Machine learning estimation of biodegradable organic matter concentrations in municipal wastewater.

This study investigates whether a novel estimation method based on machine learning can feasibly predict the readily biodegradable chemical oxygen demand (RB-COD) and slowly biodegradable COD (SB-COD) in municipal wastewater from the oxidation-reduction potential (ORP) data of anoxic batch experiments. Anoxic batch experiments were conducted with highly mixed liquor volatile suspended solids under different RB-COD and SB-COD conditions. As the RB-COD increased, the ORP breakpoint appeared earlier, and fermentation occurred in the interior of the activated sludge, even under anoxic conditions. Therefore, the ORP decline rates before and after the breakpoint were significantly correlated with the RB-COD and SB-COD, respectively (p < 0.05). The two biodegradable CODs were estimated separately using six machine learning models: an artificial neural network (ANN), support vector regression (SVR), an ANN-based AdaBoost, a SVR-based AdaBoost, decision tree, and random forest. Against the ORP dataset, the RB-COD and SB-COD estimation correlation coefficients of SVR-based AdaBoost were 0.96 and 0.88, respectively. To identify which ORP data are useful for estimations, the ORP decline rates before and after the breakpoint were separately input as datasets to the estimation methods. All six machine learning models successfully estimated the two biodegradable CODs simultaneously with accuracies of ≥0.80 from only ORP time-series data. Sensitivity analysis using the Shapley additive explanation method demonstrated that the ORP decline rates before and after the breakpoint obviously contributed to the estimation of RB-COD and SB-COD, respectively, indicating that acquiring the ORP data with various decline rates before and after the breakpoint improved the estimations of RB-COD and SB-COD, respectively. This novel estimation method for RB-COD and SB-COD can assist the rapid control of biological wastewater treatment when the biodegradable organic matter concentration dynamically changes in influent wastewater.