Abstract
This study was aimed at evaluating the artificial neural network (ANN), genetic algorithm (GA), adaptive neurofuzzy interference (ANFIS), and the response surface methodology (RSM) approaches for modeling and optimizing the simultaneous adsorptive removal of chemical oxygen demand (COD) and total organic carbon (TOC) in produced water (PW) using tea waste biochar (TWBC). Comparative analysis of RSM, ANN, and ANFIS models showed mean square error (MSE) as 5.29809, 1.49937, and 0.24164 for adsorption of COD and MSE of 0.11726, 0.10241, and 0.08747 for prediction of TOC adsorption, respectively. The study showed that ANFIS outperformed the ANN and RSM in terms of fast convergence, minimum MSE, and sum of square error for prediction of adsorption data. The adsorption parameters were optimized using ANFIS-surface plots, ANN-GA hybrid, RSM-GA hybrid, and RSM optimization tool in design expert (DE) software. Maximum COD (88.9%) and TOC (98.8%) removal were predicted at pH of 7, a dosage of 300 mg/L, and contact time of 60 mins using ANFIS-surface plots. The optimization approaches showed the performance in the following order: ANFIS-surface plots>ANN-GA>RSM-GA>RSM.