Optimization method of glycerol refining process

Abstract

The invention discloses an optimization method of a glycerol refining process, which comprises the following steps of: determining control factors according to an adopted glycerol refining process, and respectively carrying out single-factor tests by taking the control factors as variables to obtain an optimized first control factor, the control factors are at least selected from any one of the following factors: the ratio of silica gel to zeolite powder, the addition amount of the decolorizing agent and the decolorizing temperature; then carrying out orthogonal test design; setting an evaluation subset and a comment set; setting a weight set; determining a fuzzy relation comprehensive evaluation set; establishing and analyzing a secondary response surface regression model; and analyzing the fitted equation by using software to obtain the optimal process parameters of glycerol refining, and checking by using a fuzzy mathematics sensory evaluation model to determine the optimal process parameters of glycerol refining. The optimal refining process can be obtained by investigating the influence of the ratio of the silica gel to the zeolite powder, the addition amount of the decolorizing agent and the decolorizing temperature on the glycerol content by combining fuzzy mathematical evaluation with a response surface method.

Description

Technical Field

[0001] The invention belongs to the technical field of glycerol refining, and particularly relates to a method for optimizing a glycerol refining process. Background Art

[0002] High concentration of glycerin is a non-toxic and safe substance, which is widely used in the fields of cosmetics and food, and its application scope is still being extended and expanded. In the food processing industry, glycerin is usually used as a preservative, sweetener and moisturizer in sports food and milk substitutes, playing a role of lubrication and nutrition, and giving a refreshing feeling.

[0003] There are many purification processes for glycerol. The commonly used purification and refining process is vacuum distillation. However, the vacuum distillation method requires high distillation temperature and high energy consumption. High temperature can easily cause glycerol to decompose or polymerize to generate new products. The quality of the obtained glycerol cannot be guaranteed. In addition, there are defects such as large workload and inaccuracy in process optimization.

[0004] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0005] The object of the present invention is to provide a method for optimizing a glycerol refining process, which can obtain an optimal glycerol refining process by combining fuzzy mathematical evaluation and response surface methodology.

[0006] In order to achieve the above object, a technical solution provided by a specific embodiment of the present invention is as follows:

[0007] A method for optimizing a glycerol refining process comprises the following steps:

[0008] According to the adopted glycerol refining process, the control factors are determined, and single factor experiments are carried out respectively with the control factors as variables to obtain the optimized first control factors, wherein the control factors are at least selected from any one of the following: the ratio of silica gel and zeolite powder, the amount of decolorizing agent added, and the decolorizing temperature;

[0009] The glycerol refining process comprises:

[0010] S1. Mix zeolite powder and silica gel in a certain proportion to prepare an adsorbent;

[0011] S2, adsorbing the crude glycerol through an adsorbent to obtain a primary product;

[0012] S3, passing the primary product through an ion exchange resin column to obtain a semi-finished product;

[0013] S4, adding a decolorizing agent to the semi-finished product for decolorization, and then filtering and drying to obtain a finished product.

[0014] In one or more embodiments of the present invention, it also includes: performing a multi-factor and multi-level response surface test design based on the first control factor to obtain an orthogonal test result, and thereby obtaining an optimized second control factor.

[0015] In one or more embodiments of the present invention, the orthogonal test result is evaluated according to the second control factor to obtain the optimal third control factor, and the evaluation process includes:

[0016] Set up evaluation subsets and comment sets: take the color, smell, transparency and viscosity of glycerin as evaluation indicators, and the evaluation indicators form the evaluation subset U, U = (U1, U2, U3, U4) = (color, smell, transparency, viscosity); evaluate the grade of refined glycerin, and the comment set of each evaluation indicator is represented by V, V = (poor, good, excellent);

[0017] Set weight set: assign weights to the evaluation subsets and obtain the weight set K;

