Process control for synthesis of intermediates

Abstract

This invention belongs to the field of organic synthesis process control technology. It provides a method for controlling the alkylation process in intermediate synthesis, comprising: performing structural analysis on historical alkylation reaction products of the same reaction system to identify target sites, side reaction sites, and corresponding products; calculating the binding rate of the target site and the proportion of side reaction products; predicting the risk of low site selectivity based on stability analysis; if a risk exists, determining whether it is caused by a small difference in activity between the target site and the side reaction sites through quantum computing simulation and model compound experiments; determining the critical value of the site activity regulation parameter; clarifying the regulation priority through correlation analysis; and finally regulating the parameters to within the critical value according to the priority. This achieves precise prediction and control of the alkylation process, significantly improving the site selectivity of intermediate synthesis, reducing by-product generation, and reducing experimental resource waste.

Description

Technical Field

[0001] This invention belongs to the field of organic synthesis process control technology, specifically a method for controlling the alkyl process in intermediate synthesis. Background Technology

[0002] In the alkylation process of intermediate synthesis, site selectivity is a key factor affecting product purity and yield. Traditional processes rely on experience to adjust reaction conditions (such as temperature and catalyst), which makes it difficult to accurately predict and control side reactions at non-target sites, often leading to problems such as excessive side reaction products and low purity of the target product.

[0003] In existing technologies, traditional processes rely heavily on the purity of the product from a single reaction or the direct comparison of peak areas from thin-layer chromatography (TLC) and high-performance liquid chromatography (HPLC) to determine site selectivity. This lacks in-depth analysis of the differences between the target site and the side reaction sites. When low site selectivity occurs, existing technologies struggle to trace the root cause at the molecular level. The site selectivity of alkylation reactions is affected by various parameters (such as temperature, catalyst acidity, and solvent polarity), but existing technologies are somewhat blind in their parameter control. Due to the lack of systematic integration and fluctuation analysis of historical data, multiple reactions under the same reaction system often show selectivity differences (e.g., the purity of the target product fluctuates by more than 10% between different batches). This makes it difficult to meet the stringent purity requirements of intermediates in high-end fields such as pharmaceuticals and electronic chemicals (usually ≥99.5%), leading to a surge in subsequent separation and purification costs, and even batch scrapping due to excessive impurities.

[0004] Therefore, the present invention provides a method for controlling the alkyl process in the synthesis of intermediates. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is: a method for controlling the alkyl process in intermediate synthesis, comprising: By analyzing the structure of historical reaction products in multiple alkylation reaction systems, the sites in each alkylation reaction and the historical reaction products are classified. The site classification includes target sites and side reaction sites, and the historical reaction product classification includes target products and side reaction products. Based on the binding rate of target sites and the generation ratio of side reaction products, it is predicted whether the current alkylation reaction will exhibit low site selectivity. If so, through quantum computing simulations of the target site and the side reaction sites, as well as reaction transformation analysis of the model compounds generated by the target site and the side reaction sites, it can be determined whether the low site selectivity is due to the slight activity difference between the target site and the side reaction sites. If so, perform correlation analysis on the site activity regulation parameters and site selectivity to determine the critical values ​​of each site activity regulation parameter; Correlation analysis was performed on site activity regulation parameters, target site binding analysis results, and byproduct ratios to assess the influence of site activity regulation parameters on site selectivity. The regulatory priority of site activity regulation parameters was determined by combining the differences between site activity regulation parameters and critical values. The site activity regulation parameters are adjusted according to the regulation priority, and the site activity regulation parameters are adjusted to within the critical value.

[0007] Furthermore, the method for site segmentation and historical reaction products is as follows: The linkage sites of the alkylating reagents are determined by two-dimensional NMR, and the sites to be prepared for alkylation are listed and denoted as set S. Based on the reaction design, the desired binding sites are identified and denoted as the target site set T; Align the identified binding site set S with the target site set T: Sites belonging to target site T are target sites for binding to alkylating agents; Sites that do not belong to the target site T are non-target sites that bind to the alkylating agent, i.e., side reaction sites; The target product is the product generated solely by the binding of the alkylating agent to the target site, while the by-reaction product is the product generated by the binding of the alkylating agent to the by-reaction site.

[0008] Furthermore, the calculation process for the target site binding rate and the proportion of by-reaction products is as follows: The formula for calculating the proportion of target sites that bind to the alkylating agent to the total number of target sites in a single reaction is as follows: ; The amounts of the target product and each by-reaction product were determined by external standard HPLC / GC. The proportion of by-products in a single reaction is calculated to obtain the by-product ratio. The formula is as follows: .

[0009] Furthermore, the method for predicting whether the current reaction will exhibit low site selectivity is as follows: Calculate the coefficients of variation for the target site binding rate and the proportion of by-products in multiple alkylation reactions, and compare them with the preset coefficients of variation. If the coefficient of variation is less than the preset coefficient of variation, the average value of the target site binding rate and the proportion of by-products in each alkylation reaction is calculated as the proportion of by-products and the target site binding rate in the reaction system, that is, the predicted value of the proportion of by-products and the target site binding rate in the current reaction. The site selectivity judgment index is obtained by subtracting the predicted proportion of by-products from the target site binding rate, and then compared with the preset judgment index. If the site selectivity judgment index is greater than the threshold, it is predicted that the current response will exhibit low site selectivity.

