Neural network modeling of sorption of pharmaceuticals in engineered floodplain filtration system

Abstract

Engineered floodplain filtration (EFF) system is a versatile low-cost water treatment process wherein water contaminants are removed, mainly by adsorption and–or degraded by microorganisms, as the infiltrating water moves from the wastewater treatment plants to any natural water stream. An artificial neural network (ANN) based on multilayer perceptrons with back propagation algorithm was used to approximate and interpret the complex input/output relationship, essentially to understand the breakthrough times in EFF system. Triclosan and ibuprofen were selected as the two model pollutants in this study owing to their environmental significance. The input parameters to the ANN model were inlet concentration (ppm) and flow rate (m/d), and the output parameters were six concentration-time pairs ( C, t). These C, t pairs were the times in the breakthrough profile, when 1%, 5%, 25%, 50%, 75% and 95% of the pollutant was present at the outlet of the system. The 10 set of experimental data points, 5 for each pollutant, statistically investigated in the continuous column studies using the full-factorial design, were divided into training (8 × 8) and testing (2 × 8) set. The most dependable condition for the network was selected by a trial and error approach and by estimating the determination coefficient ( R 2) value (>0.99) achieved during prediction of the testing set. The proposed ANN model for EFF system operation could be used as a potential alternative for knowledge-based models through proper training and testing of variables.