Pistachio Processing Wastewater treatment by Peroxi-Electrocoagulation Method and Artificial Neural Network Modelling

In this study, the peroxi-electrocoagulation process was investigated for the treatment of pistachio processing wastewater. The electrolysis cell was configured with 5 & times; 5 iron plate electrodes at a fixed 5 mm spacing. Electrolysis time of 90 min, 25 degrees C temperature, and 200 rpm stirring speed were maintained constant throughout the experiments. The effects of H(2)O(2 )dose (94-658 mM), initial pH (2.0-6.0), and current density (1-6 mA/cm(2)) on the removal of TOC, COD, and total phenol (TPh) were evaluated. Optimum operation conditions within the studied range were found as 564 mM H2O2, 3.0 initial pH, 3 mA/cm(2) current density, resulting in 88.9% TPh, 80.7% COD, and 65% TOC removal efficiencies. Kinetic study showed that the PEC process followed second-order kinetics, with R2 of 0.82, 0.82, and 0.80 for TPh, COD, and TOC, respectively. Reaction rate constants increased with increasing current density and H2O2 concentration, whereas reaction rates decreased at pH below and above 3.0. The highest reaction rates were observed at pH 3.0, 6 mA/cm(2) CD, and 658 mM H2O2. A single hidden-layer backpropagation network with a topology of 4:6:3 was developed following hidden layer neurons optimization to predict TOC, COD, and TPh concentrations. The final ANN model performance yielded RMSE of 150.4 +/- 31, 725.7.3 +/- 156.9, and 197.2 +/- 37.9, and R2 of 0.98, 0.97, and 0.95 for TPh, COD, and TOC, respectively. The high correlation between measured and predicted concentrations (R2; TPh:0.97, COD:0.97, TOC:0.92), confirmed the reliability of the model. Under selected conditions, the specific energy consumption and operating cost were calculated as 16.2 kWh/kg COD and 65.9 $/m(3), respectively.