[0010] Furthermore, the quantum computing simulation process for the target site and the side reaction site is as follows: Based on the reaction design, all the pre-set desired binding sites are classified into the target site set T, and all non-target binding sites are classified into the side reaction site set S. Obtain the electronic activity parameters and steric hindrance parameters of all sites in the target site set and the side reaction site set; For electronic activity parameters and steric hindrance parameters, calculate the overlap of the same confidence intervals for the target site set T and the secondary site set S, and compare it with the preset overlap. If the overlap is greater than the preset overlap, then the distribution overlap between the target site set T and the secondary site set S is high. The mean values ​​of electronic activity parameters and steric hindrance parameters of the target site set and the side reaction site set are calculated respectively, and then compared with the threshold after the difference is calculated. Furthermore, the reaction transformation analysis of the model compounds generated from the target site and the side reaction sites is performed as follows: Synthesize model compound A containing only the target site and model compound B containing only the side reaction sites; Under the same reaction conditions, model compound A containing only the target site and model compound B containing only the side reaction site were reacted with alkylating agents, and the reaction rate constants k(A) and k(B) were calculated by real-time monitoring. Equal amounts of model compounds A and B were mixed and reacted with an alkylating agent to ensure that only some sites were reacted. After the reaction reached equilibrium, the conversion rates of A and B were determined by HPLC / GC. For model compounds A and B, the ratio of reaction rate constant k(A) to k(B) and the conversion ratio are subtracted from 1, and the differences are compared with preset differences.

[0011] Furthermore, the method for determining whether the low site selectivity is due to a slight difference in activity between the target site and the side reaction site is as follows: The criterion for determining that low site selectivity is caused by a small difference in activity between the target site set and the parasite set is: The mean difference of electronic activity parameters between the target site set T and the secondary site set S is less than or equal to the theoretical error, the mean difference of spatial steric hindrance parameters is less than or equal to the threshold, and the distributions of electronic activity parameters and spatial steric hindrance parameters have a high degree of overlap. The differences between the individual reaction rate constant ratios and the conversion ratios in competing reactions of the model compounds and 1 are both less than the preset differences; If the criteria are met, the low site selectivity is due to the small difference in activity between the target site and the side reaction site.

[0012] Furthermore, the critical value of each point activity regulation parameter is determined as follows: For any site activity regulation parameter, collect the corresponding Y observation values ​​of parameter X across the entire range to form a basic dataset (X1,Y1), (X2,Y2)...(X...). n ,Y n ); Assuming there exists a critical value x0, the dataset is divided into a low-parameter segment and a high-parameter segment based on x0; For low-parameter range data, high-parameter range data, and full-parameter range data, respectively construct fitting models for the corresponding trends; Calculate the residual sums of squares RSS1 and RSS2 for the low-parameter segment model and the high-parameter segment model respectively, then the total residual sum of squares RSS of the piecewise model is obtained. 1+2 =RSS1+RSS2, where the residual is the sum of squared deviations between the observed value Y and the model prediction; The sum of squared residuals of the overall model is RSS 总 ; Perform an F-test on the piecewise model and the overall model to determine whether the piecewise model is superior to the overall model; For all candidate x0 within the preset range, repeat the parameter segmentation and model difference analysis process, and calculate the piecewise model goodness of fit R for each candidate x0. 2 And the results of the F-test; Among the candidate x0s that pass the F-test, select the piecewise model total R. 2 The highest point is the final critical value.

[0013] Furthermore, the method for determining the regulation priority of each point activity regulation parameter is as follows: Calculate the selectivity factor. , where r 目标 r represents the reaction rate at the target site. 副 The reaction rate at the side reaction site; For parameter x i Based on the initial value x0, set the change amount ∆x i ; For any site activity regulation parameter; With other parameters fixed, calculate the fluctuations in site activity regulation parameters. The sensitivity coefficient is obtained by measuring the rate of change of the selectivity factor caused by time. ,in, ,in, Indicates parameter x i The change in k, where k is the experimental group number. , representing the change in the selectivity factor, x i0 S0 and S0 are the initial values ​​of the parameter and the selectivity factor, respectively; Calculate the distance between each site's activity regulation parameter and its corresponding critical value to obtain the risk index R. For any site's activity regulation parameter, if the current value of the parameter is x and the critical value is x0, then: ; The absolute value of the sensitivity coefficient is added to the risk index to obtain the priority index. The site activity regulation parameters are then arranged in descending order of the priority index to obtain the regulation priority of the site activity regulation parameters.

[0014] Furthermore, the process of regulating the site activity regulation parameters according to the regulatory priority is as follows: For any site activity regulation parameter, compare the current value of the parameter with the critical value; If the current value exceeds the critical range, regulation needs to be initiated; The direction of regulation is determined by the direction of deviation. If the critical value is the upper limit, the current value should be reduced if it exceeds the standard. If the critical value is the lower limit, the current value should be increased if it is insufficient. With the minimum objective of just entering the critical value range, the smallest adjustment should be prioritized. After adjusting the site activity regulation parameter according to the determined direction and amplitude, stabilizing the reaction system, and then re-measuring the measured value of the parameter; Once the measured value is confirmed to be within the critical range, the parameter adjustment is complete. If it does not meet the standard, repeat the adjustment until it does.

