Research Article Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption Process Mohamed K. Mostafa , 1 Ahmed S. Mahmoud , 2 Mohamed S. Mahmoud , 3 and Mahmoud Nasr 4,5 1 Faculty of Engineering and Technology, Badr University in Cairo (BUC), Cairo, Egypt 2 Scientific Research Development Unit, Egyptian Russian University (ERU), Badr, Egypt 3 Sanitary and Environmental Engineering Institute (SEI), Housing and Building National Research Center (HBRC), Egypt 4 Environmental Engineering Department, Egypt-Japan University of Science and Technology (E-JUST), New Borg El-Arab City Alexandria 21934, Egypt 5 Sanitary Engineering Department, Faculty of Engineering, Alexandria University, P.O. Box 21544, Alexandria 21526, Egypt Correspondence should be addressed to Mahmoud Nasr; mahmoud.nasr@ejust.edu.eg Received 8 March 2022; Revised 22 April 2022; Accepted 27 April 2022; Published 10 May 2022 Academic Editor: Stefano Salvestrini Copyright © 2022 Mohamed K. Mostafa 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. Predicting the adsorption performance to remove organic pollutants from wastewater is an essential environmental-related topic, requiring knowledge of various statistical tools and artificial intelligence techniques. Hence, this study is the first to develop a quadratic regression model and artificial neural network (ANN) for predicting biochemical oxygen demand (BOD) removal under different adsorption conditions. Nanozero-valent iron encapsulated into cellulose acetate (CA/nZVI) was synthesized, characterized by XRD, SEM, and EDS, and used as an efficient adsorbent for BOD reduction. Results indicated that the medium pH and adsorption time should be adjusted around 7 and 30 min, respectively, to maintain the highest BOD removal efficiency of 96.4% at initial BOD concentration ðC o Þ = 100 mg/L, mixing rate = 200 rpm, and adsorbent dosage of 3 g/L. An optimized ANN structure of 5–10–1, with the “trainlm” back-propagation learning algorithm, achieved the highest predictive performance for BOD removal (R 2 : 0.972, Adj-R 2 : 0.971, RMSE: 1.449, and SSE: 56.680). Based on the ANN sensitivity analysis, the relative importance of the adsorption factors could be arranged as pH > adsorbent dosage > time ≈ stirring speed > C o . A quadratic regression model was developed to visualize the impacts of adsorption factors on the BOD removal efficiency, optimizing pH at 7.3 and time at 46.2 min. The accuracy of the quadratic regression and ANN models in predicting BOD removal was approximately comparable. Hence, these computational-based methods could further maximize the performance of CA/nZVI material for removing BOD from wastewater under different adsorption conditions. The applicability of these modeling techniques would guide the stakeholders and industrial sector to overcome the nonlinearity and complexity issues related to the adsorption process. 1. Introduction Recently, adsorption has been employed in several types of research as an efficient and reliable process for wastewater treat- ment [1–3]. The adsorption systems neither consume a lot of electricity nor generate large amounts of sludge [4, 5]. More- over, the adsorbent material could be appropriately synthesized to provide effective adsorption sites to capture the pollutants from wastewater [6, 7]. However, the adsorption process is highly influenced by several operational factors such as time, pH, and mixing speed [8]. The correlation between these envi- ronmental factors and pollutant removal efficiency could be described by nonlinear and complex modeling methods [9]. Hence, more studies are required to investigate the applicability of various statistical tools and artificial intelligence techniques for predicting adsorption performance. Hindawi Adsorption Science & Technology Volume 2022, Article ID 9739915, 15 pages https://doi.org/10.1155/2022/9739915