chemical components or adjusting processing conditions. After implementing the improvements, conduct another purity assessment to ensure the new results meet the preset standards.

[0173] like Figure 2 As shown, according to another embodiment of the present invention, a photoinitiator preparation process is provided, which includes the following steps:

[0174] S1. Acyl chloride, aluminum trichloride and solvent are reacted with benzene in a Friedel-Crafts reaction, and the resulting Friedel-Crafts reaction product is sent to a gas-liquid separator to separate hydrogen chloride gas and the first reaction liquid.

[0175] S2. Quench the reaction with hydrochloric acid in the first reaction solution, and allow it to stand and separate after quenching. Add the organic phase to a thin film evaporator to separate the solvent and obtain the Friedel-Crafts reaction intermediate.

[0176] S3. Mix the Friedel-Crafts reaction intermediate with chlorine gas to carry out a chlorination reaction, and separate the chlorination reaction products into a gas and a second reaction liquid.

[0177] S4. Mix sodium hydroxide solution with phase transfer catalyst evenly, and then hydrolyze the mixture with the second reaction solution. The hydrolysis product is then fed into a buffer tank and a phase separation tank in sequence to obtain a crude product organic phase. The photoinitiator is obtained by purifying the crude product organic phase.

[0178] S5. Obtain photoinitiator samples and use chromatographic analysis to obtain chromatographic data of the photoinitiator samples;

[0179] S6. Based on the obtained chromatographic data, extract the purity index data of the photoinitiator using wavelet transform.

[0180] S7. Based on the purity index data of the photoinitiator, evaluate and verify the purity of the photoinitiator using the target decision evaluation method.

[0181] S8. Compare the obtained evaluation results with the pre-set evaluation criteria and determine whether the photoinitiator meets the preparation requirements.

[0182] In summary, by utilizing the above-mentioned technical solution of this invention, an automated process is formed through the sequential connection of sample analysis unit 1, index acquisition unit 2, purity evaluation unit 3, and standard comparison unit 4. This not only improves the efficiency of verification but also reduces human error, ensuring the accuracy of verification results. The purity of the photoinitiator is evaluated and verified based on a target decision evaluation method. This evaluation method combines professional knowledge and practical experience, enabling a scientific and objective evaluation of the photoinitiator's purity. The evaluation results are compared with pre-set evaluation standards; this standardized comparison method ensures the objectivity and consistency of the verification results. Through comparison, it can be clearly determined whether the photoinitiator meets the preparation requirements, providing a reliable basis for subsequent production and use. This invention performs baseline correction and noise filtering on chromatographic data, effectively improving the signal-to-noise ratio and enhancing the reliability and accuracy of the data. Wavelet transform is used to scale the processed chromatographic data. This invention analyzes and extracts wavelet coefficients at various scales. This multi-scale analysis method can capture the characteristics of different frequency components in chromatographic data, thereby achieving more refined feature extraction. The invention uses minimizing the sum of the disjointness of the interval importance judgment matrix as the objective function, ensuring a more reasonable weight allocation, reducing bias caused by subjective judgment, and helping to improve the accuracy and reliability of purity assessment. By improving the radial shift algorithm to solve the target optimization model, the optimal weight values ​​for each purity index data can be found efficiently. Combining random and deterministic search strategies, it has a fast convergence speed and good global search capability. Based on the membership method, the relative superiority of each purity index is determined, which can clearly reflect the performance of each purity index on the evaluation scale. By calculating the disjointness and membership between intervals, the relationships and differences between purity indices can be deeply understood, providing rich information for purity assessment, helping to interpret the assessment results and guide subsequent optimization work.

[0183] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.

