Artificial neural network modeling for the prediction, estimation, and treatment of diverse wastewaters: A comprehensive review and future perspective

Ibrahim M 1 , Haider A 2 , Lim JW 3 , Mainali B 4 , Aslam M 5 , Kumar M 6 Show all authors , Shahid MK 7

Affiliations 

  • 1 Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
  • 2 Department of Environmental and IT Convergence Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
  • 3 HICoE-Centre for Biofuel and Biochemical Research, Institute of Sustainable Energy and Resources, Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia; Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, 602105, Chennai, India
  • 4 School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney 2109, Australia
  • 5 Membrane Systems Research Group, Department of Chemical Engineering, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan; Faculty of Engineering & Quantity Surveying, INTI International University (INTI-IU), Persiaran Perdana BBN, Putra Nilai, Nilai, 71800, Negeri Sembilan, Malaysia
  • 6 Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India
  • 7 Department of Environmental and IT Convergence Engineering, Chungnam National University, Daejeon 34134, Republic of Korea; School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney 2109, Australia; Faculty of Civil Engineering and Architecture, National Polytechnic Institute of Cambodia (NPIC), Phnom Penh 12409, Cambodia. Electronic address: mkbutt2000@gmail.com
Chemosphere, 2024 Jul 15;362:142860.
PMID: 39019174 DOI: 10.1016/j.chemosphere.2024.142860

Abstract

The application of artificial neural networks (ANNs) in the treatment of wastewater has achieved increasing attention, as it enhances the efficiency and sustainability of wastewater treatment plants (WWTPs). This paper explores the application of ANN-based models in WWTPs, focusing on the latest published research work, by presenting the effectiveness of ANNs in predicting, estimating, and treatment of diverse types of wastewater. Furthermore, this review comprehensively examines the applicability of the ANNs in various processes and methods used for wastewater treatment, including membrane and membrane bioreactors, coagulation/flocculation, UV-disinfection processes, and biological treatment systems. Additionally, it provides a detailed analysis of pollutants viz organic and inorganic substances, nutrients, pharmaceuticals, drugs, pesticides, dyes, etc., from wastewater, utilizing both ANN and ANN-based models. Moreover, it assesses the techno-economic value of ANNs, provides cost estimation and energy analysis, and outlines promising future research directions of ANNs in wastewater treatment. AI-based techniques are used to predict parameters such as chemical oxygen demand (COD) and biological oxygen demand (BOD) in WWTP influent. ANNs have been formed for the estimation of the removal efficiency of pollutants such as total nitrogen (TN), total phosphorus (TP), BOD, and total suspended solids (TSS) in the effluent of WWTPs. The literature also discloses the use of AI techniques in WWT is an economical and energy-effective method. AI enhances the efficiency of the pumping system, leading to energy conservation with an impressive average savings of approximately 10%. The system can achieve a maximum energy savings state of 25%, accompanied by a notable reduction in costs of up to 30%.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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