A method of screening for a catalyst

Abstract

The application discloses a catalyst screening method, comprising the following steps: determining a plurality of pollution evaluation indexes in a water body to be treated; obtaining a change rate of each of the plurality of pollution evaluation indexes after each of a plurality of alternative catalysts is used to treat the water body to be treated; screening a plurality of catalysts and a plurality of correlation pollution evaluation indexes; solving a total change rate of the correlation pollution evaluation indexes of each of the catalysts; and selecting a catalyst with the largest total change rate. The application can improve the screening accuracy of the catalyst, and the catalyst screened has a better catalytic effect.

Description

Technical Field

[0001] The present invention relates to the technical field of catalysts, and in particular to a method for screening catalysts. Background Art

[0002] The petroleum industry is a pillar of national energy development. Petrochemical wastewater, primarily derived from wastewater discharged during petrochemical production, contains a variety of hazardous substances, posing serious risks to the environment and human health. For example, when organic matter, heavy metals, acids, and alkalis in petrochemical wastewater are discharged into water bodies, they can deteriorate water quality, affect the balance of aquatic ecosystems, and, in turn, harm human health. Petrochemical wastewater contains a variety of toxic and hazardous substances, such as benzene, toluene, xylene, phenols, and cyanide. These substances can enter the human body through drinking water and the food chain, causing acute and chronic poisoning. Furthermore, they pose a threat to aquatic life. Toxic substances in petrochemical wastewater can have lethal or sublethal effects on aquatic organisms, affecting their growth, reproduction, and population structure. Therefore, effective treatment technologies are needed for catalytic degradation to ensure that wastewater discharge meets national and local environmental standards, thereby reducing harm to the environment and public health.

[0003] At present, the treatment methods for petrochemical wastewater include oxidation, reduction, hydrogenation, decomposition and other technologies. Among them, ozone catalytic oxidation technology is widely used in the deep treatment of petrochemical wastewater due to its efficient oxidation effect and mild reaction conditions, of which ozone catalyst is the core of the technology. Ozone catalytic oxidation technology forms surface hydroxyl groups through the action of catalysts and water molecules, thereby promoting the decomposition of ozone to produce hydroxyl radicals (·OH) with higher redox potentials, which efficiently and non-selectively degrade organic pollutants in the water, thereby achieving the purpose of purifying the water. Heterogeneous ozone catalytic oxidation technology is regarded as a technology with important application prospects due to the advantages of easy recovery of catalysts, no secondary pollution and low cost. Studies have shown that the selection and optimization of catalysts are the key to improving wastewater treatment effects, especially in the adsorption and catalytic processes. The catalyst can promote ozone decomposition and effectively adsorb pollutants, thereby further improving treatment efficiency.

[0004] In the existing technology, the effectiveness of the catalyst used in wastewater degradation is usually evaluated by changes in water quality indicators, and then a catalyst with good effect on a certain indicator is selected. However, when it comes to selecting catalysts based on the comprehensive treatment effect of petrochemical wastewater, existing research relies more on literature searches and empirical judgment to select catalysts, and is unable to quantitatively and scientifically provide a set of accurate and applicable catalyst screening methods. Summary of the Invention

[0005] In order to solve the above problems, the present invention provides a method for screening catalysts.

[0006] A method for screening a catalyst comprises the following steps:

[0007] Determine various pollution assessment indicators in the water to be treated;

[0008] Conducting a test treatment on the water body to be treated using each of the several candidate catalysts, respectively, to obtain a rate of change of each of the plurality of pollution assessment indicators after the water body to be treated using each of the several candidate catalysts; (wherein the rate of change of the pollution assessment indicator indicates a removal rate of the corresponding pollutant);

[0009] Screening out a plurality of first target catalysts and n correlation pollution assessment indicators corresponding to each of the plurality of first target catalysts according to a change rate of each pollution assessment indicator;

[0010] Based on the n correlation pollution evaluation indicators corresponding to each of the plurality of first target catalysts and the change rates of the n correlation pollution evaluation indicators, the total change rate of the correlation pollution evaluation indicator corresponding to each first target catalyst is solved using formula (1):

[0011] R total =C1+C2...+C n (1);

[0012] Among them, R total is the total change rate of the correlation pollution assessment index corresponding to each first target catalyst, C n is the rate of change of the nth correlation pollution assessment index;

[0013] and Where, is the first correlation pollution evaluation index in multiple groups of time t1~t i The weight coefficient under is the first correlation pollution evaluation index in multiple groups of time t1~t i The rate of change under Where, is the second correlation pollution evaluation index in multiple groups of time t1~t i The weight coefficient under is the second correlation pollution evaluation index in multiple groups of time t1~t i The rate of change under Where, is the nth correlation pollution evaluation index in multiple groups of time t1~t i The weight coefficient under is the nth correlation pollution evaluation index in multiple groups of time t1~t iThe rate of change under

[0014] The total change rate of the correlation pollution assessment index corresponding to each of the multiple first target catalysts is arranged from large to small, and one or more first target catalysts in the top 20-35% are selected as second target catalysts.

