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

Wahid Ali Hamood Altowayti (), Shafinaz Shahir (), Taiseer Abdalla Elfadil Eisa, Maged Nasser, Muhammad Imran Babar, Abdullah Faisal Alshalif and Faris Ali Hamood AL-Towayti
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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

Sustainability, 2022, vol. 14, issue 22, 1-32

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.

Keywords: wastewater treatment; response surface methodology; Monod model; Contois model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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