  Citation: Irfan, M.; Waqas, S.; Arshad, U.; Khan, J.A.; Legutko, S.; Kruszelnicka, I.; Ginter-Kramarczyk, D.; Rahman, S.; Skrzypczak, A. Response Surface Methodology and Artificial Neural Network Modelling of Membrane Rotating Biological Contactors for Wastewater Treatment. Materials 2022, 15, 1932. https:// doi.org/10.3390/ma15051932 Academic Editor: Roberta G. Toro Received: 7 February 2022 Accepted: 1 March 2022 Published: 4 March 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). materials Article Response Surface Methodology and Artificial Neural Network Modelling of Membrane Rotating Biological Contactors for Wastewater Treatment Muhammad Irfan 1 , Sharjeel Waqas 2,3, * , Ushtar Arshad 2 , Javed Akbar Khan 4, *, Stanislaw Legutko 5 , Izabela Kruszelnicka 6 , Dobrochna Ginter-Kramarczyk 6 , Saifur Rahman 1 and Anna Skrzypczak 7 1 Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 11001, Saudi Arabia; miditta@nu.edu.sa (M.I.); srrahman@nu.edu.sa (S.R.) 2 Chemical Engineering Department, University Teknologi PETRONAS, Seri Iskandar 32610, Malaysia; ushtar_18003307@utp.edu.my 3 School of Chemical Engineering, The University of Faisalabad, Faisalabad 37610, Pakistan 4 Mechanical Engineering Department, University Teknologi PETRONAS, Seri Iskandar 32610, Malaysia 5 Faculty of Mechanical Engineering, Poznan University of Technology, 60-965 Poznan, Poland; stanislaw.legutko@put.poznan.pl 6 Department of Water Supply and Bioeconomy, Faculty of Environmental Engineering and Energy, Poznan University of Technology, 60-965 Poznan, Poland; izabela.kruszelnicka@put.poznan.pl (I.K.); dobrochna.ginter-kramarczyk@put.poznan.pl (D.G.-K.) 7 Health-Fire-Environmental Specialist AIGO-TEC Sp. z o.o., Gnie´ znie ´ nska 6, 62-330 Nekla, Poland; a.skrzypczak@aigo-tec.com * Correspondence: sharjeel_17000606@utp.edu.my (S.W.); javedakbar.khan@utp.edu.my (J.A.K.) Abstract: Membrane fouling is a major hindrance to widespread wastewater treatment applications. This study optimizes operating parameters in membrane rotating biological contactors (MRBC) for maximized membrane fouling through Response Surface Methodology (RSM) and an Artificial Neural Network (ANN). MRBC is an integrated system, embracing membrane filtration and conventional rotating biological contactor in one individual bioreactor. The filtration performance was optimized by exploiting the three parameters of disk rotational speed, membrane-to-disk gap, and organic loading rate. The results showed that both the RSM and ANN models were in good agreement with the experimental data and the modelled equation. The overall R 2 value was 0.9982 for the proposed network using ANN, higher than the RSM value (0.9762). The RSM model demonstrated the optimum operating parameter values of a 44 rpm disk rotational speed, a 1.07 membrane-to-disk gap, and a 10.2 g COD/m 2 d organic loading rate. The optimization of process parameters can eliminate unnecessary steps and automate steps in the process to save time, reduce errors and avoid duplicate work. This work demonstrates the effective use of statistical modeling to enhance MRBC system performance to obtain a sustainable and energy-efficient treatment process to prevent human health and the environment. Keywords: artificial neural networks (ANN); attached growth process; biofilm; response surface methodology (RSM); membrane fouling 1. Introduction Membrane fouling that can result in the rapid decline of membrane flux is a major bottleneck for limiting the wide application of various membrane technologies [1]. Var- ious methods to curtail membrane fouling are well developed, and many conventional and modern approaches to alleviate membrane fouling are in practice [2]. Conventional approaches focus on improving membrane properties, optimizing operational parameters, and tweaking the hydrodynamics near the membrane surface [3–6]. However, all these tech- niques result in high initial cost and high energy demand, thus limiting their widespread Materials 2022, 15, 1932. https://doi.org/10.3390/ma15051932 https://www.mdpi.com/journal/materials