[0015] The beneficial effects of this invention are as follows: By performing structural analysis on the products of multiple historical alkylation reactions in the same reaction system, target sites, side reaction sites, and corresponding products are identified. The binding rate of the target site and the proportion of side reaction products are calculated. Combined with stability analysis, the risk of low site selectivity is predicted, avoiding waste of experimental resources and accurately predicting risks. If risks exist, quantum computing simulation and model compound experiments are used to determine whether they are caused by the small difference in activity between the target site and the side reaction sites, avoiding blind adjustments and providing a theoretical basis for regulation. The root cause of regulation is clarified, and the critical value of the site activity regulation parameter is determined. The regulation priority is clarified through correlation analysis, and the parameters are finally regulated to within the critical value according to the priority. The critical value based on statistical testing ensures that the parameter regulation has a clear threshold, improving the accuracy of regulation. Parameter regulation according to priority can quickly improve selectivity at the lowest cost, improve the purity and yield of intermediates, realize the accurate prediction and regulation of alkylation process, significantly improve the site selectivity of intermediate synthesis, reduce the generation of by-products, and reduce the waste of experimental resources. Attached Figure Description

[0016] The invention will now be further described with reference to the accompanying drawings.

[0017] Figure 1 This is a flowchart of the steps of an alkyl process control method for intermediate synthesis according to the present invention; Figure 2 This is a logic diagram of an alkyl process control method for intermediate synthesis according to the present invention. Detailed Implementation

