An intelligent optimization method to improve the efficiency of D-amino acid in mitigating MBR membrane fouling

Abstract

From the perspective of mathematical models, the present invention uses a method combining multiple linear regression analysis (MLR) and response surface optimization (RSM), and realizes the mitigation of MBR membrane pollution and promotes the further application of DAA in MBR systems by applying D-amino acids (DAA) to the intelligent regulation and optimization prediction of operating condition parameters in the MBR sewage treatment process, which can provide a new and effective control strategy for solving the problem of MBR membrane biofouling. The present invention performs fitting optimization analysis on the relationship between operating conditions and membrane fouling rate in DAA-MBR, quantitatively evaluates the degree of influence of each operating condition on the membrane fouling rate, and determines the optimal combination process parameters of the operating conditions. At the same time, the numerical values ​​and trends of DAA-MBR membrane fouling rates under operating conditions of various levels within and outside the test range are effectively predicted. The present invention can not only achieve the mitigation of DAA-MBR membrane pollution through intelligent regulation and optimization of operating conditions, and promote the application of DAA in MBR, but also has simple calculation and strong practicality, and can provide guidance for process optimization in membrane biofouling control.

Description

Technical Field

[0001] The invention belongs to the field of sewage treatment, and specifically relates to an intelligent optimization method for improving the efficiency of D-amino acid in mitigating membrane fouling in an MBR water treatment process, that is, a method combining multivariate linear regression analysis (MLR) with response surface optimization (RSM), through intelligent regulation and optimization prediction of operating condition parameters in the MBR sewage treatment process using D-amino acid, to achieve mitigation of MBR membrane fouling and promote further application of D-amino acid in the MBR system. Background Art

[0002] Membrane bioreactor (MBR) uses membrane separation devices to replace traditional secondary sedimentation tanks to achieve effective separation of solids and liquids. It is regarded as one of the new sewage treatment processes with the greatest development potential and application prospects. However, membrane fouling is a bottleneck problem that restricts the further industrial application of MBR. How to mitigate the MBR membrane fouling problem is currently a hot topic of research. Membrane fouling in MBR is mainly due to the spontaneous aggregation of microorganisms on the membrane surface and in the membrane pores to form biofilms, which cause irreversible blockage of the membrane and a continuous decrease in membrane flux. Once formed, biofilms are difficult to eradicate. Traditional methods based on physical and chemical principles to mitigate membrane biofouling, such as membrane surface modification, optimization of operating conditions, physical and chemical cleaning, etc., have the disadvantages of low efficiency, energy consumption, and harm to microorganisms. Recently, D-amino acids (DAA) have attracted widespread attention as a new, environmentally friendly, non-bactericidal bioinhibitor.

[0003] However, the feasibility of DAA in controlling MBR membrane biofouling is still unknown. Therefore, a new type of DAA-MBR system was established by simply adding DAA to the MBR system, in order to provide a feasible way for DAA to effectively mitigate MBR membrane biofouling. In addition, in the process of using DAA to control MBR membrane biofouling, operating conditions are the key factors affecting membrane biofouling performance. Therefore, studying operating conditions is of great significance to improving the potential applicability of DAA in MBR systems and mitigating membrane biofouling.

[0004] At present, optimizing experimental conditions through mathematical models to promote the development of research has attracted great attention. Due to the lengthy experimental cycle, complex experimental process and high difficulty; in addition, the exploration of different experimental conditions will increase the cost of experimental operation and even limit the understanding of the scientific laws of the DAA-MBR membrane biofouling being studied.

[0005] In summary, the purpose of the present invention is to use a method combining multiple linear regression analysis (MLR) and response surface optimization (RSM) from the perspective of mathematical model, through the intelligent regulation and optimization prediction of operating condition parameters in the MBR sewage treatment process using D-amino acids, to achieve the mitigation of MBR membrane pollution and promote the further application of D-amino acids in the MBR system, which can provide a new and effective control strategy for solving the problem of MBR membrane biological fouling. Summary of the invention

[0006] The purpose of the present invention is to address the problems existing in the operation optimization process of using DAA to inhibit MBR membrane biofouling, and to propose a new method that combines MLR with RSM to achieve intelligent regulation and optimization prediction of operating condition parameters in the DAA-MBR system sewage treatment process.