[0015] Note: Through the setting of the above method, it is possible to screen out indicators that can characterize the effect of the catalyst from multiple pollutant evaluation indicators. At the same time, these indicators can be used to screen out catalysts with better catalytic effects. This screening method can avoid the problem of unscientific catalyst selection caused by existing literature searches and empirical judgments. In addition, this method can obtain comprehensive evaluation values ​​under multiple groups of time through calculations of the above formula, taking into account issues such as the catalyst's action time limit, thereby improving the accuracy of catalyst screening.

[0016] Furthermore, the pollutant assessment indicators include TOC, UV 254 , EEM, COD, BOD, VOCs and PAHs, and the test treatment is an adsorption test or a catalytic degradation test.

[0017] Note: The above indicators are common indicators for wastewater treatment. This scheme can also include other water quality indicators. In the above test treatment process, because for wastewater treatment, the adsorption capacity of the catalyst usually directly affects its catalytic performance, the adsorption performance of the catalyst is positively correlated with its catalytic oxidation performance, and compared with the saturated adsorption capacity, the rapid adsorption capacity of the catalyst is more important to the catalytic effect during actual operation. Therefore, this scheme directly uses adsorption test treatment to screen catalysts, and the screened catalysts can accurately reflect their catalytic effects; in addition, the use of catalytic degradation test compared to adsorption test can more directly characterize its catalytic degradation effect, but the disadvantage is that the catalytic degradation test is more complicated, the test cost is higher, it takes a long time and is greatly affected by the degradation conditions, and the study found that the results of the degradation test are consistent with those of the adsorption test.

[0018] Furthermore, the weight coefficient of the jth first target catalyst among the plurality of first target catalysts is Calculated by formula (2):

[0019]

[0020] In formula (2), is the weight coefficient, d j is the difference coefficient of the jth first target catalyst, and the total number of first target catalysts is p.

[0021] Note: The weight coefficient can be obtained through the above formula. The weight coefficient obtained by this method can more accurately reflect the differences between individual catalysts, so as to facilitate the calculation of the weighted change rate.

[0022] Furthermore, the difference coefficient d of the j-th first target catalyst j The formula (3) is satisfied:

[0023]

[0024] In formula (3), e j is the information entropy value of the jth first target catalyst. The larger the information entropy value, the lower the weight of the indicator. The smaller the information entropy value, the higher the weight of the indicator. ij is the relative value of the jth first target catalyst under the correlation pollution assessment index i, where x ij is the change rate of the correlation pollution evaluation index i corresponding to the j-th first target catalyst, and n is the number of correlation pollution evaluation indicators.

[0025] The change rate x under the correlation pollution assessment index i corresponding to the j-th first target catalyst ij Satisfying formula (4):

[0026]

[0027] In formula (4), y0 represents the monitoring value of the correlation pollution assessment index i in the water to be treated before the catalyst treatment test, y i It represents the monitoring value of the correlation pollution assessment index i in the water body after the catalyst treatment test.

[0028] Furthermore, the second target catalyst is used to perform ozone catalytic oxidation on water bodies having the correlation pollution assessment index.

[0029] Description: Ozone catalytic oxidation has a highly efficient oxidation effect and mild reaction conditions. It is widely used in the deep treatment of petrochemical wastewater. Therefore, the screening of catalysts in this field is more important.

[0030] Furthermore, the various catalysts are all prepared by using γ-Al2O3 as a carrier and loading one or more metal ions.

[0031] Note: Through the above settings, the most suitable loading components can be screened out from the catalyst with γ-Al2O3 as the carrier. The loaded components may be single-component, two-component and three-component. In addition to screening out better catalysts, this scheme can also screen out catalysts with better effects when loaded with several components.

[0032] Further, the method for screening the plurality of first target catalysts according to the change rate of each pollution evaluation index is:

[0033] determining whether the content of each of the plurality of alternative catalysts has a significant influence on the change rate of each of the plurality of pollution evaluation indexes;

[0034] If there is a significant influence, the alternative catalyst is used as the first target catalyst.

[0035] Description: By the above-mentioned method, the catalyst can be selected less, and the catalyst having an effect on the catalytic degradation reaction can be screened, thereby reducing the workload of subsequent calculation.

[0036] Further, the correlation pollution evaluation index is a pollution evaluation index having correlation between the change rates of any two pollution evaluation indexes corresponding to each first target catalyst.

