Modelling selenite and chemical oxygen demand removal in an inverse fluidized bed bioreactor using genetic algorithm optimized artificial neural network

Abstract

AbstractBACKGROUNDA genetic algorithm (GA) optimized artificial neural network (ANN) model was developed to simulate selenite removal efficiency (SRe) and chemical oxygen demand (COD) removal efficiency (CRe) in a single‐stage inverse fluidized bed reactor (IFBR) operated under different hydraulic retention time (HRT) (12–48 h), and inlet selenite concentration (12.7–635 mg L−1). Multi‐objective optimization for SRe and CRe with the ANN model was adopted to estimate the pareto optimal solutions in the bioreactor system.RESULTSGA optimized ANN topology suggested five neurons in the hidden layer, and the optimal data partitioning between training, test, and validation subsets (in 70:15:15 ratio) using cross‐validation. The average fitness for SRe and CRe of the resulting ANN model over training (R2 = 0.885), test (R2 = 0.967) and validation (R2 = 0.972) subsets were reasonably good. Sensitivity analysis suggested an increase in SRe and CRe with HRT, while an increase in inlet selenite concentration had a negative effect on SRe. Local interpretable model‐agnostic explanations and SHapley Additive exPlanations for the developed model suggested a positive coefficient of the predictors on CRe. Pareto optimal solution for SRe (92.87%–95.26%), and CRe (91.33%–99.35%) were suggested for HRT and inlet selenite concentration varying between 35.67 h and 36 h, and 0.1 g L−1 and 0.884 g L−1, respectively.CONCLUSIONSThe GA optimized ANN model could be exploited for the prediction of contaminant removal efficiencies in the bioreactor system with a fair degree of complexity (i.e., IFBR). Multiple objective pareto optimal solutions can identify the best operating conditions relevant for wastewater treatment. © 2023 Society of Chemical Industry (SCI).