Optimization of anaerobic treatment of petroleum refinery wastewater using artificial neural networks

Treatment of petroleum refinery wastewater using anaerobic treatment has many advantages over other biological method particularly when used to treat complex wastewater. In this study, accumulated data of Up-flow Anaerobic Sludge Blanket (UASB) reactor treating petroleum refinery wastewater under six different volumetric organic loads (0.58, 1.21, 0.89, 2.34, 1.47 and 4.14 kg COD/m3•d, respectively) were used for developing mathematical model that could simulate the process pattern. The data consist of 160 entries and were gathered over approximately 180 days from two UASB reactors that were continuously operating in parallel. Artificial neural network software was used to model the reactor behavior during different loads applied. Two transfer functions were compared and different number of neurons was tested to find the optimum model that predicts the reactor pattern. The tangent sigmoid transfer function (tansig) at hidden layer and a linear transfer function (purelin) at output layer with 12 neurons were selected as the optimum best model.