[0018] Determine the fuzzy relationship comprehensive evaluation set: evaluate each evaluation index of refined glycerin, divide the number of votes for each index in each comment by the total number of evaluations, and obtain the fuzzy evaluation matrix a; use fuzzy mathematics methods to process the comprehensive evaluation results of glycerin, and according to the fuzzy matrix transformation principle, multiply the weight set K and the fuzzy evaluation matrix a to obtain the evaluation result y of glycerin; set the score according to the sensory quality grade to obtain the grade matrix R, and the product of the grade matrix R and the evaluation result y is the fuzzy comprehensive score T;

[0019] Establishment and analysis of quadratic response surface regression model: Taking the ratio of silica gel to zeolite powder A, the amount of decolorizing agent added B, and the decolorizing temperature C as response surface factors, and the glycerol content Y as the response value, the software was used to perform multivariate regression fitting on the response results to obtain the quadratic response surface equation, and variance and significance analysis were performed;

[0020] The optimal process parameters for glycerin refining were obtained by software analysis and fitting equations, and the fuzzy mathematical sensory evaluation model was used for testing to determine the optimal process parameters for glycerin refining and obtain the optimized glycerin refining process.

[0021] In one or more embodiments of the present invention, the sensory quality is set to be excellent, good, and poor as 100 points, 90 points, and 80 points, respectively, and the grade matrix R=(100, 90, 80).

[0022] In one or more embodiments of the present invention, the weight set K=(color, smell, transparency, viscosity)=(0.35, 0.30, 0.25, 0.10).

[0023] In one or more embodiments of the present invention, the quadratic response surface equation is Y(%)=99.27+1.56A+0.87B+0.33C+0.84AB+1.29AC-1.35BC-11.06A 2 -6.22B 2 -3.6C 2 .

[0024] In one or more embodiments of the present invention, in step S1, the mass ratio of silica gel to zeolite powder is (0.5-2.5):1.

[0025] In one or more embodiments of the present invention, in step S4, the decolorizing agent includes rice husk charcoal, quartz sand and activated carbon.

[0026] In one or more embodiments of the present invention, in step S4, the amount of decolorizing agent added is 0.5-2.5% of the mass of the semi-finished product.

[0027] In one or more embodiments of the present invention, in step S4, the decolorization temperature is 60-100°C.

[0028] Compared with the prior art, the present invention uses crude glycerol as the main raw material, selects the adsorbent ratio, the amount of decolorant added, and the decolorization temperature as independent variables based on a single factor test, and uses fuzzy mathematics combined with a response surface method to investigate the influence on the glycerol content, and obtains the optimal glycerol refining parameters as follows: the ratio of adsorbent silica gel to zeolite is 1.5:1, the amount of decolorant added is 1.0%, and the decolorization temperature is 80°C. Under these conditions, the model mathematically predicts the glycerol content value to be 99.36 points, and after repeated tests for many times, the glycerol content is obtained to be (99.55±0.07) points, which has no significant difference from the theoretical value, fully verifying the correctness of the regression model, and the response surface test design is reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 The comparison of the effects of different ratios of silica gel and zeolite on the glycerol content in the present invention;

[0031] Figure 2 The comparison of the effect of the amount of decolorizing agent added on the glycerol content in the present invention;

[0032] Figure 3The comparison of the effect of the decolorization temperature and the amount of addition on the glycerol content in the present invention;

[0033] Figure 4 The response surface of the effect of the ratio of silica gel to zeolite and the amount of decolorizing agent added on the glycerol content in the present invention;

[0034] Figure 5 It is the contour line of the effect of the ratio of silica gel to zeolite and the amount of decolorizing agent added on the glycerol content in the present invention;

[0035] Figure 6 is the response surface of the effect of the ratio of silica gel to zeolite and the decolorization temperature on the glycerol content in the present invention;

[0036] Figure 7 It is the contour line of the effect of the ratio of silica gel to zeolite and the decolorization temperature on the glycerol content in the present invention;

[0037] Figure 8 It is the response surface of the effect of the amount of decolorizing agent added and the decolorizing temperature on the glycerol content in the present invention;

[0038] Fig. 9 It is the contour line of the influence of the addition amount of decolorizing agent and the decolorizing temperature on the glycerol content in the present invention. DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0040] A method for optimizing a glycerol refining process comprises steps S1-S1074.