[0037] The beneficial effects of the present application are:

[0038] The present application can screen the index capable of representing the effect of the catalyst from a plurality of pollution evaluation indexes, and meanwhile, the catalyst having good catalytic effect can be screened by using the indexes, and the method for screening can avoid the problem of unscientific selection of the catalyst caused by the existing literature search and experience judgment, and in addition, the comprehensive evaluation value under a plurality of groups of time can be obtained by calculation of the above-mentioned formula, and the accuracy of the catalyst screening is improved by considering the time limit of the catalyst. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 is a process flow diagram of the existing petrochemical wastewater treatment process in the experimental example of the present application;

[0040] Figure 2 is a schematic diagram of the ozone catalytic oxidation experiment in the experimental example of the present application;

[0041] Figure 3 is a scatter plot matrix of the adsorption change rates of different pollution indexes in the embodiment of the present application;

[0042] Figure 4 is a correlation thermodynamic diagram of the adsorption change rates of different pollution indexes in the embodiment of the present application;

[0043] Figure 5 is a violin plot of the pollution adsorption index change rates of single, double and triple component catalysts in the embodiment of the present application;

[0044] Figure 6 is the weight of the adsorption change rate of different pollution indicators in the embodiment of the present invention;

[0045] Figure 7 This is the data distribution of the highest adsorption change rate of the single-component, double-component, and three-component catalysts in the embodiment of the present invention;

[0046] Figure 8 This is a heat map showing the correlation between different active components and adsorption change rates in the embodiments of the present invention;

[0047] Figure 9 This is a comparison of TOC removal effects by adsorption and ozone catalytic oxidation in the embodiments of the present invention;

[0048] Figure 10 This is a comparison of the removal effects of UV254 by adsorption and ozone catalytic oxidation in the embodiments of the present invention;

[0049] Figure 11 This is a heat map of the correlation between the change rates of various pollution indicators of the ozone catalytic oxidation two-component catalyst in an embodiment of the present invention;

[0050] Figure 12 This is a heat map of the correlation between the change rates of various pollution indicators of the three-component ozone catalytic oxidation catalyst in an embodiment of the present invention;

[0051] Figure 13 This is a heat map of the correlation between the change rates of various pollution indicators of the single-component ozone catalytic oxidation catalyst in an embodiment of the present invention;

[0052] Figure 14 3 is a comparison of the integrated volume of the fluorescence area after adsorption and ozone catalytic oxidation in the embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to further illustrate the approach and effects achieved by the present invention, the technical solution of the present invention will be clearly and completely described below in conjunction with experiments.

[0054] The method provided in an embodiment of the present invention relates to the field of catalyst screening technology and can be used in the wastewater treatment process to screen out catalysts with better comprehensive catalytic effects. Specifically, a weighted calculation is performed using the pollutant change rates under multiple indicators to obtain a total pollutant change rate value, and this total change rate value is used to measure and screen out the catalyst with the best treatment effect.

[0055] In the prior art, usually the importance is determined manually, and then a rough judgment is made to obtain a suitable catalyst. This method relies too much on literature and manual experience, and it is difficult to quantify it into a standard value for calculation. It should be understood that when measuring the effect of water treatment (i.e. the effect of the catalyst), a variety of indicators are usually required. Water quality indicators (i.e. the pollutant evaluation indicators described in the present invention) (such as TOC, UV 254 This is because, compared with using only one indicator, the use of multiple indicators is comprehensive, accurate, complementary, and has better early warning capabilities. It can also evaluate the treatment effect. The comprehensiveness is reflected in the fact that different water quality indicators reflect multiple aspects of water quality. For example, TOC mainly reflects the total amount of organic matter, while UV 254 It focuses more on organic matter with specific structures (such as conjugated double bonds or aromatic rings). Combining multiple indicators can provide a more comprehensive understanding of water quality. A single indicator may not accurately reflect complex water quality conditions. Accuracy is reflected in: the combined use of multiple indicators can improve the accuracy of water quality evaluation and reduce the possibility of misjudgment. Complementarity is reflected in: different indicators can complement each other. For example, TOC can provide information on the total amount of organic matter, while UV 254 It can provide information on the types and structures of organic matter. This complementarity helps to gain a deeper understanding of water quality issues. Some indicators are more sensitive to specific types of pollution. By monitoring these indicators, changes in water quality can be detected before the concentration of pollutants reaches a harmful level, so that countermeasures can be taken in advance. In the process of water quality treatment, multiple indicators can more comprehensively evaluate the treatment effect. For example, a reduction in TOC may indicate that organic matter is effectively removed, while a decrease in UV may indicate that organic matter is effectively removed. 254 Changes in the abundance of α-glucan may indicate changes in specific types of organic matter.

[0056] Since the above indicators reflect different degrees of catalyst performance, directly adding the change rate values ​​of each indicator may result in a catalyst that is not optimal. Therefore, in a multi-indicator system, the above method is used to assign weights to the indicators, which can accurately measure the relative importance of each indicator in the overall system.

[0057] Research has found that loading different active components can significantly improve the adsorption and removal of different pollutants in wastewater. The adsorption capacity of a catalyst directly affects its ozone catalytic oxidation performance. Previous studies have shown that the adsorption performance of a catalyst is positively correlated with its catalytic oxidation performance, and that the rapid adsorption capacity of a catalyst is more important for the catalytic oxidation effect during actual operation than the saturated adsorption capacity. However, existing research lacks in-depth understanding of the adsorption process of individual catalysts and the differences in the effects of different catalyst components on the adsorption and removal of wastewater by the catalyst.