[0018] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0019] Please see Figure 1 As shown in the embodiment of the present invention, a method for controlling the alkylation process of intermediate synthesis includes the following steps: Step 1: By analyzing the structure of historical reaction products in multiple alkylation reaction systems, the sites in each alkylation reaction and historical reaction products are classified. The site classification includes target sites and side reaction sites, and the historical reaction product classification includes target products and side reaction products. Based on the binding rate of target sites and the generation ratio of side reaction products, it is predicted whether the current alkylation reaction will exhibit low site selectivity. Please see Figure 2 As shown, in step one, the same system means that the target molecule, alkylating agent, and reaction conditions are completely identical. The reaction conditions include, but are not limited to, solvent, temperature, catalyst, etc. The process of structural analysis of historical reaction products under the multiple isoalkylation reaction system includes: Obtain the reaction products from multiple alkylation reactions in the same historical and current reaction systems; For the reaction products of any single alkylation reaction: Structural analysis was used to identify all binding sites with the alkylating agent after the reaction. The identification criteria were: after binding with the alkylating agent, the chemical environment of the site undergoes characteristic changes, such as NMR chemical shift shifts and IR functional group peak changes. For example: After the hydroxyl group (-OH) is alkylated, 1 The characteristic peak of hydroxyl hydrogen (δ 4-6 ppm) disappears in H NMR, and the peak of alkyl hydrogen connected to oxygen appears (such as the δ 3.8 ppm single peak of -OCH3). After alkylation of amino groups (-NH2), the NH stretching vibration peak in IR spectroscopy (3300-3500 cm⁻¹) is observed. -1 The intensity weakens or disappears, and a CN stretching peak appears (1030-1230 cm⁻¹).-1 ); The linkage sites of the alkylating reagents are determined by two-dimensional NMR, and the sites to be prepared for alkylation are listed and denoted as set S. Based on the reaction design, the desired binding sites are identified and denoted as the target site set T; Align the identified binding site set S with the target site set T: Sites belonging to target site T are target sites for binding to alkylating agents; Sites that do not belong to the target site T are non-target sites that bind to the alkylating agent, i.e., side reaction sites; The initial total number of target sites is calculated based on the initial substrate feed. For example, if 1 mmol of phenol contains 1 mmol of phenolic hydroxyl target sites, the initial total number of target sites is 1 mmol. The number of target sites bound to the alkylating agent is determined by NMR integration, i.e., the total amount of target sites alkylated. For example, if 0.7 mmol of phenol's phenolic hydroxyl group is methylated, the number of target sites bound to the alkylating agent is 0.7 mmol. The formula for calculating the proportion of target sites that bind to the alkylating agent to the total number of target sites in a single reaction is as follows: ; Identify target products and byproducts by matching product structures with binding sites: Target product: The product generated solely by the binding of the alkylating agent to the target site, meaning that only the target site in the molecule reacts and no other sites are alkylated. Structural characteristics: The molecular skeleton is consistent with the substrate, with only the target site containing the alkylating group, while non-target sites remain in their initial state. Byproducts: Products generated by binding alkylating agents to non-target sites, such as the methylation of the ortho-hydroxyl group of phenol to form o-methyl anisole, multi-substituted products in which alkylating agents are bound to both target and non-target sites, and non-alkylated products generated by other byproducts. The amounts of the target product and each by-reaction product were determined by external standard HPLC / GC. The proportion of by-products in a single reaction is calculated to obtain the by-product ratio. The formula is as follows: ; Stability analysis was performed on the proportion of by-products and the binding rate to the target site in multiple reactions: Calculate the coefficient of variation of the proportion of by-products and the binding rate of the target site for each reaction, and compare them with the preset coefficient of variation. If the coefficient of variation is less than the preset coefficient of variation, the proportion of by-products or the binding rate of the target site is stable in multiple reactions; otherwise, it is unstable. For the proportion of by-products or the binding rate of target sites, if it is stable, the average value of multiple reactions is calculated as the proportion of by-products and the binding rate of target sites in the reaction system; otherwise, it is an outlier. Finally, the proportion of by-products and the binding rate of the target site in the reaction system are obtained, which are the predicted values ​​of the proportion of by-products and the binding rate of the target site in the current reaction. The site selectivity judgment index is obtained by subtracting the predicted proportion of side reaction products from the target site binding rate. The site selectivity judgment index is compared with the preset judgment index. If the site selectivity judgment index is greater than the threshold, it is predicted that the current reaction will have low site selectivity. Understandably, the physical meaning of the site selectivity index is: a quantitative characterization of the tendency of different potential reaction sites in a chemical reaction to be preferentially attacked. Essentially, it reflects the inherent differences in reactivity, steric hindrance, or other factors affecting reaction selectivity among different sites in a molecule. The magnitude of this difference is quantified numerically. The larger the index, the more significant the preferential reaction trend of a certain site compared to other sites, and the higher the site selectivity of the reaction. The smaller the index, the smaller the difference in reaction tendency among different sites, the lower the selectivity, and the possibility of multiple sites reacting simultaneously. It provides an intuitive quantitative basis for predicting the major product sites of a reaction and assessing the regioselectivity of a reaction. The effect on whether the current reaction will exhibit low site selectivity is as follows: Function 1: Predict selectivity issues before or in the early stages of a reaction, thus avoiding waste of subsequent experimental resources (reagents, time, manpower); Function 2: By using NMR, HPLC / GC and other methods, selective characteristics are transformed into specific numerical values ​​(such as the proportion of by-products, the binding rate of target sites, and the judgment index), eliminating subjective experience-based judgment and providing objective basis for subsequent analysis; Function 3: By analyzing the coefficient of variation, random errors can be eliminated, ensuring the reliability of the prediction results and avoiding misjudgments caused by fluctuations in a single reaction. Step 2: If so, through quantum computing simulations of the target site and the side reaction sites, and reaction transformation analysis of the model compounds generated by the target site and the side reaction sites, determine whether the low site selectivity is due to the slight activity difference between the target site and the side reaction sites. In step two, the process of determining whether the low site selectivity is due to minor activity differences between the target site and the side-reactive site includes: Based on response design, all pre-defined desired binding sites are categorized into a target site set T, and further subdivided according to structural characteristics, such as: T1, T2, ..., T nThe differences may exist due to the different positions of substituents and the different types of functional groups. For example, the para-hydroxyl group T1 and the ortho-hydroxyl group T2 of phenol are both target sites. All non-target binding sites are grouped into a set of secondary reactive sites S, and further subdivided according to structural characteristics, for example: S1, S2, ..., S m For example, the non-hydroxyl sites at C1 position S1 and C3 position S2 of the benzene ring in phenol; For each target site (T) i ) and secondary sites (S j ), where T i S is the i-th target site in the target site set T. j For the j-th side reaction site in the set of side reaction sites, record the following: Functional group type (e.g., hydroxyl, amino, CH bond); Molecular environment (electronic effects of adjacent substituents: electron withdrawal / electron donation; steric effects: steric hindrance). The relative positions between sites (whether there are interactions, such as conjugation effects or activity correlations caused by hydrogen bonds); For each T i and S j Calculate the electronic activity parameters for each site, including: the HOMO level, NBO negative charge density, and nucleophilic index of the nucleophilic site, or the LUMO level and electrophilic index of the electrophilic site. Distribution of electronic activity parameters of the target site set T: Calculation of mean ( ), standard deviation ( ), and record the maximum and minimum values ​​(reflecting the differences in activity within the site); Distribution of electronic activity parameters of the set of subsites S: Calculation of mean ( ), standard deviation ( ); Calculate the overlap of the confidence intervals of the same electronic activity parameters between the target site set T and the secondary site set S, and compare it with the preset overlap. If the overlap is greater than the preset overlap, the distribution overlap of the two sets of electronic activity parameters is high. If the difference between the two group means ( If the error range is less than or equal to the theoretical error range, and the distributions of the two sets of parameters have a high degree of overlap, then the overall difference in electronic activity is considered to be small. For each T i and S j Calculate the spatial steric hindrance parameters for each site, including steric hindrance energy and Taft steric parameters. ; Statistical mean values ​​of spatial steric hindrance parameters T and S The distribution range was then compared with a threshold. Calculate the overlap of the confidence intervals with the same spatial steric hindrance parameters between the target site set T and the secondary site set S, and compare it with the preset overlap. If the overlap is greater than the preset overlap, the distribution overlap of the two sets of spatial steric hindrance parameters is high. like If the spatial steric hindrance parameter distribution overlaps significantly, then the spatial steric hindrance has a weak effect on distinguishing between the two sets of sites. Design comparative experiments to synthesize model compound A containing only the target site and model compound B containing only the side reaction sites, ensuring that the molecular skeleton and reaction environment (such as substituents and ring structures) of both are consistent with the original substrate except for the target / side reaction sites; Under the same reaction conditions (solvent, temperature, catalyst, alkylating agent concentration, etc.), model compound A containing only the target site and model compound B containing only the side reaction sites were reacted with the alkylating agent, respectively, and the reaction rate constants k(A) and k(B) were calculated by real-time monitoring. After calculating the ratio of k(A) and k(B), the difference is taken from 1. The difference is compared with the preset difference. If the difference is less than the preset difference, it means that the reactivity difference between the target site and the side reaction site is small and they are easily attacked at the same time. In the reaction, the activity of the site is reflected in its reactivity with the alkylating agent. If the activity difference between the target site and the side reaction site is small, it indicates that the reaction rates of the target site and the side reaction site with the alkylating agent are similar, and the proportion of the target product and the side reaction product in the product is similar. Equal amounts of model compounds A and B were mixed and reacted with a limited amount of alkylating agent to ensure that only a portion of the sites were reacted. After the reaction reached equilibrium, the conversion rates of A and B were determined by HPLC / GC. The conversion ratio of A to B is calculated by subtracting 1 from the value of A, and the difference is compared with a preset difference. If the difference is less than the preset difference, it indicates that the two have a small difference in their tendency to be attacked in the competitive reaction, that is, a small difference in their activity. The criterion for determining that low site selectivity is caused by a small difference in activity between the target site set and the parasite set is: The mean difference of electronic activity parameters between the target site set T and the secondary site set S is less than or equal to the theoretical error, the mean difference of spatial steric hindrance parameters is less than or equal to the threshold, and the distributions of electronic activity parameters and spatial steric hindrance parameters have a high degree of overlap. The differences between the individual reaction rate constant ratios and the conversion ratios in competing reactions of the model compounds and 1 are both less than the preset differences; If the judgment criteria are met, the low site selectivity is due to the slight difference in activity between the target site and the side reaction site. To determine whether the low site selectivity is due to minor differences in activity between the target site and the side-reacting site, the following steps are taken: Function 1: To distinguish whether the low selectivity is caused by small differences in the activity of the sites themselves or by other external factors (such as impurity interference, reaction condition fluctuations), and to avoid blindly adjusting reaction parameters; Function 2: To reveal the root causes of selectivity problems from the essential aspects such as electronic effects and steric effects through quantum chemical calculations and comparative experiments, providing a theoretical basis for subsequent regulation; Function 3: The model compound experiment controls variables such as molecular skeletons and reaction environments, ensuring the specificity of the analysis of activity differences and improving the accuracy of cause judgment; Step 3: If so, perform a correlation analysis between the site activity regulation parameters and site selectivity to determine the critical values of the site activity regulation parameters; In Step 3, the process of determining the critical values of the site activity regulation parameters includes: For any site activity regulation parameter, its value range needs to cover the interval where site selectivity mutations may occur. Collect the corresponding Y observation values of parameter X within the full range to form a basic data set (X1, Y1), (X2, Y2)...(X n ,Y n ); Assume there is a critical value x0. Divide the data set into two segments according to x0: Low parameter segment: All samples with X < x0 (X1, Y1)...(X k ,Y k ), corresponding to the value range of X on the left side of x0; High parameter segment: All samples with X > x0 (X k+1 ,Y k+1 )...(X n ,Y n ), corresponding to the value range of X on the right side of x0; For the low parameter segment data, high parameter segment data, and full parameter segment data, construct fitting models corresponding to the trends respectively; If it is linear, use linear fitting, Calculate the sum of squared residuals RSS1 and RSS2 of the low parameter segment model and the high parameter segment model respectively. Then the total sum of squared residuals RSS of the segmented model 1+2 = RSS1 + RSS2. The residual is the sum of the squared deviations between the observed value Y and the model predicted value; The total sum of squared residuals of the overall model is RSS 总 ; Use the F - test to judge whether the segmented model is better than the overall model, that is, whether x0 is the true critical point: Null hypothesis H0: There is no significant difference between the segmented fitting and the overall fitting, that is, there is no critical value; Alternative hypothesis H1: The segmented fitting is significantly better than the overall fitting, that is, there is a critical value; Formula for calculating F-statistic: Where k is the number of parameters added after segmentation, n is the total sample size, and m is the total number of parameters for segmented fitting; For all candidate x0 within the preset range, repeat the parameter segmentation and model difference analysis process, and calculate the piecewise model goodness of fit R for each candidate x0. 2 And the results of the F-test; Prioritize selecting candidate x0s that pass the F-test. Among the x0s that meet the significance criteria, select two segments from the model's total R-value. 2 The highest point is the final critical value; For example, suppose the research objective is to determine a critical temperature (x) at which the proportion of by-products (y) increases abruptly from a low proportion; Obtain sample data covering the parameter range, including independent variables and response values. The data points must be evenly distributed and have repetition. Example data: |Temperature x|20|30|40|50|60|70|80|Repeat each temperature three times and take the average value|; |Proportion of by-reaction products y|5|6|7|22|35|48|60|; Draw a scatter plot to make an initial observation of the trend; Based on data trends and reaction mechanisms, preset the range of candidate critical values. For example, y increases slowly in the low temperature range and increases rapidly in the high temperature range. It is estimated that the critical value is between 40 and 50℃. Candidate points can be set as 42℃, 44℃, 46℃, 48℃, and 50℃. For each candidate critical value x0, the data is divided into two segments and fitted separately. Taking candidate x0=45℃ as an example: Low parameter range (x < 45℃): Data points are (20, 5), (30, 6), (40, 7). Fitted linear model: y1 = a1x + b1. Calculated result: y1 = 0.1x + 3, R0 2 =0.99, indicating a high goodness of fit; High parameter range (x>45℃): Data points are (50,22), (60,35), (70,48), (80,60). The fitted linear model is: y²=a²x+b², yielding y²=1.3x-43, R². 2 =0.98, indicating a high goodness of fit; Calculate the sum of squared residuals, and the lower segment residuals: , high-level residual: RSS 分段 =RSS1+RSS2=0.05; The overall fit RSS=300, indicating a large deviation due to the significant difference in the trends between the two segments. Perform an F-test and calculate the F-statistic: Consult the F-distribution table (degrees of freedom k=2, nm=3), the critical value F 0.05 (2,3)=9.55, the calculated F=8980>>9.55, and p<0.05, so we reject H0 and conclude that there is a significant difference in the fit between the two segments at x0=45℃; For all candidate x0 (42℃, 44℃, 46℃, 48℃, 50℃) within the preset range, the differences between the segmented and fitted models were verified, and the F-value and R-value of each x0 and the fitted values ​​of the two segments were recorded. 2 p-value, filtering results: |Candidate x0|F value|Low segment R 2 |High segment R 2 |p value|; |42|5000|0.95|0.95|<0.05|; |45|8980|0.95|0.98|<0.05|; |48|6000|0.95|0.97|<0.05|; Optimal critical value: x0 = 45℃, F value is maximum, R 2 The highest value was achieved, and p < 0.05. The purpose of determining the critical values ​​of site activity regulation parameters is: Function 1: Determining the critical value provides a threshold reference for parameter control, avoiding blind parameter adjustment (e.g., knowing that the temperature needs to be higher / lower than a certain value to significantly improve selectivity). Function 2: By using F-test and goodness-of-fit analysis, we can ensure that the critical value is not the result of random data fluctuations, but a real mutation point based on statistical significance, thus ensuring the reliability of subsequent regulation. Function 3: The critical value is tied to the specific reaction system (based on historical data of the same system), avoiding the inapplicability of general empirical parameters and improving the accuracy of control; Step 4: Integrate the site activity regulation parameters, target site binding analysis results, and byproduct ratios to conduct correlation analysis, assess the influence of site activity regulation parameters on site selectivity, and determine the regulatory priority of site activity regulation parameters by combining the differences between site activity regulation parameters and critical values. In step four, the site activity regulation parameters include: Electronic activity parameters: site charge density, frontier orbital energy level, bond dissociation energy, etc. (reflecting the effect of electron cloud density on electrophilic / nucleophilic reactions); Spatial parameters: steric volume of the site (such as molecular surface electrostatic potential, Taft steric parameters, bond length / bond angle of adjacent groups (reflecting the hindrance of steric hindrance to the reaction); Environmental parameters: Reaction condition-related parameters, such as catalyst acidity, solvent polarity, and temperature (which affect the difference in activation energy); The process of performing correlation analysis on the integrated site activity regulation parameters, target site binding analysis results, and by-product ratios includes: Low site selectivity refers to a low selectivity caused by insufficient difference in reactivity between the target site and the side reaction site. Calculate the selectivity factor. , where r 目标 r represents the reaction rate at the target site. 副 The reaction rate at the side reaction site; Select reaction parameters related to site activity regulation, including but not limited to catalyst acidity, solvent polarity, and temperature, for example: Catalyst acidity: expressed as acid strength, denoted as x1; Solvent polarity: expressed by dielectric constant or solvent polarity parameter, denoted as x2; Temperature: Represented by absolute temperature (T, K), denoted as x3; The site selectivity factor S was measured under different parameter values ​​using a univariate control method, resulting in the following dataset: For parameter x i Based on the initial value x0, set the change amount. ,For example: The corresponding measurement selectivity factor S i1 ,S i2 ...; For any given site activity regulation parameter, while keeping other parameters constant, calculate the rate of change in the selectivity factor caused by fluctuations in the site activity regulation parameter to obtain the sensitivity coefficient. ,in, Indicates parameter x i The change in k, where k is the experimental group number. , representing the change in the selectivity factor, x i0 S0 and S0 are the initial values ​​of the parameter and the selectivity factor, respectively; It is understandable that the sensitivity coefficient represents the parameter x. i When the relative change is 1%, the percentage change in site selectivity S is represented by the larger the absolute value, indicating a more significant impact of the parameter on selectivity. i If negative, parameter x i As the sensitivity coefficient S increases, the sensitivity coefficient S decreases, meaning that increasing the parameter exacerbates low selectivity, and vice versa. i If positive, parameter x i When the parameter increases, the Xunze new factor S increases, meaning that increasing the parameter will alleviate the low selectivity. The risk index R is obtained by calculating the distance between each site's activity regulation parameter and the critical value. For any site activity regulation parameter, the current value of the parameter is x, and the critical value is x0, then: ; For any site activity regulation parameter, the absolute value of the sensitivity coefficient is added to the risk index to obtain the priority index. The site activity regulation parameters are then arranged in descending order of the priority index to obtain the regulation priority of the site activity regulation parameter. Understandably, the physical meaning of the priority index is: to simultaneously consider the strength of the parameter's influence on selectivity (sensitivity coefficient) and the urgency of the current state's loss of control (risk index), ensuring that parameters with a large impact and high risk are prioritized for regulation; The purpose of determining the priority of regulation is: Function 1: By prioritizing parameters that have a significant impact on selectivity and whose current values ​​are close to the critical value, selectivity can be improved quickly with minimal experimental cost (such as fewer parameter adjustments). Function 2: Avoid wasting energy on parameters with negligible or far-from-critical effects, and concentrate resources on solving key problems; Function 3: The sensitivity coefficient and risk index can be updated as the reaction conditions change, ensuring that the control strategy always matches the current reaction state and improving the timeliness of selective improvement; Step 5: Adjust the site activity regulation parameters according to the regulation priority, and adjust the site activity regulation parameters to within the critical value; In step five, the process of regulating the site activity regulation parameters according to the regulation priority includes: For any site activity regulation parameter, compare the current value of the parameter with the critical value to determine whether regulation is needed: If the current value is already within the critical range, no adjustment is needed, and the next priority parameter can be selected.

