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/m 3 •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.

A petroleum refinery facility discharge wastewater with average influent COD concentration of approximately 500-750 mg/L during the period of this study. Study on the treatability of the petroleum refinery effluent wastewater was conducted using bench scale biological sequencing batch reactor systems. Six sequencing batch reactors (SBR) each of 2L liquid volume were operated at a 24 hours cycle. The SBRs were operated in various anaerobically stirred and aerobic modes. The average COD removals percentages for the aerobic reactor, combined anaerobic-aerobic reactors and aerobic mixed with domestic wastewater were found to be approximately, 91%, 91%, and 88% respectively, with its final average effluent COD of 63 mg/L, 65 mg/L, and 44 mg/L, respectively.

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The anoxic-aerobic wastewater treatment process increases wastewater treatment efficiency and decreases the aeration basin. In this study, raw data obtained from two anoxic-aerobic biological reactors (AABR) used for the treatment of different loads of petroleum refinery wastewater (PRW) were used for developing a mathematical model that could simulate the process trend. The data consists of 160 entries and was gathered over approximately 180 days from two AABR reactors that were continuously operated in parallel. Two configurations of artificial neural networks were compared and different numbers of neurons were tested for an optimum model that could represent the process behaviour under different loads. The tangent sigmoid transfer function (Tansig) at the hidden layer and a linear transfer function (Purelin) at the output layer with 9 hidden neurons were selected as the best optimum model. From the simulation model, the highest removal efficiency was observed as 96%, which was recorded for chemical oxygen demand (COD) influent concentration of 3150 mg/L. Effluent concentration below 100 mg/L was recorded for influent COD concentration, which ranged between 150 and 700 mg/L corresponding to the removal efficiency in the range of 78-88%.

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