Prediction of performance of the moving-bed biofilm pilot reactor using back-propagation artificial neural network (BP-ANN)

Abstract

Coal gasification stripped gas liquor (CGSGL) wastewater contains large quantities of complex organic and inorganic pollutants which include phenols, ammonia, hydantoins, furans, indoles, pyridines, phthalates and other monocyclic and polycyclic nitrogen containing aromatics, oxygen- and sulphur containing heterocyclic compounds. Most conventional aerobic systems for coal gasification wastewater treatment are not sufficient in reducing pollutants such as chemical oxygen demand (COD), phenols and ammonia due to the presence of toxic and inhibitory organic compounds. The current paper reports on the degradation of aromatic compounds and the reduction of hard COD in CGSGL using a Moving-Bed Biofilm Reactor (MBBR) system. The inoculum contained a genetically enhanced mixed culture of Pseudomonas putida, Pseudomonas plecoglossicida, Rhodococcus erythropolis, Rhodococcus qingshengii, Enterobacter cloacae, Enterobacter asburiae strains of bacteria, seaweed and diatoma. Consistently high hard COD removal (>88 %) and degradation of targeted phenolic compounds (>93 %) was achieved in the reactor with no loss of biodiversity in the consortium culture. The performance of the reactor outside the observable range was projected using a Back-Propagation Artificial Neural Network (BP-ANN) developed in this study.