Application of Response Surface Methodology and Artificial Neural Network for Analysis of p-chlorophenol Biosorption by Dried Activated Sludge

Abstract

Phenolic compounds are considered as priority pollutants because of their high toxicity at low concentration. In the present study, the sorption of p-chlorophenol (p-CP) by dried activated sludge was investigated. Activated sludge was collected as slurry from the sludge return line of a municipal wastewater treatment plant. Sorption experiments were carried out in batch mode. In order to investigate the effect of operating parameters on the removal efficiency of p-CP, four independent variables, including pH, initial concentration of p-CP, contact time and adsorbent dosage were studied. Artificial neural network (ANN) and response surface methodology (RSM) were developed for modeling of biosorption process. Results indicated that, Dried activated sludge can efficiently remove p-chlorophenol from aqueous solutions. The RSM method suggested that pH is the most significant parameter for biosorption process. Finally, RSM technique gave a function and neural network gave a structure for prediction. Neural network have a higher degree of accuracy and ANN predicted outputs were closer to the actual outputs of experiments in comparison with RSM technique.