Fe recovery from drinking water treatment sludge for reuse in tannery wastewater treatment: Machine learning and statistical modelling

Abstract

This work experimentally and numerically studied the reusability of Fe recovered from water treatment sludge as a coagulant and compared its performance with a commercial coagulant in tannery wastewater treatment. The performance of the recycled and commercial coagulants in total suspended solid (TSS), chemical oxygen demand (COD), color, chromium (Cr3+) and turbidity removal was compared with considering pH, coagulant type and dose as independent variables. Additionally, the process was modelled by two statistical and machine learning procedures, i.e., response surface methodology (RSM) and artificial neural network (ANN). For RSM model, a 32 run experimental matrix was designed by factorial design, while ANN model was developed by a dataset extracted from the publications inclusive of the 32 experiments. The results showed Fe recovery rate of 181 mg/g dry sludge. The maximum TSS, COD, color, Cr3+ and turbidity removal by recovered and commercial coagulants were (85 %, 40 %, 42 %, 68 %, and 88 %) and (70 %, 35 %, 49 %, 64 % and 88 %) respectively. ANN COD removal model showed a good prediction in all training, validation and testing phases with R 0.891, 0.963 and 0.917 respectively. While the R for TSS, COD, color, Cr3+ and turbidity statistical models were 0.962, 0.866, 0.960, 0.962 and 0.973 respectively.