Research Article Evaluation of Contemporary Computational Techniques to Optimize Adsorption Process for Simultaneous Removal of COD and TOC in Wastewater Areej Alhothali , 1 Hifsa Khurshid , 2 Muhammad Raza Ul Mustafa , 2,3 Kawthar Mostafa Moria, 1 Umer Rashid , 4 and Omaimah Omar Bamasag 5 1 Department of Computer Sciences, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia 2 Department of Civil & Environmental Engineering, Universiti Teknologi PETRONAS 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia 3 Centre for Urban Resource Sustainability, Institute of Self-Sustainable Building, Universiti Teknologi PETRONAS, Seri Iskandar, 32610 Perak, Malaysia 4 Institute of Nanoscience and Nanotechnology (ION2), Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia 5 Center of Excellence in Smart Environment Research, King Abdulaziz University, Jeddah, Saudi Arabia Correspondence should be addressed to Hifsa Khurshid; hifsa_18002187@utp.edu.my Received 31 October 2021; Revised 3 January 2022; Accepted 30 March 2022; Published 27 April 2022 Academic Editor: George Kyzas Copyright © 2022 Areej Alhothali et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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. 1. Introduction With an increase in the world population, industrialization, and urbanization, the evaluation of water resources and monitoring of their quality have become a significant con- cern in hydroenvironmental science. Various contaminants are being released continuously into water resources and causing the degradation of aquatic animals’ habitat and freshwater quality up to a greater extent [1, 2]. Attempts have been made to establish strategies for the safe removal of contaminants in wastewaters, e.g., coagulation-floccula- tion, photocatalytic treatment, electrocoagulation, adsorp- tion, and oxidation [3, 4]. However, in comparison to other methods, adsorption has gained prominence due to its high operating speed, design stability, cost-effectiveness, and robustness [5, 6]. The adsorption process is influenced by various operat- ing variables, including contact time between adsorbent and adsorbate, adsorbent particle size, pollutant concentra- tion, and pH of the solution. It has been noted that building an automated and optimized adsorption treatment process is complex in wastewater treatment plants (WWTP) due to the Hindawi Adsorption Science & Technology Volume 2022, Article ID 7874826, 16 pages https://doi.org/10.1155/2022/7874826