An Artificial Neural Network for Simulation of an Upflow Anaerobic Filter Wastewater Treatment Process

The purpose of this work was to develop a problem-solving approach and simulation tool that is useful for the specification wastewater treatment process equipment design parameters. proposition using an artificial neural network (ANN) numerical model supervised learning dataset then on new investigated. effectiveness assessed by evaluating capacity distinguish differences in To demonstrate approach, mock derived from experimentally acquired data physical effects reported literature. comprised influent flow rate, bed packing material dimension, type packed height-to-diameter ratio as predictors calorific value reduction. multilayer perceptron (MLP) ANN compared polynomial model. validation test results show MLP has four hidden layers, each having 256 units (nodes), accurately predicts When fed previously unseen data, root-mean-square error (RMSE) predicted responses 0.101 coefficient determination (R2) 0.66. all 125 possible combinations 3 mechanical parameters identical profiles were ranked according total A t-test difference between mean reduction two highest experiments showed means are significantly different (p-value = 0.011). Thus, Consequently, values three feature simulated experiment recommended use industrial scale upflow anaerobic filter (UAF) treatment.