[0020] If the current value exceeds the critical range, regulation needs to be initiated; Determined by the direction of deviation: if the critical value is the upper limit, the current value exceeds the limit and needs to be reduced; if the critical value is the lower limit, the current value is insufficient and needs to be increased. Amplitude: The minimum target is to just enter the critical value range. For example, if the critical temperature value is ≤50℃ and the current temperature is 55℃, it needs to be reduced by at least 5℃ to adjust to 50℃ or below. Principle: Prioritize adjustments with minimal amplitude to avoid excessive control that could cause drastic disturbances to the reaction system and potentially affect other parameters; After adjusting the site activity regulation parameter according to the determined direction and amplitude, stabilizing the reaction system, and then re-measuring the measured value of the parameter; Once the measured value is confirmed to be within the critical range, the parameter adjustment is complete. If it does not meet the standard, repeat the adjustment until it does. The effect of adjusting the site activity regulation parameters to within the critical value according to the regulatory priority is: Function 1: By strictly controlling the parameters within the critical value, abnormal site activity caused by excessive parameters (such as excessively high temperature) or insufficient parameters (such as excessively low concentration) can be avoided, ensuring that the reaction proceeds according to the expected mechanism; Function 2: By adopting the principle of "minimum amplitude adjustment", the drastic disturbance of the reaction system during the control process can be reduced, and fluctuations in other parameters caused by over-control can be avoided, thereby maintaining the overall stability of the system and reducing the risk of chain anomalies.

