Intelligent Algorithm-Based Modelling Wastewater Treatment

The experimental dataset used are of two bioreactor studies, Airlift bioreactor (ALR) and Inverse Fluidized Bed Bioreactor (IFBB). Influent selenite concentration (ICselenite) and hydraulic retention time (HRT) are predictor for both datasets. The Selenite removal efficiency (REselenite) (%) is the response in ALR, while REselenite (%) and chemical oxygen demand removal efficiency (RECOD) (%) in IFBB. The datasets were trained using three conventional (Levenberg-Marquardt, Bayesian regularization, scaled conjugate gradient) and three evolutionary (genetic algorithm, particle swarm, reptile search) algorithms. In this submission we have the compiled data, selecting architecture, and graphical representation representation.