[0007] That is, by using MLR to fit the linear equation to simulate the relationship between the prediction variable (membrane fouling rate of the DAA-MBR system) and the response variable (operating conditions), the influence of a single operating condition on the membrane fouling rate is quantitatively evaluated, and the main control factor is screened; in order to further optimize the operating parameters, based on the MLR single-factor experiment, the Box-Behnken principle in the response surface analysis is used to design a three-factor two-level experiment, with the DAA-MBR system operating conditions as the independent variable and the membrane fouling rate as the response value, to further investigate the interaction of the operating conditions, explore the optimal operating conditions and process parameters, and optimize to achieve the lowest membrane fouling rate. In addition, MLR and RSM can respectively realize the prediction of the DAA-MBR membrane fouling rate under the single-factor operating conditions and the interactive combination of multiple-factor operating conditions, provide useful results with relatively less data, realize the intelligent regulation and optimization prediction of the operating conditions in the DAA-MBR system, and further slow down the membrane biofouling of the DAA-MBR system and improve the application of DAA in MBR.

[0008] The present invention achieves its purpose through the following technical solutions:

[0009] (1) MLR univariate interaction analysis

[0010] The MLR model can simulate the nature of the relationship between variables (independent variables) and response variables (independent variables) by fitting linear equations. The coefficient value can help use a suitable model to estimate the regression equation of linearly related independent variables and give an accurate prediction of the dependent variable. The correlation coefficient analysis can quantitatively evaluate the influence of a single influencing factor on the response variable and screen the main controlling factor. In this way, MLR can be used to determine the influence of various operating condition parameters on the MBR membrane fouling rate and provide guidance for the application of DAA in MBR to mitigate membrane biofouling. The MLR formula in this study is expressed as follows:

[0011]

[0012] in, represents the observed dependent variable (membrane fouling rate of DAA-MBR); represents the independent variable to be observed (operant condition); is the regression coefficient in the MLR model, is the random error in observing the independent variable.

[0013] The steps involved in developing an MLR model are as follows:

[0014] Step 1: Establish the MLR model of DAA-MBR conceptual operating conditions-membrane fouling rate, such as Figure 2 As shown;

[0015] Step 2: Collect data based on the variables included in the model, that is, design an experimental plan and conduct repeated experiments to obtain the membrane fouling rate of the DAA-MBR system related to its relevant operating conditions;

[0016] Step 3: Process the collected data and import them into SPSS software to perform multiple linear stepwise regression, and test the significance and multiple correlation coefficient at the same time to establish the optimal equation reflecting the relationship between the independent variable and the dependent variable;

[0017] Step 4: The numerical model can be used to quantitatively evaluate the impact of a single operating condition on DAA-MBR membrane fouling, screen the main controlling factors, and determine the interaction between various operating conditions.

[0018] (2) RSM multi-factor combination optimization

[0019] The MLR model reflects the relationship and interaction between various operating conditions and the DAA-MBR membrane fouling rate. In order to further integrate various situations to find the optimal operating condition combination that makes the DAA-MBR membrane fouling rate lower, a method based on the Box-Behnken response surface method to optimize the operating conditions to slow down the DAA-MBR membrane fouling rate and improve the application of DAA in MBR is proposed. By establishing a continuous variable curve model, the factor levels and their interactions that affect the response value are optimized, so that the optimal conditions of the multi-factor system can be quickly and effectively determined.

[0020] The present invention takes the membrane fouling rate of DAA-MBR as the response value, selects three operating conditions that have a greater impact on the fouling rate, and designs a three-factor two-level response surface analysis experiment based on the single factor experiment, thereby optimizing the optimal operating combination process conditions of the DAA-MBR system.

[0021] The steps involved in RSM multi-factor system combination optimization are as follows:

[0022] Step 1: Based on the MLR model in (1), the operating conditions that affect the membrane fouling rate in the DAA-MBR system are obtained, and the parameter range of each operating condition is screened;

[0023] Step 2: Based on the parameter range of each operating condition in step 1, combined with the response surface methodology, taking the operating conditions as the investigation object and the DAA-MBR membrane fouling rate as the evaluation index, a three-factor two-level test table was designed;

[0024] Step 3: Based on the three-factor two-level test table obtained in step 2, use Design Expert 8.0 software to perform Box-Behnken response surface optimization design.

[0025] Step 4: Based on the optimized operating condition combination process, the best operating condition combination that affects the DAA-MBR membrane fouling rate can be obtained to further verify the experimental conclusions and guide future operating process design.

[0026] (3) Model combination optimization prediction

[0027] Based on the RSM response surface method, the complex and unknown functional relationship is fitted with a polynomial in a small area within the test range. At the same time, the obtained model is continuous, and in the process of optimizing the test conditions, each level within the test range can be continuously predicted.

[0028] The prediction method based on MLR model analysis is to predict the membrane fouling rate change trend corresponding to a single influencing factor under different conditions through a multivariate linear regression equation, and quantitatively predict the membrane fouling rate outside the test range with specific operating conditions.

