Modeling applications in bioremediation of hydrocarbon pollutants

Abstract

This chapter addressed various statistical and modeling techniques that have been recently employed for studying the bioremediation of hydrocarbon pollutants. Isotherm adsorption models such as Temkin, Freundlich, Langmuir, and Dubinin-Radushkevich were used for describing the removal of hydrocarbon contaminants from aqueous phases. Statistical techniques, viz., regression analysis, quadratic model, and response surface methodology, were performed to demonstrate the effects of operational conditions on the remediation of water contaminated with hydrocarbon. Artificial intelligence including artificial neural network (ANN) and fuzzy inference system (FIS) was also presented as a black-box model for the prediction of hydrocarbon removal efficiencies. In addition, this chapter included literature studies that have implemented advanced modeling techniques within the field of hydrocarbon bioremediation.