[0058] like Figure 1As shown, the embodiment of the present invention is implemented using a petrochemical wastewater as the water body to be treated, wherein a plurality of catalysts are catalysts using γ-Al2O3 as a carrier, respectively loaded with one or more metal ions; the one or more catalysts are used to perform ozone catalytic oxidation tests on pollutants in the water to be treated (the principle is as shown in FIG. Figure 2 As shown); The following is a catalyst screening method provided by the present invention, including S1-S5;

[0059] S1. Determine various pollution assessment indicators in the water to be treated;

[0060] Wherein, the change rate of the pollution assessment index indicates the removal rate of the corresponding pollutant;

[0061] Petrochemical wastewater (i.e., the water body to be treated in the embodiment of the present invention) is taken from the secondary effluent of a typical petrochemical integrated sewage treatment plant. The sewage treatment scale of the plant is 5000m3 / h. The influent water quality of the sewage treatment plant is complex, and there are more than 70 sources, including fertilizer plants, resin plants, refineries, styrene plants, etc. The biochemical treatment is hydrolysis acidification + A / O method, and the secondary effluent is light yellow-brown. The secondary effluent of the plant is deeply treated by micro-flocculation combined with catalytic ozone oxidation process. Its process flow is as follows Figure 1 shown.

[0062] The pollutant assessment indicators include TOC, UV 254 , EEM, COD, BOD, VOCs and PAHs;

[0063] Determination of pollutant assessment indicators: Petrochemical wastewater samples were treated with a 0.45 μm filter membrane before measurement, and TOC concentration was determined using a total organic carbon analyzer using catalytic combustion oxidation-non-dispersive infrared absorption method; UV 254 Measurements were made using a UV-visible spectrophotometer; EEM was measured using a fluorescence spectrophotometer with excitation and emission slit widths of 5 nm, Ex and Em ranging from 200 to 550 nm, and a scan rate of 12,000 nm / min. The fluorescence intensity of ultrapure water was measured simultaneously with the sample to correct for Raman and Rayleigh scattering interferences. Data analysis was performed using the fluorescence regional integration method (FRI). The FRI method divides the fluorescence spectrum into five regions, I-V, representing five types of organic matter: tyrosine-like, tryptophan-like, fulvic acid-like, soluble metabolite-like, and humic acid-like. The remaining regions were measured using existing methods and are not described here.

[0064] S2. Conducting a test treatment on the water body to be treated using each of the several candidate catalysts, respectively, to obtain a rate of change of each of the plurality of pollution assessment indicators after the water body to be treated using each of the several candidate catalysts;

[0065] In the embodiment of the present invention, the test process is an adsorption test;

[0066] The adsorption test method is: weigh 60g of catalyst, wash it with clean water, pour it into a conical flask, add pure water, set the shaker to 25℃, 100r / min, until the TOC of the supernatant is zero, pour the catalyst into the conical flask, and use other containers to prepare 120mL of wastewater. Perform two parallel experiments for each catalyst at the same time. First add wastewater to the two conical flasks and quickly put them into the shaker (start timing, turn on the shaker), and take 20mL of samples at 3, 6, 10, and 15 minutes respectively. Stop timing when sampling. In order to ensure that the volume ratio of the catalyst to the water sample in the container is the same after each sampling, each conical flask needs to take out an appropriate amount of catalyst when sampling, and try to control the sampling time within 1 minute. It is necessary to measure the pollutant assessment indicators of each sample later (TOC, UV 254 , EEM, etc.).

[0067] Specifically, the change rate x under the correlation pollution assessment index i corresponding to the j-th first target catalyst ij satisfy Where y0 represents the monitoring value of the correlation pollution assessment index i in the water to be treated before the catalyst treatment test, y i It represents the monitoring value of the correlation pollution assessment index i in the water body after the catalyst treatment test.

[0068] S3. Screening out a plurality of first target catalysts and n correlation pollution assessment indicators corresponding to each of the plurality of first target catalysts according to the change rate of each pollution assessment indicator;

[0069] The method for screening out a plurality of first target catalysts according to the change rate of each pollution assessment index is:

[0070] S3-1. Determining whether the content of each of the several candidate catalysts has a significant effect on the rate of change of each of the multiple pollution assessment indicators;

[0071] S3-2. If there is a significant impact, the alternative catalyst is used as the first target catalyst.

[0072] For example, one-way analysis of variance (ANOVA) is used to determine the significant effects mentioned above. Specifically, one-way analysis of variance can be used to explore whether the content of the three active components as independent variables will affect the change rate of TOC, UV254 and five fluorescent substances after the catalyst adsorbs petrochemical wastewater. One-way analysis of variance is performed on the experimental data using Python language to explore the effects of changes in the three components of Cu, Mn and Ce on TOC, UV254 and five fluorescent substances.254 And whether it has a significant effect on the adsorption change rate of five fluorescent substances.

[0073] The results showed that all three components had significant effects, that is, the catalysts loaded with Cu, Mn and Ce were selected for subsequent calculation process.

