Investigation and optimization of anaerobic system for treatment of seafood processing wastewater

In recent years, expanding the seafood production has led to dramatic increases in the volume and pollution of wastewater produced by this industry. In this study, anaerobic treatment of seafood processing wastewater was investigated. Further, the performance of treatment process was optimized through maximizing the chemical oxygen demand (COD) removal (%). Several operative parameters such as inlet flow rate, hydraulic retention time (HRT), inlet COD, organic loading rate etc. affect the performance of treatment system. In this experiment, the effects of two parameters on treatment performance were investigated, including HRT and inlet COD. Further, multi-layer feed forward neural network (MLFFNN) and radial basis function (RBF) were applied to model the treatment process. To develop the neural network model, leave-one-out method was used. This robust model was used for optimization the performance of treatment process. The artificial intelligence system were capable of predicting COD removal (%) in different conditions. According to the optimum model, the optimized value of COD removal (%) is 57%.