[0041] S1, preparation of adsorbent: crush the zeolite and soak it in a 10% hydrochloric acid solution for 2 hours, wash it with water until it is neutral, calcine it at 300℃ in a Ma Fuel, dry it and dehydrate it, crush it again and pass it through a 200 mesh sieve. Mix silica gel with a particle size of 74-154μm and activated zeolite in different proportions to prepare adsorbent for use.

[0042] S2, adsorption: different proportions of adsorbents were loaded into the column in sequence, crude glycerol was passed through the column to adsorb impurities in the crude glycerol, and the effluent was taken for use. The purity of the crude glycerol was 72%, which was purchased from Qingdao Jinniu Oil Technology Co., Ltd.

[0043] S3, secondary resin adsorption: dilute the effluent with 1 volume of pure water, pass it through D201 strong alkaline ion exchange resin at a flow rate of 2.2 ml / min; take the effluent from D201 strong alkaline ion exchange resin, pass it through 732 strong acid ion exchange resin at a flow rate of 1.5 ml / min, and take the effluent;

[0044] S4, decolorizing agent: wash the rice husk charcoal, dry it in the sun, crush it into powder, add water to stir, pass the slurry through an 18-mesh sieve to remove impurities and large particles of rice husk charcoal, dry it for later use, and obtain product A; take an appropriate amount of quartz sand and soak it in a 5% sulfuric acid solution for 2 hours to clean and activate it, and pass it through a 100-mesh sieve after drying to obtain product B; take activated carbon that has passed a 200-mesh sieve, mix products A and B with activated carbon in a mass ratio of 1:1:2 to obtain a decolorizing agent.

[0045] S5, decolorization: decolorize the effluent, change the decolorization temperature, the amount of decolorizer added, that is, the mass ratio of the decolorizer to the effluent, and take the decolorized glycerol effluent.

[0046] S6, drying: take the decolorized glycerol effluent and remove the glycerol water.

[0047] S7, Testing: The glycerin content is tested using conventional testing methods, and sensory testing of the glycerin's odor, color, and transparency is performed.

[0048] S8, single factor experiment:

[0049] S81, under the conditions of taking 100.0g crude glycerol raw material, bleaching temperature of 80℃, and adding 1.0% decolorant, the effects of the mass ratio of silica gel to zeolite on the color, filtration effect and glycerol content of the glycerol product were investigated when the mass ratio of silica gel to zeolite was 0.5:1, 1:1, 1.5:1, 2:1, and 2.5:1, respectively. The experimental results are as follows: Figure 1 As shown, Figure 1 The abscissa is the mass ratio of silica gel to zeolite, and the ordinate is the mean value of glycerol content (%);

[0050] Depend on Figure 1 It can be seen that different adsorbent ratios have different effects on the glycerol content. The glycerol content shows a trend of first increasing and then decreasing. When the mass ratio of silica gel to zeolite is 1.5:1, the color of the effluent is light yellow, the impurity adsorption effect is high, and the glycerol content also reaches the highest value. Therefore, the mass ratio of silica gel to zeolite is 1:1, 1.5:1, and 2:1 for subsequent response surface experiments.

[0051] S82, under the conditions of taking 100.0g of glycerol raw material after ion exchange, bleaching temperature of 80°C, and adsorbent ratio of 1.5:1, the effects of adding bleaching agent at 0.5%, 1.0%, 1.5%, 2.0%, and 2.5% on the color, filtration and glycerol content of the glycerol product were investigated. The experimental results are as follows: Figure 2 As shown;

[0052] Depend on Figure 2 It can be seen that different amounts of decolorizing agents have different effects on the glycerol content. The glycerol content shows a trend of first increasing and then decreasing. The reason is that the greater the amount of decolorizing agent added, the greater the glycerol loss. When 0.5% of the decolorizing agent is added, the product color is darker and the filtering effect is not good. Therefore, the decolorizing agent dosage is determined to be 1.0% based on its filtering effect. At this time, the product color is light yellow and the filtering effect is relatively easy. Therefore, the decolorizing agent addition levels of 0.5%, 1.0%, and 1.5% are selected for subsequent response surface experiments.

