Response surface optimization method for wastewater treatment by electrooxidation process

Abstract

The invention discloses a response surface optimization method for wastewater treatment by an electrooxidation process, which comprises the following steps: (1) single factor experiment: carrying out electrooxidation experiment by taking current density, initial pH, electrode spacing and treatment time as independent variables, and measuring COD (Chemical Oxygen Demand) by using national standard HJ 828-2017; (2) response surface method test design: taking current density, initial pH and treatment time as three factor conditions, selecting a proper level range according to a single factor test result, and performing test design by using Design Expert 13 software according to a Box-Behnken design method; (3) performing variance analysis on the model: establishing a multivariate quadratic regression equation according to the data in the step (2) by using Design Expert 13 software, and performing variance analysis and significance analysis on the quadratic multivariate regression model equation; and (4) response surface method optimization: according to a response surface diagram and Design Expert 13 software analysis, obtaining an optimal process condition. According to the method, the optimal process parameters of the COD removal rate and the energy consumption in the electrochemical oxidation wastewater treatment process are helped to be determined through the response surface method, and reference is provided for formulating an efficient and energy-saving treatment scheme in actual engineering application.

Description

Technical Field

[0001] The invention relates to the technical field of sewage treatment, and in particular to a response surface optimization method for treating wastewater by an electro-oxidation process. Background Art

[0002] Advanced oxidation processes (AOPs) based on hydroxyl radicals, also known as advanced oxidation processes, are considered to be one of the most effective technologies for decomposing environmental pollutants because of their fast reaction speed and ability to completely remove and recycle treated wastewater. The outstanding feature of AOPs is the generation of hydroxyl radicals with strong oxidizing ability through homogeneous or heterogeneous catalysis, which is considered to be the most effective oxidant for degrading difficult-to-degrade organic pollutants in water. Among AOPs, electro-oxidation has been widely used and studied in the field of wastewater treatment due to its advantages such as high efficiency, environmental protection, economy and simple operation. Although electro-oxidation has many advantages, in terms of engineering applications, the power consumption of electrochemical oxidation methods is relatively high, most of which are between 10 and 60 kWh / m 3 , which increases operating costs. Response surface methodology (RSM) is a mathematical and statistical technique used to model and analyze optimization problems involving multiple variables. In technology and engineering, this method can efficiently and accurately identify the optimal variable levels to achieve the best process conditions.

[0003] Chinese patent CN106971080A discloses a response surface optimization method for modified biochar adsorption of phosphorus in wastewater, comprising the following steps: (1) preparation of modified biochar; (2) Plackett-Burman test; (3) steepest climbing test; (4) response surface optimization experimental design; (5) multiple regression analysis, establishment of quadratic multiple regression model equation; (6) variance analysis and significance analysis of the quadratic multiple regression model equation; (7) using Design Expert 8.0.5 software to draw and analyze the relationship between independent variables and response value Y according to the quadratic multiple regression model, obtain the response surface diagram of the regression equation, and obtain the optimal adsorption conditions for dynamic adsorption. Although the patent uses the response surface method to optimize the adsorption of phosphorus in wastewater by biochar, it does not involve the use of the response surface method to optimize the electro-oxidation process technology. The literature (Response Surface Methodology for Optimizing Electro-Oxidation-Flocculation Coupling Process for Deep Treatment of Landfill Leachate, China Environmental Science, 2017, 37(3): 948-955.) mentioned that the electro-oxidation-flocculation coupling process is used to remove residual organic matter (COD) and total nitrogen (TN) in the biochemical effluent of landfill leachate. The single factor and response surface method were used to explore the effects of plate spacing, circulation flow rate and chloride ion (Cl-) concentration on the removal rate of COD and TN in landfill leachate. However, the main goal was the removal rate, and the issue of energy consumption was ignored. The literature (Process Optimization of Electro-Oxidation-Electro-Flocculation Treatment of Thallium-Containing Industrial Wastewater, Contemporary Chemical Research, 2024, 15: 160-162.) used the response surface optimization method to study the optimal operating conditions and the interaction between various factors of electro-oxidation-electro-flocculation (EO-EC) coupling treatment of thallium-containing wastewater, but did not reflect the mutual balance between removal efficiency and energy consumption. Summary of the invention

[0004] The invention provides a response surface optimization method for treating wastewater by electro-oxidation process. The response surface method and BoxBehnken design are used to analyze the influence of current density, initial pH and treatment time on COD removal efficiency and specific energy consumption. A mathematical model of process conditions, removal efficiency and specific energy consumption is established, and the significance of the model is verified. A quadratic polynomial model is established to predict the experimental results, and the electro-oxidation process conditions are optimized.

