TY - JOUR
T1 - Enhancement of membrane system performance using artificial intelligence technologies for sustainable water and wastewater treatment
T2 - A critical review
AU - Viet, Nguyen Duc
AU - Jang, Duksoo
AU - Yoon, Yeomin
AU - Jang, Am
N1 - Publisher Copyright:
© 2021 Taylor & Francis Group, LLC.
PY - 2022
Y1 - 2022
N2 - In recent years, membrane technologies are widely utilized in water and wastewater treatment processes. However, controlling and improving these systems still need to be investigated and, therefore, are attracting increasing amounts of attention from researchers worldwide. Industry 4.0 has increased in importance over the past few years, and artificial intelligence (AI) technology has demonstrated its strength in supporting decision-making in various fields, including environmental systems and especially membrane processes. AI allows for cost-effective operation of systems, including better planning and tracking as well as comprehensive understanding of resource-loss in real-time, then maximizing revenue capture and water quality satisfaction. This study therefore aims to provide a comprehensive review of the current application of AI-based tools in simulating membrane processes as well as the feasibility of applying these models to other fields in which membranes are to be used in the future. The existing conventional mathematical models are illustrated along with their advantages and shortcomings. The definition and classification of state-of-the-art AI models, as well as the benefits of these over conventional models, are also discussed. Furthermore, the basic principle of membrane processes and current application of AI-based technologies in simulating the performance of these membrane systems are systematically reviewed. Finally, the implications and recommendations for future studies are discussed.
AB - In recent years, membrane technologies are widely utilized in water and wastewater treatment processes. However, controlling and improving these systems still need to be investigated and, therefore, are attracting increasing amounts of attention from researchers worldwide. Industry 4.0 has increased in importance over the past few years, and artificial intelligence (AI) technology has demonstrated its strength in supporting decision-making in various fields, including environmental systems and especially membrane processes. AI allows for cost-effective operation of systems, including better planning and tracking as well as comprehensive understanding of resource-loss in real-time, then maximizing revenue capture and water quality satisfaction. This study therefore aims to provide a comprehensive review of the current application of AI-based tools in simulating membrane processes as well as the feasibility of applying these models to other fields in which membranes are to be used in the future. The existing conventional mathematical models are illustrated along with their advantages and shortcomings. The definition and classification of state-of-the-art AI models, as well as the benefits of these over conventional models, are also discussed. Furthermore, the basic principle of membrane processes and current application of AI-based technologies in simulating the performance of these membrane systems are systematically reviewed. Finally, the implications and recommendations for future studies are discussed.
KW - Artificial intelligence
KW - Daniel Tsang
KW - Industry 4.0
KW - membrane processes
KW - modeling
KW - sustainable water treatment
UR - http://www.scopus.com/inward/record.url?scp=85122622978&partnerID=8YFLogxK
U2 - 10.1080/10643389.2021.1940031
DO - 10.1080/10643389.2021.1940031
M3 - Review article
AN - SCOPUS:85122622978
SN - 1064-3389
VL - 52
SP - 3689
EP - 3719
JO - Critical Reviews in Environmental Science and Technology
JF - Critical Reviews in Environmental Science and Technology
IS - 20
ER -