[0053] S83, under the conditions of taking 100.0g of glycerol raw material after ion exchange, adding 1.0% of decolorizing agent, and the ratio of adsorbent to 1.5:1, the effect of decolorizing agent at 60℃, 70℃, 80℃, 90℃, and 100℃ on the color, filtration and glycerol content of glycerol product was investigated. The experimental results are as follows Figure 3 As shown;

[0054] Depend on Figure 3 It can be seen that different bleaching temperatures have different effects on glycerol content. The glycerol content shows a trend of first increasing and then decreasing with the increase of temperature. The reason is that the appropriate temperature can improve the decolorization and adsorption effect of the decolorizer, and improve the removal rate and adsorption amount of the adsorbent. However, too high a temperature will cause sintering and carbonization, affecting its surface and pore structure, and degrading the adsorption performance of the adsorbent. When the bleaching temperature is 60℃ and 70℃, the filtration effect is relatively difficult. Therefore, the bleaching temperature is determined to be 80℃ in combination with the glycerol content and the filtration effect. At this time, the product color is colorless, the filtration effect is relatively easy and the glycerol content is also relatively high; therefore, the bleaching temperatures of 75℃, 80℃ and 85℃ were selected for subsequent response surface experiments.

[0055] S9, orthogonal experimental design: Based on the results of the single factor experiment, three factors, namely, adsorbent ratio (A), decolorant addition amount (B) and decolorization temperature (C), were selected to conduct a three-factor three-level response surface experimental design. The adsorbent ratio, decolorant addition amount and decolorization temperature were used as independent variables and glycerol content was used as the dependent variable. The response surface design was performed using Design-Expert 13 software to determine the optimal process parameters for glycerol content. The specific factor levels of the experimental design are shown in Table 1.

[0056] Table 1 Experimental design of response surface analysis factor levels

[0057]

[0058]

[0059] S10, establish a fuzzy mathematical evaluation model:

[0060] Ten healthy food professional evaluators aged between 25 and 45 years old were selected to form an evaluation team and received sensory evaluation training on glycerin-related oils. The color, smell, transparency and viscosity of glycerin were then evaluated, and the average value of the results was taken. The evaluation criteria are shown in Table 2.

[0061] Table 2 Sensory evaluation of glycerin

[0062]

[0063] S101, determination of evaluation objects: the evaluation object set is the set of glycerols to be evaluated X = (X1, X2, X3, ..., X17), Xi represents the total evaluation of glycerols, where i = 1, 2, 3, ..., 17;

[0064] S102, determining the evaluation factors: using the four sensory indicators of glycerin as factors, namely U=(U1, U2, U3, U4), where U1 represents the color of glycerin, U2 represents the smell of glycerin; U3 represents the transparency of glycerin; and U4 represents the viscosity of glycerin.

[0065] S103, determination of evaluation grade set: the evaluation team members evaluate the grade of the refined glycerin and determine the evaluation comment set as V = (poor v1, good v2, excellent v3) = (80 points, 90 points, 100 points).

[0066] S104, determination of evaluation weight:

[0067] The weight and influence of each factor in the evaluation factors are the weights. According to the importance of the proportion of each indicator in the evaluation, the higher the importance, the greater the proportion. K = (k1, k2, k3, k4) is used to represent the set of evaluation objects, and k1 + k2 + k3 + k4 = 1. In the present invention, 10 assessors are selected to score the importance of the four quality factors of refined glycerin, namely, color, smell, transparency and viscosity, with a total score of 10 points. Finally, the ratio of the sum of the scores of each factor to the total score of 100 points is the weight factor of each factor.