[0005] A response surface optimization method for treating wastewater by electro-oxidation process comprises the following steps:

[0006] (1) Single factor experiment: The electro-oxidation test was carried out with current density, initial pH, electrode spacing and treatment time as independent variables. COD was determined using the national standard HJ 828-2017, and the COD removal rate was calculated according to the following formula. Each test was repeated three times and the average value was taken.

[0007] COD removal efficiency (RE%) = (influent COD - effluent COD) / influent COD × 100%;

[0008] (2) Response surface methodology experimental design: With current density, initial pH and treatment time as three factors, the appropriate level range was selected based on the results of the single factor experiment, and the experimental design was carried out using the Box-Behnken design method using the Design Expert 13 software;

[0009] (3) Variance analysis of the model: Use Design Expert 13 software to establish a multivariate quadratic regression equation based on the data in step (2), and perform variance analysis and significance analysis on the quadratic multivariate regression model equation;

[0010] (4) Response surface method optimization: The optimal process conditions were obtained based on the response surface diagram and Design Expert 13 software analysis.

[0011] Furthermore, the current density experimental range of the single factor experiment in step (1) is 0.01-0.5 A / cm 2 , the initial pH range is 1-14, the electrode spacing range is 0.5-10cm, and the treatment time range is 1-10h.

[0012] Furthermore, the response surface methodology in step (2) is used to design an experiment using A current density, B initial pH and C treatment time as independent factors and COD removal rate and specific energy consumption as response values.

[0013] Specific energy consumption = {[voltage (E) × current (I) × operating time (t) × 1000] / [(influent COD - effluent COD) wastewater volume (V)]}.

[0014] Furthermore, the variance analysis described in step (3) is to judge whether the model is statistically significant based on the model "F-value", to judge whether the model terms are significant based on the "P-values", to judge the applicability of the model based on the "Lack of Fit F-value", and to judge whether the model is statistically significant based on the "Adj.R 2 ” and “Pred.R 2 ” Determine the predictive value of the model.

[0015] Furthermore, the response surface method optimization described in step (4) is to select "Optimization" → "Numerical" in Design Expert 13 software. Set the target, current density "in range", initial pH "in range", treatment time "in range", COD removal rate "maximize", specific energy consumption "minimize". The predicted result "Desirability" greater than 0.85 is considered desirable.

[0016] The above technical solution has the following beneficial effects:

[0017] (1) Response surface methodology can optimize key parameters in the electrochemical oxidation process, such as current density, electrolyte concentration, initial pH value, operation time, etc., thereby improving the removal efficiency of pollutants.

[0018] (2) Response surface methodology Compared with Box-Behnken experimental design (BBD) and central composite experimental design (CCD), response surface methodology can obtain more information with fewer experimental times, saving time and resources.

[0019] (3) Response surface methodology can help determine the optimal process parameters for COD removal rate and energy consumption in the electrochemical oxidation process of wastewater, providing a reference for formulating efficient and energy-saving treatment schemes in practical engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a three-dimensional response surface diagram of the effects of current density, initial pH and treatment time on COD removal efficiency in the present invention.

[0021] Figure 2 This is a three-dimensional response surface diagram of the current density, initial pH and treatment time compared with the energy consumption of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be further described below in conjunction with the accompanying drawings and examples. It should be understood that these examples are only used to illustrate the present invention and do not have any limiting effect on the present invention.

[0023] Embodiment 1:

[0024] (1) Single factor experiment: The electro-oxidation test was carried out with current density, initial pH, electrode spacing and treatment time as independent variables. COD was determined using the national standard HJ 828-2017, and the COD removal rate was calculated according to the following formula. Each test was repeated three times and the average value was taken. The current density experimental range of the single factor experiment was 0.01-0.2 PA / cm 2 , the initial pH range is 2 to 12, the electrode spacing range is 0.5 to 5 cm, and the treatment time range is 1N10h.

[0025] COD removal efficiency (RE%) = (influent COD - effluent COD) / influent COD × 100%;

[0026] (2) Response surface methodology experimental design: Current density, initial pH, and treatment time were used as three-factor conditions, and the appropriate level range was selected based on the results of the single-factor experiment. The experimental design was carried out using the Design Expert 13 software according to the Box-Behnken design method.