[0074] It should be understood that ANOVA is a method for analyzing the influence of categorical independent variables on numerical dependent variables. The influence of an independent variable on a dependent variable is also called the independent variable effect. The magnitude of this effect is reflected in how much of the error in the dependent variable is attributable to the independent variable. Therefore, ANOVA uses the analysis of the numerical error in the dependent variable to test whether this effect is significant. By comparing systematic error (uncertain control factors) with other random errors, ANOVA eliminates the possibility of chance in statistical results and assesses the significance of differences between sample groups, thereby evaluating the degree of influence of the variable on the overall sample.

[0075] S3-3, the correlation pollution assessment index is a pollution assessment index in which the change rates of any two pollution assessment indicators among the multiple pollution assessment indicators corresponding to each first target catalyst are correlated;

[0076] For example, a correlation analysis method is used to determine whether there is a correlation, that is, when the significance p value between the change rates of any two pollution assessment indicators is less than 0.05, the two are considered to be correlated, and when the p value is greater than or equal to 0.05, they are considered to be uncorrelated; specifically, Figure 4 The analysis shows that the TOC, UV 254 And the data distribution of the change rates of the five fluorescent substances. If the data distribution is closer to the straight line y=x, the greater the correlation between the two indicators. Figure 4 The correlation between the change rates of various indicators was analyzed, and the larger the value, the higher the correlation.

[0077] After screening, TOC, UV 254 and five fluorescent substances as pollutant assessment indicators in this embodiment;

[0078] like Figure 3 and Figure 4 As shown in FIG, the closer the data distribution is to the straight line y=x, the greater the correlation between the two indicators. Figure 4 The correlation between the change rates of various indicators was analyzed. The larger the value, the higher the correlation. Figure 3 and Figure 4It can be seen that there is a strong correlation between the adsorption change rates of TOC and UV254 at different time points, and the correlation analysis of the five fluorescent substances shows that the changes of fulvic acid-like, humic acid-like (i.e. regions III and V), tyrosine-like, tryptophan-like and soluble metabolic product-like (i.e. regions I, II and IV) substances during the adsorption process have a strong correlation.

[0079] S4, based on the n correlation pollution evaluation indexes corresponding to each of the plurality of first target catalysts, and the change rates of the n correlation pollution evaluation indexes, solving the total change rate of the correlation pollution evaluation index corresponding to each of the first target catalysts by formula (1):

[0080] R total =C1+C2…+C n (1);

[0081] Wherein, R total is the total change rate of the correlation pollution evaluation index corresponding to each of the first target catalysts, C n is the change rate of the nth correlation pollution evaluation index;

[0082] And In the formula, is the weight coefficient of the first correlation pollution evaluation index in the plurality of groups of time t1-t i , is the change rate of the first correlation pollution evaluation index in the plurality of groups of time t1-t i ; In the formula, is the weight coefficient of the second correlation pollution evaluation index in the plurality of groups of time t1-t i , is the change rate of the second correlation pollution evaluation index in the plurality of groups of time t1-t i ; In the formula, is the weight coefficient of the nth correlation pollution evaluation index in the plurality of groups of time t1-t i , is the change rate of the nth correlation pollution evaluation index in the plurality of groups of time t1-t i ;

[0083] The weight coefficient of the jth first target catalyst in the plurality of first target catalysts is calculated by formula (2):

[0084]

[0085] The difference coefficient d jThe formula (3) is satisfied:

[0086]

[0087] In formula (3), e j is the information entropy value of the jth first target catalyst. The larger the information entropy value, the lower the weight of the indicator. The smaller the information entropy value, the higher the weight of the indicator. ij is the relative value of the jth first target catalyst under the correlation pollution assessment index i, where x ij is the change rate of the correlation pollution evaluation index i corresponding to the j-th first target catalyst, and n is the number of correlation pollution evaluation indicators.

[0088] The change rate x under the correlation pollution assessment index i corresponding to the j-th first target catalyst ij Satisfying formula (4):

[0089]

[0090] In formula (4), y0 represents the monitoring value of the correlation pollution assessment index i in the water to be treated before the catalyst treatment test, y i It represents the monitoring value of the correlation pollution assessment index i in the water body after the catalyst treatment test.

[0091] For example, by weighted combination of the change rates of various indicators, Figure 6 The calculated weights are shown. It can be seen that the weights of fluorescent regions I, II, and IV are relatively large, indicating that the adsorption of the catalyst has the most obvious removal effect on the removal of fluorescent substances. The total change rate of the catalyst, namely formula (1), is further obtained as follows:

[0092] R total =0.0186·R TOC-3 +0.0058·R TOC-6 +0.0043·R TOC-10 +0.0030·R TOC-15 +0.0123·R UV254-3 +0.0062·R UV254-6 +0.0034·R UV254-10 +0.0018·R UV254-15 +0.1615·R EEM-Ⅰ +0.3183·R EEM-Ⅱ +0.0673·R EEM-Ⅲ +0.3033·R EEM-Ⅳ +0.0185·R EEM-Ⅴ +0.0757·R 总EEM .

