Citation: Frontistis, Z.; Lykogiannis, G.; Sarmpanis, A. Artificial Neural Networks in Membrane Bioreactors: A Comprehensive Review— Overcoming Challenges and Future Perspectives. Sci 2023, 5, 31. https:// doi.org/10.3390/sci5030031 Received: 7 June 2023 Revised: 7 August 2023 Accepted: 11 August 2023 Published: 15 August 2023 Copyright: © 2023 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/). Review Artificial Neural Networks in Membrane Bioreactors: A Comprehensive Review—Overcoming Challenges and Future Perspectives Zacharias Frontistis 1, * , Grigoris Lykogiannis 2 and Anastasios Sarmpanis 2 1 Department of Chemical Engineering, University of Western Macedonia, 50132 Kozani, Greece 2 ECOTECH LTD., 245 Syngros Ave., 17122 Athens, Greece * Correspondence: zfrontistis@uowm.gr; Tel.: +30-24-6105-6659 Abstract: Among different biological methods used for advanced wastewater treatment, membrane bioreactors have demonstrated superior efficiency due to their hybrid nature, combining biological and physical processes. However, their efficient operation and control remain challenging due to their complexity. This comprehensive review summarizes the potential of artificial neural networks (ANNs) to monitor, simulate, optimize, and control these systems. ANNs show a unique ability to reveal and simulate complex relationships of dynamic systems such as MBRs, allowing for process optimization and fault detection. This early warning system leads to increased reliability and performance. Integrating ANNs with advanced algorithms and implementing Internet of Things (IoT) devices and new-generation sensors has the potential to transform the advanced wastewater treatment landscape towards the development of smart, self-adaptive systems. Nevertheless, several challenges must be addressed, including the need for high-quality and large-quantity data, human resource training, and integration into existing control system facilities. Since the demand for advanced water treatment and water reuse will continue to expand, proper implementation of ANNs, combined with other AI tools, is an exciting strategy toward the development of integrated and efficient advanced water treatment schemes. Keywords: membrane bioreactors (MBRs); artificial neural networks (ANNs); wastewater treatment; monitoring; modeling; optimization; control; deep learning; Internet of Things (IoT) 1. Introduction Wastewater management, treatment, and reuse have become crucial processes for the protection of the environment and public health. Rapid industrialization, along with urbanization, has consistently increased both the volume and complexity of produced wastewater [1,2]. This is because wastewater is now composed of a variety of synthetic xeno- biotic substances. Therefore, there is an emerging need for the development of advanced wastewater treatment technologies that will facilitate water purification and reuse [1,2]. Among these technologies, membrane bioreactors (MBRs) present a very interesting strat- egy for advanced wastewater treatment, as they incorporate the “green” aspect of biological degradation with the advantages of membrane separation [3–5]. They demonstrate high performance and produce high-quality effluent suitable for various uses [4,5]. In contrast to conventional aeration systems such as activated sludge, MBRs demon- strate several advantages. These include higher efficiency and effluent quality, improved sludge retention time, lower sludge production, and higher nutrient removal. Moreover, MBRs can handle higher organic loads, increasing the possibility for use in various indus- trial applications in addition to the treatment of domestic wastewater [3–6]. However, the operation and maintenance of MBRs present several challenges that prevent their widespread industrial applications. These include issues related to membrane Sci 2023, 5, 31. https://doi.org/10.3390/sci5030031 https://www.mdpi.com/journal/sci