[0027] Table 1. Experimental factors and level settings

[0028]

[0029] Table 2. Experimental design and results

[0030]

[0031] (3) Variance analysis of the model: Use Design Expert 13 software to establish a multivariate quadratic regression equation based on the data in step (2), and perform variance analysis and significance analysis on the quadratic multivariate regression model equation;

[0032] Quadratic multiple regression model equation:

[0033] COD removal rate (%) = 84.22 + 15.01A - 0.5729B + 11.52C - 0.3333AB - 4.56AC + 0.9792BC - 7.30A 2 -1.17B 2 -4.03C 2 Specific energy consumption (kWh / kg COD) = 53.99 + 28.99A + 0.3570B + 22.68C - 0.0653AB + 15.84AC - 0.3656BC + 6.23A 2 +0.3872B 2 +0.6868C 2 .

[0034] Table 3. ANOVA analysis of COD removal data

[0035]

[0036] From the variance analysis table (Table 3), we can see that the "F-value" is 183.84, indicating that the model is statistically significant, "P-values" less than 0.0500 indicate that the model items are significant, and the "Lack of Fit F-value" is 0.466, indicating that the lack of fit is not significant compared with the pure error, confirming the applicability of the model, and the "Adj.R 2 ” and “Pred.R 2 ” A difference less than 0.2 indicates that the model has predictive value.

[0037] Table 4. ANOVA analysis of specific energy consumption data

[0038]

[0039]

[0040] From the variance analysis table (Table 3), we can see that the "F-value" is 237.62, indicating that the model is statistically significant, "P-values" less than 0.0500 indicate that the model items are significant, "Lack of Fit F-value" is 9.07, which confirms the applicability of the model, and "Adj.R 2 ” and “Pred.R 2 ” A difference less than 0.2 indicates that the model has predictive value.

[0041] (4) Response surface method optimization: The optimal process conditions were obtained based on the response surface diagram and Design Expert 13 software analysis.

Claims

1. A response surface optimization method for treating wastewater by electro-oxidation process, comprising the following steps: (1) Single factor experiment: The electro-oxidation test was carried out with current density, initial pH, electrode spacing and treatment time as independent variables. COD was determined according to the national standard HJ 828-2017, and the COD removal rate was calculated as follows. Each test was repeated three times and the average value was taken; COD removal rate (RE%) = (influent COD - effluent COD) / influent COD × 100%; (2) Response surface methodology experimental design: With current density, initial pH and treatment time as three factors, the appropriate level range was selected based on the results of the single factor experiment, and the experimental design was carried out using the Box-Behnken design method using the Design Expert 13 software; (3) Variance analysis of the model: Use Design Expert 13 software to establish a multivariate quadratic regression equation based on the data in step (2), and perform variance analysis and significance analysis on the quadratic multivariate regression model equation; (4) Response surface method optimization: The optimal process conditions were obtained based on the response surface diagram and Design Expert 13 software analysis.

2. The response surface optimization method for treating wastewater by electro-oxidation process according to claim 1, characterized in that: The current density experimental range of the single factor experiment in step (1) above is 0.01-0.5A / cm 2 , the initial pH range is 1-14, the electrode spacing range is 0.5-10cm, and the treatment time range is 1-10h.

3. The response surface optimization method for treating wastewater by electro-oxidation process according to claim 1, characterized in that: The response surface method described in the above step (2) uses A current density, B initial pH and C treatment time as independent factors, and COD removal rate and specific energy consumption as response values ​​to carry out experimental design.

4. The response surface optimization method for treating wastewater by electro-oxidation process according to claim 1, characterized in that: The variance analysis described in step (3) above is to judge whether the model is statistically significant based on the model "F-value", to judge whether the model terms are significant based on the "P-values", to judge the applicability of the model based on the "Lack of Fit F-value", and to judge whether the model is statistically significant based on the "Adj.R 2 " and "Pred.R 2 ” Determine the predictive value of the model.

5. The response surface optimization method for treating wastewater by electro-oxidation process according to claim 1, characterized in that: The response surface method optimization described in step (4) above is to select "Optimization" → "Numerical" in Design Expert 13 software. Set the target, current density "in range", initial pH "in range", treatment time "in range", COD removal rate "maximize", specific energy consumption "minimize". The predicted result "Desirability" greater than 0.85 is considered desirable.

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