[0093] S5. Arrange the total change rate of the correlation pollution assessment index corresponding to each of the multiple first target catalysts from large to small, and select one or more first target catalysts in the top 20-35% as the second target catalyst.

[0094] For example, according to the weights of each indicator obtained by the above entropy weight method, the two-component catalyst has the best single-component adsorption removal effect by weighting using the entropy weight method; the top three catalysts with the best single-component adsorption removal effect by weighting are 2, 48, and 16, and the top three catalysts with the best two-component adsorption removal effect are No. 49, No. 51, and No. 60 (this number is based on the different contents of the three components of Ce, Cu, and Mn. The specific content of each is shown in Table 1 below), and the top three catalysts with the best three-component adsorption removal effect are No. 59, No. 58, and No. 47.

[0095] Table 1 Catalyst numbers obtained with different contents of Ce, Cu and Mn

[0096] Number Ce (kg / 12 kg) Cu (kg / 12 kg) Mn (kg / 12 kg) 2 0 0 0.83 16 0.83 0 0 47 1.65 1.53 1.67 48 3.30 0 0 49 3.30 0 0.42 51 3.30 0 1.67 58 3.30 0.76 0.83 59 3.30 0.76 1.67 60 3.30 1.53 0

[0097] Experimental example:

[0098] 1. Verify the catalytic effect of the selected catalyst through ozone catalytic oxidation test;

[0099] In actual engineering, the catalyst can reach adsorption saturation in a short time after loading, and the removal of pollutants mainly depends on the catalytic ability of the catalyst rather than its adsorption ability. Therefore, in the ozone catalytic oxidation test, in order to eliminate the influence of adsorption on the catalytic oxidation effect, the catalyst is placed in the petrochemical secondary effluent and soaked for more than 24 hours before the test to achieve saturated adsorption of pollutants. Theoretically, the adsorption saturated catalyst will no longer have the adsorption and removal effect on pollutants. Then, the soaked and drained catalyst is placed in the reactor, 100mL of petrochemical wastewater is added, and 5mg / L of ozone is introduced at a gas flow rate of 30mL / min. The reaction is carried out for 120min, so that the ozone dosage for the entire reaction is 180mg / L. After the reaction, the TOC and UV before and after ozone catalytic oxidation are measured. 254 and EEM.

[0100] The three catalysts with the best single, double and three components screened by the entropy weight method were tested for ozone catalytic oxidation, and the oxidation results were compared with the adsorption results. Figure 9-12 As shown, Figure 9Figure 9a is a comparison of the TOC change rates after adsorption and oxidation of each catalyst. Figure 9a is the TOC change rate after adsorption and oxidation of three single-component catalysts No. 2, No. 48, and No. 16. It can be seen that the TOC change rate of the effluent after ozone catalytic oxidation is higher than that of the adsorption process, which is 5%-10% higher than that of adsorption removal, and the removal effect of catalyst No. 2 is the best. The active component of this catalyst is Mn (6.92%). Figure 9 b is a comparison of three two-component catalysts No. 49, No. 51, and No. 60. Similarly, the TOC change rate after ozone catalytic oxidation is about 10% higher than that after adsorption. Catalyst No. 51 has the best treatment effect. The Ce content of this catalyst is 27.5% and the Mn content is 13.92%. Figure 9 c is a comparison of the TOC change rates of three three-component catalysts No. 59, No. 58 and No. 47. It can be seen that the TOC after oxidation is about 8% higher than that after adsorption. The treatment effect of catalyst No. 47 is relatively the best. The Ce, Cu and Mn contents of this catalyst are 13.75%, 12.75% and 13.92% respectively.

[0101] Figure 10 UV after adsorption and oxidation of each catalyst 254 From the comparison of the change rates, it can be seen that the change rate of UV254 after ozone catalytic oxidation is about 40% higher than that after adsorption. Figure 10 a is the UV after adsorption and oxidation of three single-component catalysts No. 2, 48, and 16 254 The change rate shows that after the ozone catalytic oxidation of catalyst No. 2, UV 254 The change rate can reach 81.7%, and the removal effect is the best. Figure 10 b is a comparison of three dual-component catalysts, No. 49, No. 51, and No. 60. Catalyst No. 51 has the best treatment effect. After ozone catalytic oxidation, UV 254 The rate of change can reach 76.88%. Figure 10 c is three kinds of three-component catalyst UV No. 59, No. 58 and No. 47 254 The comparison of the change rates shows that the treatment effect of catalyst No. 47 is relatively the best. The UV 254 The rate of change is 77.34%.

[0102] like Figure 11 、 Figure 12 、 Figure 13 As shown in the figure, the correlation analysis of the change rate of each pollutant after ozone catalytic oxidation found that after petrochemical wastewater was catalytically oxidized by single, double and three component catalysts, UV 254 The change rate of UV is significantly positively correlated with the change rate of the five fluorescent substances and the total fluorescent substances, indicating that in ozone catalytic oxidation, UV 254The catalyst with good treatment effect also has good treatment effect on the five fluorescent substances. 254 The treatment effects of the best single-component catalyst No. 2, two-component catalyst No. 51, and three-component catalyst No. 47 on five fluorescent substances after ozone catalytic oxidation were analyzed.

