Modeling of oily sludge composting process by using artificial neural networks and differential evolution: Prediction of removal of petroleum hydrocarbons and organic carbon

Abstract

Abstract Since total petroleum hydrocarbons (TPH) and organic carbon (OC) are two important variables in the performance of oily sludge composting process; the prediction of their changes is of great importance to attain high removal efficiency. The main objective of this work was to model oily sludge composting process by using neuro-evolutive methodology based on artificial neural networks (ANNs) and differential evolution (DE) in order to predict TPH and OC removal in various conditions of the process. The experimental data on oily sludge composting were used to validate the model. So as to determine the optimal ANN model, a set of randomly generated models are initially generated and their parameters are evolved by the DE until a stop criterion is reached. It was found that TPH and OC were modeled well and the ANN model can provide predictions which were in accordance with the experimental data. The obtained results can be used to lessen the costs of full-scale bioremediation through eliminating the need for further experiments.