[0029] In summary, the present invention proposes an intelligent optimization method for improving the efficiency of D-amino acid in mitigating MBR membrane fouling, which has the following advantages:

[0030] (1) The combined model takes into account the random errors of the experiment, performs fitting optimization analysis on the relationship between the operating conditions and the membrane fouling rate in the DAA-MBR, quantitatively evaluates the influence of each operating condition on the membrane fouling rate, and determines the optimal combination of process parameters of the operating conditions.

[0031] (2) The combined model effectively predicted the values ​​and trends of the DAA-MBR membrane fouling rate at various operating conditions within and outside the experimental range.

[0032] (3) This method can not only reduce DAA-MBR membrane fouling through intelligent regulation and optimization of operating conditions, thus promoting the application of DAA in MBR, but also has simple calculation and strong practicality, thus providing a new and effective control strategy for solving the problem of MBR membrane biofouling. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Schematic diagram of the mechanism of the intelligent optimization method to improve the effectiveness of D-amino acids in mitigating MBR membrane fouling.

[0034] Figure 2 This is the conceptual model diagram of MLR.

[0035] Figure 3 This is a trend chart predicted by the MLR model.

[0036] Figure 4 This is a trend chart predicted by the RSM model. DETAILED DESCRIPTION

[0037] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. It should be noted that this is a further description of the present invention, rather than a limitation of the present invention.

[0038] Embodiment 1:

[0039] In this example, the transmembrane pressure (TMP) measured by the experiment was selected as the evaluation index after data processing to obtain data that can reflect the membrane fouling rate, and the operating conditions were used as the influencing factors. SPSS 22.0 software was used to perform a multivariate linear regression analysis on the two. The specific classification and values ​​are as follows:

[0040] Table 1 Parameter range and classification table of evaluation indicators and influencing factors

[0041]

[0042] Among them, membrane characteristics, DAA addition time and DAA control mode are three factors that affect the membrane fouling rate, and their corresponding parameter ranges are -1 and 1. Among them, -1 corresponding to membrane characteristics represents hydrophilic membrane components, and 1 represents hydrophobic membrane components; in DAA addition time, -1 represents addition before operation, that is, DAA reacts with microorganisms for 4 hours before inoculating sludge into MBR, and 1 represents addition after operation, that is, DAA is added when TMP reaches 15kPa; in DAA control mode, -1 represents DAA as a cleaning agent, and after the membrane component is polluted, the membrane component is soaked in an aqueous solution of D-amino acids for 4 hours, and 1 represents DAA as an additive, added to MBR for membrane fouling control.

[0043] Through data collation and analysis, the statistical table of membrane fouling rate under different operating conditions is as follows:

[0044] Table 2 Numerical statistics of membrane fouling rate under different operating conditions

[0045]

[0046] After processing, the above data were introduced into SPSS 22.0 software for multivariate linear stepwise regression analysis, and the optimal equation between the reaction operating conditions and the membrane fouling rate was obtained as follows:

[0047] y=1.281+0.089-0.287x3 2 -0.126x1x2+0.088x1x3+0.049x2x3

[0048] Among them, the entire multiple correlation coefficient R 2 The Pearson correlation coefficients of membrane characteristics, DAA addition time, and DAA control mode were 0.352, 0.349, and -0.721, respectively, which proved that the contribution rate of the three factors to the membrane fouling rate was large, especially the DAA control mode.

[0049] It can be obtained from the above MLR model that membrane characteristics, DAA addition time and DAA control mode all have a significant impact on the membrane fouling rate. Therefore, these three influencing factors are taken as the objects of investigation, and the membrane fouling rate is used as the evaluation index to use the RSM optimization method to explore the optimal combination of operating conditions, namely, membrane characteristics: hydrophobic membrane; DAA addition time: addition before operation; DAA control mode: additive.

[0050] Among them, the three-factor two-level test table is shown in Table 2. The Box-Behnken response surface method is used to optimize the optimal operating condition combination:

[0051]

[0052] After the above analysis, the combined model effectively predicted the values ​​and trends of the DAA-MBR membrane fouling rate under various operating conditions within and outside the test range, such as Figure 3 and Figure 4 .