[0184] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A verification system for the preparation process of a photoinitiator, characterized in that, The verification system for the photoinitiator preparation process includes: a sample analysis unit, an index acquisition unit, a purity assessment unit, and a standard comparison unit, and the sample analysis unit, the index acquisition unit, the purity assessment unit, and the standard comparison unit are connected in sequence. The sample analysis unit is used to acquire photoinitiator samples and obtain chromatographic data of the photoinitiator samples using chromatographic analysis. The index acquisition unit is used to extract the purity index data of the photoinitiator based on the obtained chromatographic data using wavelet transform. The purity assessment unit is used to assess and verify the purity of the photoinitiator based on the purity index data of the photoinitiator and the target decision evaluation method. The standard comparison unit is used to compare the obtained evaluation results with the pre-set evaluation standards and determine whether the photoinitiator meets the preparation requirements. The purity evaluation unit includes: a judgment matrix construction module, an optimal weight determination module, a superiority matrix construction module, an evaluation value calculation module, and a comprehensive evaluation calculation module, wherein the judgment matrix construction module, the optimal weight determination module, the superiority matrix construction module, the evaluation value calculation module, and the comprehensive evaluation calculation module are connected sequentially. The judgment matrix construction module is used to compare the purity index data of photoinitiators pairwise based on the interval hierarchical analysis method, and construct an interval importance judgment matrix based on the comparison results. The optimal weight determination module is used to establish a weight target optimization model based on the interval importance judgment matrix and determine the optimal weight value of each purity index through an improved radial shift algorithm. The superiority matrix construction module is used to determine the relative membership degree of each purity index based on the membership degree method, and to construct the relative superiority matrix of each purity index. The evaluation value calculation module is used to calculate the evaluation value of each purity index based on the optimal weight value and the relative superiority matrix of each purity index. The comprehensive evaluation calculation module is used to comprehensively evaluate the purity of the photoinitiator based on the evaluation values ​​of each purity index, and obtain a comprehensive evaluation value.

2. The verification system for the photoinitiator preparation process according to claim 1, characterized in that, The sample analysis unit includes a sample injection module, a chromatographic analysis module, and a signal acquisition and storage module, and the sample injection module, the chromatographic analysis module, and the signal acquisition and storage module are connected in sequence. The sample injection module is used to dissolve and dilute the sample containing the photoinitiator, and then inject the dissolved and diluted sample into a pre-set chromatographic instrument. The chromatographic analysis module is used to perform chromatographic analysis on dissolved and diluted samples using a chromatographic instrument based on thin-layer chromatography. The signal acquisition and storage module is used to acquire and store the detection signals of each component of the photoinitiator sample through a mass spectrometer detector.

3. The verification system for the photoinitiator preparation process according to claim 1, characterized in that, The index acquisition unit includes a preprocessing module, a signal decomposition module, and a purity index determination module, and the preprocessing module, the signal decomposition module, and the purity index determination module are connected in sequence. The preprocessing module is used to perform baseline correction and noise filtering on the obtained chromatographic data; The signal decomposition module is used to perform scale analysis on the processed chromatographic data through wavelet transform and extract wavelet coefficients at each scale. The purity index determination module is used to extract features from the peaks at each scale based on the extracted wavelet coefficients to determine the purity index of the photoinitiator.

4. The verification system for the photoinitiator preparation process according to claim 3, characterized in that, The process of extracting features from peaks at each scale based on the extracted wavelet coefficients to determine the purity index of the photoinitiator includes: Based on the extracted wavelet coefficients, peaks in chromatographic data are identified and their starting points, peak apex points, and ending points are located. Based on the identification results, determine the characteristic parameters of the peaks in the chromatographic data; By utilizing the extracted feature parameters and combining them with prior knowledge, the purity index of the photoinitiator is determined.

5. The verification system for the photoinitiator preparation process according to claim 1, characterized in that, The process of establishing a target optimization model based on the interval importance judgment matrix and determining the optimal weight values ​​for each purity index data using an improved radial shift algorithm includes: For the interval numbers in the interval importance judgment matrix, the degree of separation between each interval number is calculated using the degree of separation theory; The objective function is to minimize the sum of the discontinuities of the interval importance judgment matrix, and the objective optimization model is established with the constraints that the sum of the weights of each purity index data is 1 and the range of the weight values. By using the objective function as the fitness function, and solving the objective optimization model through an improved radial shift algorithm, the optimal weight values ​​for each purity index data are obtained.

