Keywords: wastewater treatment, chemical oxygen demand, COD, dynamic score normalisation, Mahalanobis distance, spectral projected gradient algorithm, ANFIS, Runge–Kutta learning, feature selection, particle swarm optimisation, PSO, anaerobic filters, organic matter removal, cheese dairy wastewater, adaptive neuro–fuzzy inference systems, fuzzy logic, neural networks
An effective feature selection approach using hybrid particle swarm optimisation with spectral projected gradient algorithm for an up–flow anaerobic filter
Wastewater treatment plant (WWTP) helps to overcome water from excessive pollution. Chemical oxygen demand (COD) or biological oxygen demand (BOD) is the measure for the wastewater effluents. Up–flow anaerobic filter (UAF) is used for the removal and digestion of organic matter present in wastewater. In this work, the performance of the UAF with cheese–dairy wastewater was considered for modelling the ANN to predict the effluent COD level. The proposed method uses an effective dynamic score normalisation technique with Mahalanobis distance as a preprocessing step. The hybrid particle swarm optimisation (PSO) with spectral projected gradient (SPG2) algorithm selected the relevant features. Adaptive neuro–fuzzy inference system (ANFIS) with Runge–Kutta learning method (RKLM) is used for the prediction of COD effluent. Experiments are conducted on real datasets obtained from cheese–whey wastewater to predict the COD effluent. The experimental results proved that the proposed method achieves better accuracy and execution time. The average accuracy obtained in the proposed method is 92.94.