[0021] The technical solution and advantages of this application are as follows: By analyzing the structure of historical reaction products in multiple isoalkylation reaction systems, the sites and historical reaction products in each alkylation reaction are divided. The site division includes target sites and side reaction sites, and the historical reaction product division includes target products and side reaction products. Based on the binding rate of the target site and the generation ratio of the side reaction products, it is predicted whether the current alkylation reaction will exhibit low site selectivity. If so, through quantum computing simulation of the target site and side reaction sites and reaction transformation analysis of the model compounds generated by the target site and side reaction sites, it is determined whether the low site selectivity is due to the slight activity difference between the target site and the side reaction sites. If so, a correlation analysis is performed on the site activity regulation parameters and site selectivity to determine the critical value of each site activity regulation parameter. The correlation analysis is performed on the site activity regulation parameters, the target site binding analysis results, and the proportion of side reaction products to evaluate the degree of influence of the site activity regulation parameters on site selectivity. Based on the difference between the site activity regulation parameters and the critical value, the regulation priority of the site activity regulation parameters is determined. The site activity regulation parameters are regulated according to the regulation priority to bring the site activity regulation parameters within the critical value. This invention analyzes the structure of products from multiple historical alkylation reactions in the same reaction system, identifies target sites, side reaction sites, and corresponding products, calculates the binding rate of target sites and the proportion of side reaction products, and predicts the risk of low site selectivity through stability analysis. If a risk exists, quantum chemical calculations and model compound experiments are used to determine whether it is caused by the small difference in activity between target sites and side reaction sites, thereby determining the critical value of site activity regulation parameters. Correlation analysis is used to clarify the regulation priority, and finally, the parameters are adjusted to within the critical value according to the priority. This achieves precise prediction and regulation of the alkylation process, significantly improves the site selectivity of intermediate synthesis, reduces the generation of by-products, and reduces the waste of experimental resources.