[0068] Table 3 shows the statistical results of the weights of the four quality factors of refined glycerin by 10 assessors. It can be seen from Table 3 that the sensory assessors have certain differences in the weight distribution of the four influencing factors, namely color, smell, transparency, and viscosity. The weight score of each influencing factor is divided by the total score of 100 to obtain the weight of each quality factor, namely color (0.35), smell (0.30), transparency (0.25), and viscosity (0.10); among them, the color and smell have larger weights, and the transparency and viscosity have smaller weights.

[0069] Table 3 Glycerol evaluation factor weight score table

[0070]

[0071]

[0072] S105, rewards and results of fuzzy matrix:

[0073] Ten evaluators were asked to evaluate 17 groups of refined glycerols designed in orthogonal experiments. As shown in Table 4, among the 10 people in the evaluation team, the number of votes for the excellent, good, and poor grades of sample No. 1 in the color evaluation results was 0, 1, and 9 respectively. The number of votes for each grade of different indicators of sample No. 1 was divided by the total number of evaluators, and the fuzzy evaluation matrix a1 of sample No. 1 was obtained.

[0074] Table 4 Glycerol scoring table

[0075]

[0076]

[0077] According to the results in Table 4, the data in the table are converted into a fuzzy mathematical matrix:

[0078]

[0079] S106, Calculation and analysis of fuzzy mathematical sensory scoring:

[0080] The product of the weight matrix K and the fuzzy relationship matrix a is the comprehensive evaluation of the sample;

[0081] Taking sample 1 as an example, the product of the weight matrix K and the fuzzy relationship matrix a1 is the comprehensive evaluation y1 of sample 1, that is, y1 = K·a1. Similarly, the total evaluation results of other samples can be obtained;

[0082]

[0083] S107, Box-Behnken test design and result analysis:

[0084] The sensory quality is set to be excellent, good, and poor as 100 points, 90 points, and 80 points respectively. The product of the grade matrix R = (100, 90, 80) and the evaluation result y is the fuzzy comprehensive score T;

[0085] Taking sample 1 as an example, T1=y1×R=(0, 0.065, 0.935)×(100, 90, 80)=80.65. The comprehensive scores of other samples can be obtained in the same way. See Table 5 for details.

[0086] Table 5 Box-Behnken response surface method experimental design results

[0087]

[0088]

[0089] S1071, Establishment and analysis of quadratic response surface regression model:

[0090] According to the above experiment, the adsorbent ratio (A), decolorant addition amount (B) and decolorization temperature (C) were used as response surface factors, and the glycerol content was the response value (Y). Origin-Expert 13 software was used to perform multivariate regression fitting on the response results to estimate the glycerol content; the terms that had no significant effect on the model were removed, and the following quadratic response surface equation was obtained after optimization:

[0091] Y(%)=99.27+1.56A+0.87B+0.33C+0.84AB+1.29AC-1.35BC-11.06A 2 -

[0092] 6.22B 2 -3.6C 2 .

[0093] S1072, Box-Behnken test ANOVA:

[0094] Table 6 Significance test and variance analysis of regression equation model

[0095] factor sum of squares Degrees of Freedom Mean Square F-number P-value Significance Model 841.70 9 93.52 105.95 <0.0001 ** A-Adsorbent ratio 19.53 1 19.53 22.13 0.0022 ** B-Decolorizing agent addition amount 6.04 1 6.04 6.84 0.0346 * C-bleaching temperature 0.8778 1 0.8778 0.9945 0.3519 / AB 2.81 1 2.81 3.18 0.1178 / AC 6.63 1 6.63 7.51 0.0289 * BC 7.29 1 7.29 8.26 0.0239 * <![CDATA[A 2 ]]> 515.05 1 515.05 583.52 <0.0001 ** <![CDATA[B 2 ]]> 163.03 1 163.03 184.70 <0.0001 ** <![CDATA[C 2 ]]> 54.49 1 54.49 61.74 0.0001 ** Residual 6.18 7 0.8827 / / / Lack of Fit 4.78 3 1.59 4.54 0.0890 Not significant Pure Error 1.40 4 0.3508 / / / sum 847.87 16 / / / /

