Application of artificial intelligence tools in wastewater and waste gas treatment systems: Recent advances and prospects

Shishir Kumar Behera; S Karthika; Biswanath Mahanty; Saroj K Meher; Mohd Zafar; Divya Baskaran; Ravi Rajamanickam; Raja Das; Kannan Pakshirajan; Abubakar M Bilyaminu; Eldon R Rene

doi:10.1016/j.jenvman.2024.122386

Application of artificial intelligence tools in wastewater and waste gas treatment systems: Recent advances and prospects

J Environ Manage. 2024 Sep 10:370:122386. doi: 10.1016/j.jenvman.2024.122386. Online ahead of print.

Authors

Affiliations

¹ Process Simulation Research Group, School of Chemical Engineering, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India. Electronic address: shishir.kb@vit.ac.in.
² Department of Chemical Engineering, Alagappa College of Technology, Anna University, Chennai, 600 025, Tamil Nadu, India.
³ Division of Biotechnology, Karunya Institute of Technology & Sciences, Coimbatore, 641 114, Tamil Nadu, India.
⁴ Systems Science and Informatics Unit, Indian Statistical Institute, Bangalore, 560059, India.
⁵ Department of Applied Biotechnology, College of Applied Sciences & Pharmacy, University of Technology and Applied Sciences - Sur, P.O. Box: 484, Zip Code: 411, Sur, Oman.
⁶ Department of Chemical and Biomolecular Engineering, Chonnam National University, Yeosu, Jeonnam, 59626, South Korea; Department of Biomaterials, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, 600 077, Tamil Nadu, India.
⁷ Department of Chemical Engineering, Annamalai University, Chidambaram, 608002, Tamil Nadu, India.
⁸ Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India.
⁹ Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, 781 039, Assam, India.
¹⁰ Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, P. O. Box 3015, 2601, DA Delft, the Netherlands.

PMID: 39260284
DOI: 10.1016/j.jenvman.2024.122386

Abstract

The non-linear complex relationships among the process variables in wastewater and waste gas treatment systems possess a significant challenge for real-time systems modelling. Data driven artificial intelligence (AI) tools are increasingly being adopted to predict the process performance, cost-effective process monitoring, and the control of different waste treatment systems, including those involving resource recovery. This review presents an in-depth analysis of the applications of emerging AI tools in physico-chemical and biological processes for the treatment of air pollutants, water and wastewater, and resource recovery processes. Additionally, the successful implementation of AI-controlled wastewater and waste gas treatment systems, along with real-time monitoring at the industrial scale are discussed.

Keywords: Air pollution; Artificial neural network; Automation and control; Machine learning; Resource recovery; Wastewater treatment.

Publication types

Review