[0022] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the alkyl process in the synthesis of intermediates, characterized in that: include: By analyzing the structure of historical reaction products in multiple alkylation reaction systems, the sites in each alkylation reaction and the historical reaction products are classified. The site classification includes target sites and side reaction sites, and the historical reaction product classification includes target products and side reaction products. Based on the binding rate of target sites and the generation ratio of side reaction products, it is predicted whether the current alkylation reaction will exhibit low site selectivity. If so, through quantum computing simulations of the target site and the side reaction sites, as well as reaction transformation analysis of the model compounds generated by the target site and the side reaction sites, it can be determined whether the low site selectivity is due to the slight activity difference between the target site and the side reaction sites. If so, perform correlation analysis on the site activity regulation parameters and site selectivity to determine the critical values ​​of each site activity regulation parameter; Correlation analysis was performed on site activity regulation parameters, target site binding analysis results, and byproduct ratios to assess the influence of site activity regulation parameters on site selectivity. The regulatory priority of site activity regulation parameters was determined by combining the differences between site activity regulation parameters and critical values. The site activity regulation parameters are adjusted according to the regulation priority, and the site activity regulation parameters are adjusted to within the critical value.

2. The method for controlling the alkyl process in the synthesis of intermediates according to claim 1, characterized in that: The method for defining the site and historical reaction products is as follows: The linkage sites of the alkylating reagents are determined by two-dimensional NMR, and all sites to be alkylated are listed and denoted as set S. Based on the reaction design, the desired binding sites are identified and denoted as the target site set T; The identified binding site set S is compared with the target site set T. Sites belonging to the target site T are target sites that bind to the alkylating agent, while sites that do not belong to the target site T are non-target sites that bind to the alkylating agent, i.e., side reaction sites. The target product is the product generated solely by the binding of the alkylating agent to the target site, while the by-reaction product is the product generated by the binding of the alkylating agent to the by-reaction site.

3. The method for controlling the alkyl process in the synthesis of intermediates according to claim 1, characterized in that: The calculation process for the target site binding rate and the proportion of by-products is as follows: The formula for calculating the proportion of target sites that bind to the alkylating agent to the total number of target sites in a single reaction is as follows: ; The proportion of by-products in a single reaction is calculated to obtain the by-product ratio. The formula is as follows: .

4. The method for controlling the alkyl process in the synthesis of intermediates according to claim 1, characterized in that: The method for predicting whether the current reaction will exhibit low site selectivity is as follows: Calculate the coefficients of variation for the target site binding rate and the proportion of by-products in multiple alkylation reactions, and compare them with the preset coefficients of variation. If the coefficient of variation is less than the preset coefficient of variation, calculate the mean value of the target site binding rate and the proportion of by-products for each alkylation reaction, which is the predicted value of the proportion of by-products and the target site binding rate for the current reaction. The site selectivity judgment index is obtained by subtracting the predicted proportion of by-products from the target site binding rate, and then compared with the preset judgment index. If the site selectivity judgment index is greater than the threshold, it is predicted that the current response will exhibit low site selectivity.

