International Journal of Engineering and Technology

Wastewaters are waterborne solids and liquids which get discharged into sewers representing the wastes of public life. Wastewater Treatment is the process of removing contaminants from both runoff and domestic wastewater. Chemical Oxygen Demand (COD) is used to measure the degradable organic matter confined in wastewater effluents. An anaerobic process becomes popular method for the treatment of a number of waste streams. Up-flow Anaerobic Filter (UAF) is an anaerobic reactor which is used for the removal and digestion of organic matter present in wastewater. A systematic prediction of agro-food wastewater using Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed to predict COD effluent of a full-scale anaerobic wastewater treatment plant in accordance with UAF. Wastewater Treatment process initiates with Data collection and pre-processing preceded by normalization technique using Z-Score, which is followed by Feature selection algorithm and the prediction of COD is concluded using ANFIS by the model when applied to the description of UAF treating agro-food industrial wastewaters such as fruit juice wastewater. Prediction of agro-food wastewater can be improved using various feature selection algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Particle Swarm Optimization (PSO) With Spectral Projected Gradient (SPG2) algorithm. The experimental results proved that Combination of ANFIS with PSO-SPG2 is considered as a best fit to predict the COD effluent level with greater accuracy and with low Mean Square Error values.