6. The verification system for the photoinitiator preparation process according to claim 5, characterized in that, The formula for calculating the degree of separation between intervals using the separation theory is as follows: ； In the formula, G ( B ij , K ij ) represents the interval number B ij and K ij The degree of separation; K ij Indicates the first i The purity index data and the first j The pairwise judgment range of purity index data when comparing importance; ( x ij , y ij ) represents the interval number B ij The range of intervals.

7. The verification system for the photoinitiator preparation process according to claim 5, characterized in that, The step of using the objective function as the fitness function and solving the objective optimization model through an improved radial shift algorithm to obtain the optimal weight values ​​for each purity index data includes: Initialize the parameters of the improved radial movement algorithm and randomly generate a population of weighted solutions; The objective function is used as the fitness function, and the fitness function value for each weighted solution is calculated. For each weighted solution, calculate its fitness function value and update the weighted solution; The fitness evaluation and weight solution update are repeated through iterative optimization until a pre-set number of iterations is reached, and the optimal weight values ​​of each purity index data are output.

8. The verification system for the photoinitiator preparation process according to claim 7, characterized in that, The fitness function is calculated using the following formula: ； In the formula, f Represents the fitness function; G ( B ij , K ij ) represents the interval number B ij and K ij The degree of separation; K ij Indicates the first i The purity index data and the first j The pairwise judgment range of purity index data when comparing importance; n This indicates the number of purity indicators; w i Indicates the first i The optimal weight value for each purity index data; w j Indicates the first j The optimal weight value for each purity index data.

9. The verification system for the photoinitiator preparation process according to claim 8, characterized in that, The method of determining the relative membership degree of each purity index based on membership degree and constructing the relative superiority matrix of each purity index includes: An evaluation scale is set for each purity index, and the membership degree of each purity index on each evaluation scale is calculated using a membership function. Compare the membership degree of each purity index on the same evaluation scale, and determine the membership degree of each index relative to other indexes to obtain the relative membership degree; Based on the relative membership values ​​of each purity index relative to other indices, construct a relative superiority matrix for each purity index.

10. A process for preparing a photoinitiator, used to prepare the photoinitiator according to any one of claims 1-9, characterized in that, The photoinitiator preparation process includes the following steps: S1. Acyl chloride, aluminum trichloride and solvent are reacted with benzene in a Friedel-Crafts reaction, and the resulting Friedel-Crafts reaction product is sent to a gas-liquid separator to separate hydrogen chloride gas and the first reaction liquid. S2. Quench the reaction with hydrochloric acid in the first reaction solution, and allow it to stand and separate after quenching. Add the organic phase to a thin film evaporator to separate the solvent and obtain the Friedel-Crafts reaction intermediate. S3. Mix the Friedel-Crafts reaction intermediate with chlorine gas to carry out a chlorination reaction, and separate the chlorination reaction products into a gas and a second reaction liquid. S4. Mix sodium hydroxide solution with phase transfer catalyst evenly, and then hydrolyze the mixture with the second reaction solution. The hydrolysis product is then fed into a buffer tank and a phase separation tank in sequence to obtain a crude product organic phase. The photoinitiator is obtained by purifying the crude product organic phase. S5. Obtain photoinitiator samples and use chromatographic analysis to obtain chromatographic data of the photoinitiator samples; S6. Based on the obtained chromatographic data, extract the purity index data of the photoinitiator using wavelet transform. S7. Based on the purity index data of the photoinitiator, evaluate and verify the purity of the photoinitiator using the target decision evaluation method. S8. Compare the obtained evaluation results with the pre-set evaluation criteria and determine whether the photoinitiator meets the preparation requirements.

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