5. The method for controlling the alkyl process in the synthesis of intermediates according to claim 1, characterized in that: The quantum computing simulation process for the target site and the side reaction site is as follows: Based on the reaction design, all the pre-set desired binding sites are classified into the target site set T, and all non-target binding sites are classified into the side reaction site set S. Obtain the electronic activity parameters and steric hindrance parameters of all sites in the target site set and the side reaction site set; For electronic activity parameters and steric hindrance parameters, calculate the overlap of the same confidence intervals for the target site set T and the secondary site set S, and compare it with the preset overlap. If the overlap is greater than the preset overlap, the electronic activity parameters and spatial steric hindrance parameters of the target site set T and the subsite set S have a high degree of overlap. The mean values ​​of electronic activity parameters and steric hindrance parameters of the target site set and the side reaction site set are calculated separately, and then compared with the threshold after difference calculation.

6. The method for controlling the alkyl process in the synthesis of intermediates according to claim 5, characterized in that: The reaction transformation analysis of the model compounds generated from the target site and the side reaction site is conducted as follows: Synthesize model compound A containing only the target site and model compound B containing only the side reaction sites; Under the same reaction conditions, model compound A containing only the target site and model compound B containing only the side reaction site were reacted with alkylating agents, and the reaction rate constants k(A) and k(B) were calculated by real-time monitoring. Equal amounts of model compounds A and B were mixed and reacted with an alkylating agent. After the reaction reached equilibrium, the conversion rates of A and B were measured. For model compounds A and B, the ratio of reaction rate constant k(A) to k(B) and the conversion ratio are subtracted from 1, and the differences are compared with preset differences.

7. The method for controlling the alkyl process in the synthesis of intermediates according to claim 5, characterized in that: The method for determining whether the low site selectivity is due to a slight difference in activity between the target site and the side reaction site is as follows: The criterion for determining that low site selectivity is caused by a small difference in activity between the target site set and the parasite set is: The mean difference of electronic activity parameters between the target site set T and the secondary site set S is less than or equal to the theoretical error, the mean difference of spatial steric hindrance parameters is less than or equal to the threshold, and the distributions of electronic activity parameters and spatial steric hindrance parameters have a high degree of overlap. The differences between the individual reaction rate constant ratios and the conversion ratios in competing reactions of the model compounds and 1 are both less than the preset differences; If the criteria are met, the low site selectivity is caused by the slight difference in activity between the target site and the side reaction site.

8. The method for controlling the alkylation process of intermediate synthesis according to claim 1, characterized in that: The critical values ​​of the activity regulation parameters at each bit point are determined as follows: For any site activity regulation parameter, collect the corresponding Y observation values ​​of parameter X across the entire range to form a basic dataset; Assuming there exists a critical value x0, the dataset is divided into a low-parameter segment and a high-parameter segment based on x0; For low-parameter range data, high-parameter range data, and full-parameter range data, respectively construct fitting models for the corresponding trends; Calculate the residual sums of squares RSS1 and RSS2 for the low-parameter segment model and the high-parameter segment model respectively, then the total residual sum of squares RSS of the piecewise model is obtained. 1+2 =RSS1+RSS2; The sum of squared residuals of the overall model is RSS 总 ; Perform an F-test on the piecewise model and the overall model to determine whether the piecewise model is superior to the overall model; For all candidate x0 within the preset range, repeat the parameter segmentation and model difference analysis process, and calculate the piecewise model goodness of fit R for each candidate x0. 2 And the results of the F-test; Among the candidate x0s that pass the F-test, select the piecewise model total R. 2 The highest point is the final critical value.

9. The method for controlling the alkyl process in the synthesis of intermediates according to claim 1, characterized in that: The method for determining the regulation priority of each point activity regulation parameter is as follows: Calculate the selectivity factor. , where r 目标 r represents the reaction rate at the target site. 副 The reaction rate at the side reaction site; For parameter x i Based on the initial value x0, set the change amount. ; For any site activity regulation parameter; With other parameters fixed, calculate the fluctuations in site activity regulation parameters. The sensitivity coefficient is obtained by measuring the rate of change of the selectivity factor caused by time. ,in, Indicates parameter x i The change in k, where k is the experimental group number. , representing the change in the selectivity factor, x i0 S0 and S0 are the initial values ​​of the parameter and the selectivity factor, respectively; Calculate the distance between the current activity regulation parameter at each site and the corresponding critical value to obtain the risk index R. For any activity regulation parameter at a site, if the current value of the parameter is x and the critical value is x0, then: ； The absolute value of the sensitivity coefficient is added to the risk index to obtain the priority index. The site activity regulation parameters are then arranged in descending order of the priority index to obtain the regulation priority of the site activity regulation parameters.

10. The method for controlling the alkyl process in the synthesis of intermediates according to claim 1, characterized in that: The process of regulating site activity parameters according to regulatory priority is as follows: For any site activity regulation parameter, compare the current value of the parameter with the critical value; If the current value exceeds the critical range, regulation needs to be initiated; The direction of regulation is determined by the direction of deviation. If the critical value is the upper limit, the current value should be reduced if it exceeds the standard. If the critical value is the lower limit, the current value should be increased if it is insufficient. With the minimum objective of just entering the critical value range, the smallest adjustment should be prioritized. After adjusting the site activity regulation parameter according to the determined direction and amplitude, stabilizing the reaction system, and then re-measuring the measured value of the parameter; Once the measured value is confirmed to be within the critical range, the parameter adjustment is complete. If it does not meet the standard, repeat the adjustment until it does.

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