[0096] Note: ** indicates extremely significant difference (P<0.01), * indicates significant difference (P<0.05);

[0097] Each experiment was conducted three times in parallel, and then their average values ​​were calculated and expressed as mean ± standard deviation. Origin2018 software and Excel software were used to process the results, and Design-Expert 13 software was used to analyze and calculate the single-factor variance. The significance judgment standard was p < 0.05 for significant difference, and p < 0.01 for extremely significant difference.

[0098] The variance and significance of the regression model were analyzed, and the results are shown in Table 6. As shown in Table 6, the P value of the model is <0.0001, and the model is extremely significant, indicating that the test method is reliable. The F of the lack of fit item is 4.54, P = 0.0890> 0.05, indicating that the difference is not significant, indicating that the test model has a high degree of fit with the test results. The R 2 =0.9927, Correction determination coefficient R adj 2 =0.9833, indicating that the regression model has a high degree of fit with the experimental data and can well reflect the true value of the experiment. The P value shows that the interaction terms (AC, BC) and the first-order term (B) have significant effects on the glycerol content (P < 0.05), and the first-order term (A) and the second-order term (A 2 , B 2 , C 2 ) had a very significant effect on sensory evaluation (P<0.01), while other items had no significant effect on the results (P>0.05);

[0099] From the F value, we can see that the influence of the three factors on the glycerol content is as follows: adsorbent ratio (A) > decolorizing agent addition amount (B) > decolorizing agent decolorizing temperature (C).

[0100] S1073, response surface and contour analysis results:

[0101] Figure 4-9 It is a trend graph of the three factors of adsorbent ratio, decolorizing agent addition, and decolorizing temperature with glycerol content as the response value. The three-dimensional response surface is mapped to the bottom to form a contour outline, indicating the influence of each two factors on the response value and the intensity of their interaction; if the contour is elliptical, it means that the interaction is large, and if the contour is circular, it means that the interaction is small, which is consistent with the conclusion of the regression equation.

[0102] S1074, verification experiment results:

[0103] The fitting equation was analyzed by Design-Expert 13, and the optimal glycerol refining parameters were obtained as follows: the adsorbent ratio was 1.54:1, the decolorant addition amount was 1.03%, and the decolorization temperature was 80.23℃. Under these conditions, the model sensory score prediction value was 99.37 points. The accuracy of the model prediction results was tested. For the convenience of actual operation, the process parameters were adjusted to an adsorbent ratio of 1.5:1, a decolorant addition amount of 1%, and a decolorization temperature of 80℃. Repeated tests were performed to obtain a glycerol content of (99.56±0.07)%, which was not significantly different from the theoretical value. The correctness of the regression model was fully verified, and the response surface test design was reasonable.

[0104] In summary, the present invention first combines a specific adsorbent, an ion exchange method and a decolorant to form a process route for refining crude glycerol, and obtains high-purity glycerol. Secondly, the present invention adopts fuzzy mathematical evaluation combined with response surface method to predict the glycerol content, and obtains the best process route, specifically, the adsorbent ratio (silica gel: zeolite) is 1.5:1, the decolorant addition amount is 1.0%, and the decolorization temperature is 80°C. Under this condition, the glycerol content is (99.55±0.07)%, which is small in deviation from the theoretical prediction value. The response surface method optimizes the glycerol refining process with the advantages of simple operation, precise conditions, and intuitive results; the established regression equation can analyze the influence of various factors on the index and can perform actual prediction.

[0105] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be considered exemplary and non-restrictive in all respects, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present invention.

