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Smart Modelling of a Sustainable Biological Wastewater Treatment Technologies: A Critical Review

Author

Listed:
  • Wahid Ali Hamood Altowayti

    (Department of Biosciences, Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia)

  • Shafinaz Shahir

    (Department of Biosciences, Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia)

  • Taiseer Abdalla Elfadil Eisa

    (Department of Information Systems-Girls Section, King Khalid University, Mahayil 62529, Saudi Arabia)

  • Maged Nasser

    (School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia)

  • Muhammad Imran Babar

    (Department of Computer Science, FAST-National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan)

  • Abdullah Faisal Alshalif

    (Department of Civil Engineering, Faculty of Civil Engineering and Built Environment, Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, Johor, Malaysia)

  • Faris Ali Hamood AL-Towayti

    (Departement of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia)

Abstract

One of the most essential operational difficulties that water companies face today is the capacity to manage their water treatment process daily. Companies are looking for long-term solutions to predict how their treatment methods may be enhanced as they face growing competition. Many models for biological growth rate control, such as the Monod and Contois models, have been suggested in the literature. This review further emphasized that the Contois model is the best and is more suited to predicting the performance of biological growth rate than the other applicable models with a high correlation coefficient. Furthermore, the most well-known models for optimizing and predicting the wastewater treatment process are response surface methodology (RSM) and artificial neural networks (ANN). Based on this review, the ANN is the best model for wastewater treatment with high accuracy in biological wastewater treatment. Furthermore, the present paper conducts a bibliometric analysis using VOSviewer to assess research performance and perform a scientific mapping of the most relevant literature in the field. A bibliometric study of the most recent publications in the SCOPUS database between 2018 and 2022 is performed to assess the top ten countries around the world in the publishing of employing these four models for wastewater treatment. Therefore, major contributors in the field include India, France, Iran, and China. Consequently, in this research, we propose a sustainable wastewater treatment model that uses the Contois model and the ANN model to save time and effort. This approach may be helpful in the design and operation of clean water treatment operations, as well as a tool for improving day-to-day performance management.

Suggested Citation

  • Wahid Ali Hamood Altowayti & Shafinaz Shahir & Taiseer Abdalla Elfadil Eisa & Maged Nasser & Muhammad Imran Babar & Abdullah Faisal Alshalif & Faris Ali Hamood AL-Towayti, 2022. "Smart Modelling of a Sustainable Biological Wastewater Treatment Technologies: A Critical Review," Sustainability, MDPI, vol. 14(22), pages 1-32, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15353-:d:977072
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    References listed on IDEAS

    as
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    4. Briant Kang Xian Ho & Baharin Azahari & Mohd Firdaus Bin Yhaya & Amir Talebi & Charles Wai Chun Ng & Husnul Azan Tajarudin & Norli Ismail, 2020. "Green Technology Approach for Reinforcement of Calcium Chloride Cured Sodium Alginate Films by Isolated Bacteria from Palm Oil Mill Effluent (POME)," Sustainability, MDPI, vol. 12(22), pages 1-13, November.
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    1. Syafiqa Ayob & Wahid Ali Hamood Altowayti & Norzila Othman & Faisal Sheikh Khalid & Shafinaz Shahir & Husnul Azan Tajarudin & Ammar Mohammed Ali Alqadasi, 2023. "Experimental and Modeling Study on the Removal of Mn, Fe, and Zn from Fiberboard Industrial Wastewater Using Modified Activated Carbon," Sustainability, MDPI, vol. 15(8), pages 1-23, April.

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