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Challenges of industrial wastewater treatment: Utilizing Membrane Bioreactors (MBRs) in conjunction with Artificial Intelligence (AI) technology

Chang, H-L, Liu, Y-L, Keng, C-J, Jiang, H-L, Hu, J (2024) Challenges of industrial wastewater treatment: Utilizing Membrane Bioreactors (MBRs) in conjunction with Artificial Intelligence (AI) technology. Journal of Industrial and Production Engineering, . pp. 1-6. ISSN 2168-1015. E-ISSN 2168-1023. (doi:10.1080/21681015.2024.2330401) (KAR id:105273)

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Abstract

In the past, decisions on wastewater treatment methods have predominantly rested on expert opinions, utilizing the Delphi method. Yet, with an anticipated increase in diversification and customization, especially in the ‘small-batch and diverse’ market over the next decade, addressing the formulation and execution of wastewater treatment for these non-traditional production processes will present substantial challenges. Relying solely on Delphi experts’ decision-making within a short and time-constrained production planning window is expected to prove inadequate. Predominantly relies on the authors’ over 15 years of industry experience in wastewater treatment, this perspective paper proposes an inventive solution that integrates Membrane Bioreactors (MBRs) with Artificial Intelligence (AI) applications. This approach signifies a more advanced method for industrial wastewater treatment compared to conventional methods, with the intention of garnering increased interest for future research endeavors.

Item Type: Article
DOI/Identification number: 10.1080/21681015.2024.2330401
Uncontrolled keywords: Membrane Bioreactors (MBRs), Artificial Intelligence (AI), Wastewater treatment, Artificial Neural Networks (ANN), Genetic Algorithms (GA)
Subjects: H Social Sciences > HF Commerce
Divisions: Divisions > Kent Business School - Division > Department of Marketing, Entrepreneurship and International Business
Funders: University of Kent (https://ror.org/00xkeyj56)
Depositing User: Yu-Lun Liu
Date Deposited: 10 Mar 2024 23:02 UTC
Last Modified: 10 Apr 2024 14:54 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/105273 (The current URI for this page, for reference purposes)

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