[0106] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for optimizing a glycerol refining process, characterized in that: The steps include: According to the adopted glycerol refining process, the control factors are determined, and single factor experiments are carried out respectively with the control factors as variables to obtain the optimized first control factors, wherein the control factors are at least selected from any one of the following: the ratio of silica gel and zeolite powder, the amount of decolorizing agent added, and the decolorizing temperature; The glycerol refining process comprises: S1. Mix zeolite powder and silica gel in a certain proportion to prepare an adsorbent; S2, adsorbing the crude glycerol through an adsorbent to obtain a primary product; S3, passing the primary product through an ion exchange resin column to obtain a semi-finished product; S4, adding a decolorizing agent to the semi-finished product for decolorization, and then filtering and drying to obtain a finished product.

2. The method for optimizing the glycerol refining process according to claim 1, characterized in that: The method also includes performing a multi-factor and multi-level response surface test design based on the first control factor to obtain an orthogonal test result, and thereby obtaining an optimized second control factor.

3. The method for optimizing the glycerol refining process according to claim 2, characterized in that: The method further includes evaluating the orthogonal test result according to the second control factor to obtain the optimal third control factor, wherein the evaluation process includes: Set up evaluation subsets and comment sets: take the color, smell, transparency and viscosity of glycerin as evaluation indicators, and the evaluation indicators form the evaluation subset U, U = (U1, U2, U3, U4) = (color, smell, transparency, viscosity); evaluate the grade of refined glycerin, and the comment set of each evaluation indicator is represented by V, V = (poor, good, excellent); Set weight set: assign weights to the evaluation subsets and obtain the weight set K; Determine the fuzzy relationship comprehensive evaluation set: evaluate each evaluation index of refined glycerin, divide the number of votes for each index in each comment by the total number of evaluations, and obtain the fuzzy evaluation matrix a; use fuzzy mathematics methods to process the comprehensive evaluation results of glycerin, and according to the fuzzy matrix transformation principle, multiply the weight set K and the fuzzy evaluation matrix a to obtain the evaluation result y of glycerin; set the score according to the sensory quality grade to obtain the grade matrix R, and the product of the grade matrix R and the evaluation result y is the fuzzy comprehensive score T; Establishment and analysis of quadratic response surface regression model: Taking the ratio of silica gel to zeolite powder A, the amount of decolorizing agent added B, and the decolorizing temperature C as response surface factors, and the glycerol content Y as the response value, the software was used to perform multivariate regression fitting on the response results to obtain the quadratic response surface equation, and variance and significance analysis were performed; The optimal process parameters for glycerin refining were obtained by software analysis and fitting equations, and the fuzzy mathematical sensory evaluation model was used for testing to determine the optimal process parameters for glycerin refining and obtain the optimized glycerin refining process.

4. The method for optimizing the glycerol refining process according to claim 3, characterized in that: The sensory quality is set to be excellent, good and poor as 100 points, 90 points and 80 points respectively, and the grade matrix R = (100, 90, 80).

5. The method for optimizing the glycerol refining process according to claim 3, characterized in that: Weight set K = (color, smell, transparency, viscosity) = (0.35, 0.30, 0.25, 0.10).

6. The method for optimizing the glycerol refining process according to claim 3, characterized in that: The quadratic response surface equation is Y(%) = 99.27 + 1.56A + 0.87B + 0.33C + 0.84AB + 1.29AC - 1.35BC - 11.06A 2 -6.22B 2 -3.6C 2 .

7. The method for optimizing the glycerol refining process according to claim 1, characterized in that: In step S1, the mass ratio of silica gel to zeolite powder is (0.5-2.5):

1.

8. The method for optimizing the glycerol refining process according to claim 1, characterized in that: In step S4, the decolorizing agent includes rice husk charcoal, quartz sand and activated carbon.

9. The method for optimizing the glycerol refining process according to claim 1, characterized in that: In step S4, the amount of decolorizing agent added is 0.5-2.5% of the mass of the semi-finished product.

10. The method for optimizing the glycerol refining process according to claim 1, characterized in that: In step S4, the decolorization temperature is 60-100°C.

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