[0103] like Figure 14 As shown in the figure, compared with the raw water, the fluorescence area and intensity of the five regions of the wastewater decreased after the wastewater was adsorbed and oxidized by each catalyst. By comparing the integrated volume of the fluorescence area after the adsorption and oxidation of each catalyst, it can be obviously seen that the fluorescence intensity of the five fluorescent substances was greatly reduced after the wastewater was catalytically oxidized by ozone, especially the catalyst No. 2 had the best removal effect on fluorescent substances after ozone catalytic oxidation.

[0104] A comprehensive analysis of the above results reveals that catalysts No. 2, No. 51, and No. 47 exhibit the best ozone catalytic oxidation performance. This is because increasing the percentage of the three active components, Cu, Mn, and Ce, increases the number of active sites within the catalyst, thereby enhancing catalytic activity and increasing the rate of pollutant conversion in the wastewater. However, further increases in the percentage of active components, such as when the Ce percentage exceeds 13.75%, may cause catalyst pore blockage, thereby decreasing the rate of pollutant conversion.

[0105] Second, determine the catalyst selection mechanism through data analysis methods;

[0106] 1. Preferentially select a catalyst with a two-component γ-Al2O3 as the carrier;

[0107] In the adsorption test of this study, after the petrochemical wastewater was adsorbed by the catalyst, the integrated volume of the fluorescent areas III and V decreased, indicating that after the catalyst adsorption, the difficult-to-degrade substances in the petrochemical wastewater were effectively removed. To further evaluate the distribution of the data, the catalysts were divided into single-component catalysts, two-component catalysts, and three-component catalysts. The data distribution violin plot was drawn for the change rate of different indicators at different time points. Figure 5 It can be seen that TOC and UV 254 The rate of change of TOC and UV showed a gradual upward trend with the increase of time, indicating that the removal effect continued to improve with the extension of treatment time. 254 The adsorption change rate distributions of fluorescent substances are relatively concentrated, but compared with the two-component and three-component catalysts, the adsorption change rate distributions of fluorescent substances of the single-component catalyst are relatively discrete, indicating that the adsorption and removal effect of the single-component catalyst is relatively unstable.

[0108] To further illustrate the effect, the score density distribution of the best catalyst obtained by weighting the single-component, double-component and triple-component catalysts is analyzed, as shown in Figure 2.Figure 7 As shown. It can be seen that Figure 5 The results are consistent with those shown in . The score distribution of the two-component catalyst is the most concentrated, indicating that the performance of the two-component catalyst is the most stable, while the distribution of the single-component catalyst is the most discrete and has a large variability, indicating that the performance stability of the single-component catalyst is poorer than that of the other two types of catalysts.

[0109] Therefore, when the catalyst of this embodiment is subsequently loaded with metal components, the dual-component catalyst is preferably selected and the single-component catalyst is not considered.

[0110] 2. Prove the adsorption effect of a certain component on a certain indicator;

[0111] ANOVA analysis of variance was used to further analyze the relationship between different active components and different adsorption change rates. Table 2 shows the p-values ​​from the ANOVA analysis of variance. Smaller p-values ​​indicate greater significance. Consistent with the results of the correlation analysis, it can be seen that the rare earth element Ce significantly influences the adsorption change rates of fluorescent regions I-IV, further demonstrating that the presence of Ce can effectively adsorb and remove protein-like and fulvic acid-like substances.

[0112] Table 2 ANOVA analysis of variance between the adsorption rate of different pollutants and different active components

[0113]

[0114]

[0115] (*:p≤0.05,**:p≤0.01***:p≤0.001,****:p≤0.0001)

[0116] Further analysis of the relationship between the type of catalyst active components and different adsorption change rates is shown in Table 3. The analysis shows that the single, double and triple component catalysts have a significant effect on TOC and UV 254 The adsorption removal showed a significant effect, indicating that the combination and amount of metals in the catalyst have a significant impact on TOC, UV 254 The effect of adsorption removal is small.

[0117] In addition, all three component types have a significant effect on the adsorption and removal of fluorescent region V. The two-component and three-component catalysts also have a significant effect on the fluorescent region III, i.e., fulvic acid-like substances, but the three-component catalyst has a greater impact, indicating that the three-component catalyst is more beneficial for the adsorption and removal of difficult-to-degrade fulvic acid-like and humic acid-like substances in petrochemical wastewater. This may be because the addition of Cu, Mn, and Ce is loaded on the alumina surface, which increases the pore volume and pore size of the catalyst, thereby increasing the contact area between difficult-to-degrade substances such as fulvic acid-like and humic acid-like substances and the catalyst, thereby enhancing the adsorption effect of the catalyst on such substances.

