Method for optimizing electric flocculation wastewater treatment process by using response surface method

Abstract

The invention relates to a method for optimizing an electric flocculation wastewater treatment process by using a response surface method.The method comprises the following steps that an experimental subject in the electric flocculation process is selected for a single-factor test, proper factors are selected according to the influence of all factors on the COD removal rate when the factors change, and response surface method test design is carried out by using Design Expert; and establishing a quadratic multiple regression model equation, carrying out variance analysis, drawing a response surface of the regression equation and a contour map of the response surface, and obtaining a range of optimal process conditions. The electric flocculation wastewater treatment process is optimized on the basis of response surface method test design, the test times can be remarkably reduced, the suspended matter removal efficiency and the COD removal rate are improved, and the operation cost is reduced.

Description

Technical Field

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

[0002] Depending on the type of industry, the test wastewater contains a wide range of pollutants. Each industry generates typical wastewaters that require a specific and adapted treatment able to respect the environmental standards for discharge. Biological processes have long been used for the treatment of industrial wastewaters, but microorganisms are ineffective against recalcitrant and toxic compounds. In response to this problem, a number of physicochemical techniques, such as electrochemical treatment, make it possible to improve the purification of industrial wastewaters. Among the cost-effective methods for treating a wide range of wastewater pollutants, electroflocculation has proven to be effective in treating colloidal particles, heavy metals, orthophosphates, nitrates, fluorides, lead, etc.

[0003] Electrocoagulation mainly uses electric current to gradually dissolve the sacrificial anode during the reaction process, generating a large number of highly charged metal cations in situ. These metal cations act as coagulants and hydrolyze near the anode to form hydroxides M(OH) n , and then flocculate the suspended and colloidal pollutants. At the same time, water is reduced to hydrogen through the cathode reaction, and pollutants are effectively removed by precipitation and flotation during the electroflocculation process. The literature (Efficiency and condition optimization of electroflocculation method for treating leachate from garbage transfer stations, Environmental Protection and Circular Economy, 2024, 7: 16-19.) used electroflocculation to treat leachate from garbage transfer stations, and investigated the effects of anode materials, current density, plate spacing, reaction time, initial pH and electrolyte potassium persulfate on the COD removal rate of leachate. This paper mainly uses single-factor experiments for the optimization method of electroflocculation experiments, which makes it difficult to achieve global optimization of the electroflocculation process. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method for optimizing the electro-flocculation wastewater treatment process by using the response surface method, which is simple to operate and easy to industrialize.

[0005] A method for optimizing an electro-flocculation wastewater treatment process using a response surface methodology comprises the following steps:

[0006] (1) The experimental objects in the electro-flocculation process were selected for single factor experiments, and appropriate factors were selected according to the influence of each factor on the COD removal rate;

[0007] (2) Analyze and compare the changes in COD removal rate when the values ​​of various factors are different, determine the experimental factors and levels, and use Design Expert to generate a data table of experimental points for response surface optimization;

[0008] (3) Based on the COD removal rate at each test point, a quadratic multiple regression model equation was established and variance analysis was performed, and response surface fitting was performed on the test results obtained from each group of tests;

[0009] (4) Use the response surface of the regression equation and its contour map to obtain the range of optimal process conditions.

[0010] The experimental object in step (1) refers to the factor variables that have a greater impact on the COD removal rate, including but not limited to one or more of initial pH, treatment time, initial COD concentration, current density, electrode spacing, and electrolyte concentration.

[0011] The experimental design method in step (2) is to conduct an experimental design based on the Box-Behnken design method for the appropriate range of the key factors selected in step (1).

[0012] The variance analysis described in step (3) is to test whether the lack of fit is significant based on the p value of the lack of fit of the regression equation, determine whether the quadratic regression equation is appropriate, and determine whether the regression equation is significant based on the significance test. 2 and calibration R 2 , determine the predictive value of the model.

[0013] The above technical solution has the following beneficial effects:

[0014] (1) The response surface methodology was used to optimize the process parameters in a multi-factor coordinated manner, analyze the interactions among the various influencing factors, and further improve the removal efficiency of suspended matter and COD removal rate.

[0015] (2) Response surface optimization can reduce the energy required for wastewater treatment per unit volume, thereby reducing operating costs.

[0016] (3) Response surface methodology develops an interaction effect model between parameters to graphically display the relationship between various factors and response values, thereby achieving process optimization and obtaining the best operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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. DETAILED DESCRIPTION

[0018] The present invention is further described below in conjunction with the accompanying drawings and examples. It should be understood that the following examples will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form.

[0019] Embodiment 1:

[0020] (1) Single factor electrocoagulation test was carried out with current density, initial pH, treatment time, electrode spacing, and electrolyte concentration as variables, and COD was determined by the dichromate method.

[0021] (2) The changes in COD removal rate when the values ​​of each factor are different were analyzed and compared. The experimental factors were determined to be three factors and three levels: A current density, B initial pH, and C treatment time. The data table of the experimental points for response surface optimization was generated using Design Expert.

[0022] Table 1. Test point data table

[0023]

[0024]

[0025] (3) Taking the COD removal rate at each test point as the test result, a quadratic multivariate regression model equation was established and variance analysis was performed, and response surface fitting was performed on the test results obtained from each group of tests.

[0026] Quadratic multiple regression model equation:

[0027] COD removal rate (%) = 18.05-0.3750A-0.8558B-1.13C-2.77AB-0.5962AC+1.40BC-0.1603A 2 -0.6987B 2 -3.72C 2 .

[0028] Table 2. ANOVA analysis of COD removal data

[0029]

[0030] From the variance analysis table 2, we can see that the model is significant according to the F value. The smaller the P value, the more significant the variable is. 2 and calibration R 2 It is judged that the relationship between the independent variable and the response variable is significant, indicating that the model is appropriate.

[0031] (4) Use the response surface of the regression equation and its contour map to obtain the range of optimal process conditions.

Claims

1. A method for optimizing the electrocoagulation wastewater treatment process using response surface methodology, comprising the following steps: (1) The experimental objects in the electro-flocculation process were selected for single factor experiments, and appropriate factors were selected according to the influence of each factor on the COD removal rate; (2) Analyze and compare the changes in COD removal rate when the values ​​of various factors are different, determine the experimental factors and levels, and use Design Expert to generate a data table of experimental points for response surface optimization; (3) Based on the COD removal rate at each test point, a quadratic multiple regression model equation was established and variance analysis was performed, and response surface fitting was performed on the test results obtained from each group of tests; (4) Use the response surface of the regression equation and its contour plot to analyze the range of optimal process conditions.

2. According to claim 1, a method for optimizing the electro-flocculation process for treating wastewater by using response surface methodology, characterized in that: The experimental object in the above step (1) refers to the factor variables that have a greater impact on the COD removal rate, including but not limited to one or more of the initial pH, treatment time, initial COD concentration, current density, electrode spacing, and electrolyte concentration.

3. The method for optimizing the electro-flocculation wastewater treatment process using response surface methodology according to claim 1, characterized in that: The experimental design method described in the above step (2) is to conduct experimental design on the appropriate range of the key factors selected in step (1) based on the Box-Behnken design method.

4. The method for optimizing the electro-flocculation wastewater treatment process using response surface methodology according to claim 1, characterized in that: The variance analysis described in step (3) above is to test whether the lack of fit is significant based on the p value of the lack of fit of the regression equation, determine whether the quadratic regression equation is appropriate, and determine whether the regression equation is significant based on the significance test. 2 and calibration R 2 , determine the predictive value of the model.

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