Optimization and modeling of coagulation-flocculation to remove algae and organic matter from surface water by response surface methodology

Highlights

Charge neutralization and sweep flocculation were the major mechanisms.

Effect of process parameters was investigated.

Optimal coagulation conditions were studied by response surface methodology.

ANN models presented more robust and accurate prediction than RSM.

Abstract

Seasonal algal blooms of Lake Yangcheng highlight the necessity to develop an effective and optimal water treatment process to enhance the removal of algae and dissolved organic matter (DOM). In the present study, the coagulation performance for the removal of algae, turbidity, dissolved organic carbon (DOC) and ultraviolet absorbance at 254 nm (UV₂₅₄) was investigated systematically by central composite design (CCD) using response surface methodology (RSM). The regression models were developed to illustrate the relationships between coagulation performance and experimental variables. Analysis of variance (ANOVA) was performed to test the significance of the response surface models. It can be concluded that the major mechanisms of coagulation to remove algae and DOM were charge neutralization and sweep flocculation at a pH range of 4.66–6.34. The optimal coagulation conditions with coagulant dosage of 7.57 mg Al/L, pH of 5.42 and initial algal cell density of 3.83 × 10⁶ cell/mL led to removal of 96.76%, 97.64%, 40.23% and 30.12% in term of cell density, turbidity, DOC and UV₂₅₄ absorbance, respectively, which were in good agreement with the validation experimental results. A comparison between the modeling results derived through both ANOVA and artificial neural networks (ANN) based on experimental data showed a high correlation coefficient, which indicated that the models were significant and fitted well with experimental results. The results proposed a valuable reference for the treatment of algae-laden surface water in practical application by the optimal coagulation-flocculation process.

Graphical abstract

Keywords

Algae / Coagulation-flocculation / Response surface methodology / Artificial neural networks

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Acknowledgements

This work was financially supported by the Ontario-China Research and Innovation Fund (OCRIF) “Ensuring Water Supply Safety in Beijing: Water Diversion from South to North China” (No. 2015DFG71210). This work was also supported by grants from the Mega-projects of Science Research for Water Environment Improvement (Nos. 2012ZX07404-002, 2017ZX07108-003, 2017ZX07502003).

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