[0118] Table 3 ANOVA analysis of variance between the adsorption change rate of different pollutants and different component types

[0119]

[0120]

[0121] (In Table 3, *: p≤0.05, **: p≤0.01, ***: p≤0.001, ****: p≤0.0001)

[0122] Based on the above analysis, we further used statistical test methods to obtain the three components and TOC, UV 254 The relationship between the adsorption and removal effect of fluorescent substances. First, the Spearman correlation coefficient was used to calculate the correlation between the catalyst active components and the adsorption change rate of each index. Figure 8 The Spearman correlation coefficients between different active components and the adsorption change rates of various indicators are shown. Figure 8 It can be seen that the metal component Ce shows a strong correlation with the adsorption and removal of total fluorescent substances and the adsorption and removal of fluorescent areas I-IV, indicating that metal Ce plays an important role in the adsorption and removal of tyrosine-like, tryptophan-like, fulvic acid-like, and soluble metabolites by the catalyst. This may be because the addition of rare earth element oxides can accelerate the activation of lattice oxygen and make the active components more dispersed in the catalyst, thereby making the catalyst more effective in adsorbing and removing these fluorescent substances.

Claims

1. A method for screening a catalyst, characterized in that: The following steps are involved: Determine various pollution assessment indicators in the water to be treated; Conducting a test treatment on the water body to be treated using each of the several candidate catalysts, respectively, to obtain a change rate of each of the plurality of pollution assessment indicators after the water body to be treated using each of the several candidate catalysts; Screening out a plurality of first target catalysts and n correlation pollution assessment indicators corresponding to each of the plurality of first target catalysts according to a change rate of each pollution assessment indicator; Based on the n correlation pollution evaluation indicators corresponding to each of the plurality of first target catalysts and the change rates of the n correlation pollution evaluation indicators, the total change rate of the correlation pollution evaluation indicator corresponding to each first target catalyst is solved using formula (1): R total =C1+C2...+C n (1); Among them, R total is the total change rate of the correlation pollution assessment index corresponding to each first target catalyst, C n is the rate of change of the nth correlation pollution assessment index; and Where, is the first correlation pollution evaluation index in multiple groups of time t1~t i The weight coefficient under is the first correlation pollution evaluation index in multiple groups of time t1~t i The rate of change under Where, is the second correlation pollution evaluation index in multiple groups of time t1~t i The weight coefficient under is the second correlation pollution evaluation index in multiple groups of time t1~t i The rate of change under Where, is the nth correlation pollution evaluation index in multiple groups of time t1~t i The weight coefficient under is the nth correlation pollution evaluation index in multiple groups of time t1~t i The rate of change under The total change rate of the correlation pollution assessment index corresponding to each of the multiple first target catalysts is arranged from large to small, and one or more first target catalysts in the top 20-35% are selected as second target catalysts.

2. The method for screening a catalyst according to claim 1, wherein: The various pollution assessment indicators include: TOC, UV 254 , EEM, COD, BOD, VOCs and PAHs, and the test treatment is an adsorption test or a catalytic degradation test.

3. The method for screening a catalyst according to claim 1, wherein: The weight coefficient of the jth first target catalyst among the plurality of first target catalysts Calculated by formula (2): In formula (2), is the weight coefficient, d j is the difference coefficient of the jth first target catalyst, and the total number of first target catalysts is p.

4. The method for screening a catalyst according to claim 3, wherein: The difference coefficient d of the j-th first target catalyst j The formula (3) is satisfied: In formula (3), e j is the information entropy value of the jth first target catalyst. The larger the information entropy value, the lower the weight of the indicator. The smaller the information entropy value, the higher the weight of the indicator. ij is the relative value of the jth first target catalyst under the correlation pollution assessment index i, where x ij is the change rate of the correlation pollution evaluation index i corresponding to the j-th first target catalyst, and n is the number of correlation pollution evaluation indicators.

5. The method for screening a catalyst according to claim 4, wherein: The change rate x under the correlation pollution assessment index i corresponding to the j-th first target catalyst ij Satisfying formula (4): In formula (4), y0 represents the monitoring value of the correlation pollution assessment index i in the water to be treated before the catalyst treatment test, y i It represents the monitoring value of the correlation pollution assessment index i in the water body after the catalyst treatment test.

6. The method for screening a catalyst according to claim 1, wherein: The second target catalyst is used to perform ozone catalytic oxidation on a water body having the correlation pollution assessment index.

7. The method for screening a catalyst according to claim 6, wherein: The various catalysts are all prepared by using γ-Al2O3 as a carrier and loading one or more metal ions.

8. The method for screening a catalyst according to claim 1, wherein: The method for screening out a plurality of first target catalysts according to the change rate of each pollution assessment index is: Determining whether the content of each of the several candidate catalysts has a significant effect on the rate of change of each of the multiple pollution assessment indicators; If there is a significant effect, the candidate catalyst is taken as the first target catalyst.

9. The method for screening a catalyst according to claim 1, wherein: The correlation pollution assessment index is a pollution assessment index in which the change rates of any two pollution assessment indicators among the multiple pollution assessment indicators corresponding to each first target catalyst are correlated.

Images