Abstract
In this study, an enhanced coagulation-flocculant process incorporating magnetic powder was used to further treat the secondary effluent of domestic wastewater from a municipal wastewater treatment plant. The purpose of this work was to improve the discharged water quality to the surface water class IV standard of China. A novel approach using a combination of the response surface methodology and an artificial neural network (RSM-ANN) was used to optimize and predict the total phosphorus (TP) pollutant removal and turbidity. This work was first evaluated by RSM using the concentrations of coagulant, magnetic powder, and flocculant as the controllable operating variables to determine the optimal TP removal and turbidity. Next, an ANN model with a back-propagation algorithm was constructed from the RSM data along with the non-controllable variables, raw TP concentration, and raw water turbidity. Under the optimized experimental conditions (28.42 mg/L coagulant, 623 mg/L magnetic powder, and 0.18 mg/L flocculant), the TP and turbidity removal reached 88.79 ± 5.45% and 63.48 ± 9.60%, respectively, compared with 83.28% and 59.80%, predicted by the single RSM model, and 87.71 ± 5.74% and 64.62 ± 10.75%, predicted by the RSM-ANN model. The treated water were 0.17 ± 6.69% mg/L of TP and 2.46 ± 5.09% NTU of turbidity, respectively, which completely met the surface water class IV standard (TP < 0.3 mg/L; turbidity < 3 NTU). Therefore, this work demonstrated that the discharged water quality was completely improved using the magnetic coagulation process. In addition, the combined RSM-ANN approach could have potential application in municipal wastewater treatment plants.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
We thank Dr. Alan K Chang (Wenzhou University) for valuable discussion and for revising the language of the manuscript.
Funding
This work was supported by the National Key Research and Development Program of China (Grant No. 2018YFE0103700); the National Natural Science Foundation of China (Nos. 61901303 and 61871293); the Science and Technology Program of Cangnan, China (Grant No: 2018G29); and the Science and Technology Major Program of Wenzhou, China (Grant No: 2018ZG002).
national key research and development program of china,No. 2018YFE0103700,Qi Wang,National Natural Science Foundation of China,Nos. 61901303,Qi Wang,61871293,Qi Wang,science and technology program of Cangnan,China,No: 2018G29,Qi Wang,science and technology major program of wenzhou,China,No: 2018ZG002,Qi Wang
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KW established the models, analyzed and interpreted the experimental data, and was a major contributor in writing the manuscript. YM designed the experiments, performed the water quality measurements along with KW, and was a contributor on graphical processing. CW did the literature research. QK downloaded and installed the required software. MZ provided the vehicles for the experimental condition. QW came up with the idea, consulted the literatures, and contacted with the management of the multiple wastewater treatment plants. All authors read and approved the final manuscript.
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Wang, K., Mao, Y., Wang, C. et al. Application of a combined response surface methodology (RSM)-artificial neural network (ANN) for multiple target optimization and prediction in a magnetic coagulation process for secondary effluent from municipal wastewater treatment plants. Environ Sci Pollut Res 29, 36075–36087 (2022). https://doi.org/10.1007/s11356-021-18060-7
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DOI: https://doi.org/10.1007/s11356-021-18060-7