Claims

1. An intelligent optimization method for improving the efficiency of D-amino acid in mitigating MBR membrane fouling, characterized in that: From the perspective of mathematical model, the method of combining multivariate linear regression analysis (MLR) with response surface optimization (RSM) is used to intelligently control and optimize the operating condition parameters of D-amino acid (DAA) in the MBR wastewater treatment process, so as to slow down the MBR membrane fouling rate and promote the further application of DAA in the MBR system; this is achieved in the following ways: (1) MLR univariate interaction analysis The MLR model simulates the nature of the relationship between variables and response variables by fitting linear equations; the coefficient value can help use a suitable model to estimate the regression equation of linearly related independent variables and give an accurate prediction of the dependent variable. The correlation coefficient analysis can quantitatively evaluate the influence of a single influencing factor on the response variable and screen the main controlling factor; in this way, MLR can be used to determine the influence of various operating condition parameters on the MBR membrane fouling rate and provide guidance for the application of DAA in MBR to mitigate membrane biofouling; the MLR formula is as follows: in, represents the observed dependent variable, i.e., the membrane fouling rate of DAA-MBR; represents the independent variable to be observed, i.e., the operating condition; is the regression coefficient in the MLR model, is the random error of the observed independent variable; The steps involved in developing an MLR model are as follows: Step 1: Establish the MLR model of DAA-MBR conceptual operating conditions-membrane fouling rate; Step 2: Collect data based on the variables included in the model, that is, design an experimental plan and conduct repeated experiments to obtain the membrane fouling rate of the DAA-MBR system related to its relevant operating conditions; Step 3: Process the collected data and import them into SPSS software to perform multiple linear stepwise regression, and test the significance and multiple correlation coefficients to establish the optimal equation reflecting the relationship between the independent variable and the dependent variable; Step 4: The numerical model can be used to quantitatively evaluate the influence of a single operating condition on DAA-MBR membrane fouling, screen the main controlling factors, and determine the interaction between various operating conditions; (2) RSM multi-factor combination optimization The MLR model reflects the relationship and interaction between various operating conditions and the DAA-MBR membrane fouling rate. In order to further integrate various situations to find the best operating condition combination that can reduce the DAA-MBR membrane fouling rate, the Box-Behnken response surface method is used to optimize the operating conditions to slow down the DAA-MBR membrane fouling rate and improve the application of DAA in MBR. By establishing a continuous variable curve model, the factor levels and their interactions that affect the response value are optimized to determine the optimal conditions of the multi-factor system. The steps involved in RSM multi-factor system combination optimization are as follows: Step 1: Based on the MLR model in (1), the operating conditions that affect the membrane fouling rate in the DAA-MBR system are obtained, and the parameter range of each operating condition is screened; Step 2: Based on the parameter range of each operating condition in step 1, combined with the response surface methodology, taking the operating conditions as the investigation object and the DAA-MBR membrane fouling rate as the evaluation index, a three-factor two-level test table was designed; Step 3: Based on the three-factor two-level test table obtained in step 2, use Design Expert 8.0 software to perform Box-Behnken response surface optimization design; Step 4: Based on the optimized operating condition combination process, the best operating condition combination that affects the DAA-MBR membrane fouling rate is obtained to further verify the test conclusions and guide future operating process design; (3) Model combination optimization prediction Based on the RSM response surface method, the prediction is optimized and the complex and unknown functional relationship is fitted with a polynomial in a small area within the test range. At the same time, the obtained model is continuous and can continuously predict each level within the test range during the optimization process of the test conditions.

2. According to claim 1, an intelligent optimization method for improving the efficiency of D-amino acid in mitigating MBR membrane pollution is characterized in that: The operating conditions for applying DAA in the MBR sewage treatment process are membrane characteristics, DAA addition time, and DAA control mode.

3. An intelligent optimization method for improving the efficiency of D-amino acid in mitigating MBR membrane pollution according to claim 2, characterized in that: The parameter ranges of the operating conditions are -1 and 1, respectively; wherein -1 corresponding to the membrane characteristics represents a hydrophilic membrane component, and 1 represents a hydrophobic membrane component; in the DAA addition time, -1 represents addition before operation, i.e. DAA first reacts with microorganisms for 4 hours before inoculating the sludge into the MBR, and 1 represents addition after operation, i.e. DAA is added when the transmembrane pressure difference (TMP) reaches 15 kPa; in the DAA control mode, -1 represents DAA as a cleaning agent, and after the membrane component is contaminated, the membrane component is soaked in an aqueous solution of D-amino acids for 4 hours, and 1 represents DAA as an additive added to the MBR for membrane contamination control.

4. The intelligent optimization method for improving the efficiency of D-amino acid in mitigating MBR membrane pollution according to claim 1, characterized in that: The membrane fouling rate is the data reflecting the membrane fouling rate obtained by calculating the transmembrane pressure (TMP) measured experimentally.

5. The intelligent optimization method for improving the efficiency of D-amino acid in mitigating MBR membrane pollution according to claim 1, characterized in that: The RSM model takes the membrane fouling rate of DAA-MBR as the response value, selects three operating conditions that have a greater impact on the fouling rate according to the MLR model, and designs a three-factor two-level response surface analysis experiment based on a single factor experiment.

6. The intelligent optimization method for improving the efficiency of D-amino acid in mitigating MBR membrane pollution according to claim 1, characterized in that: The combination of MLR and RSM can effectively predict the value and trend of the DAA-MBR membrane fouling rate under various operating conditions within and outside the test range.

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