IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i22p15353-d977072.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/22/15353/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/22/15353/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hill, Tim & Marquez, Leorey & O'Connor, Marcus & Remus, William, 1994. "Artificial neural network models for forecasting and decision making," International Journal of Forecasting, Elsevier, vol. 10(1), pages 5-15, June.
    2. Mosleh Hmoud Al-Adhaileh & Fawaz Waselallah Alsaade, 2021. "Modelling and Prediction of Water Quality by Using Artificial Intelligence," Sustainability, MDPI, vol. 13(8), pages 1-18, April.
    3. Abdullah Faisal Alshalif & J. M. Irwan & Husnul Azan Tajarudin & N. Othman & A. A. Al-Gheethi & S. Shamsudin & Wahid Ali Hamood Altowayti & Saddam Abo Sabah, 2021. "Factors Affecting Carbonation Depth in Foamed Concrete Bricks for Accelerate CO 2 Sequestration," Sustainability, MDPI, vol. 13(19), pages 1-15, October.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Prybutok, Victor R. & Yi, Junsub & Mitchell, David, 2000. "Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations," European Journal of Operational Research, Elsevier, vol. 122(1), pages 31-40, April.
    2. C, Deep Prakash & Majumdar, Adrija, 2023. "Predicting sports fans’ engagement with culturally aligned social media content: A language expectancy perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    3. Leung, Philip C.M. & Lee, Eric W.M., 2013. "Estimation of electrical power consumption in subway station design by intelligent approach," Applied Energy, Elsevier, vol. 101(C), pages 634-643.
    4. Chatfield, Chris, 1995. "Positive or negative?," International Journal of Forecasting, Elsevier, vol. 11(4), pages 501-502, December.
    5. Oscar Claveria & Salvador Torra, 2013. "“Forecasting Business surveys indicators: neural networks vs. time series models”," AQR Working Papers 201312, University of Barcelona, Regional Quantitative Analysis Group, revised Nov 2013.
    6. Thomassey, Sebastien & Happiette, Michel & Castelain, Jean Marie, 2005. "A short and mean-term automatic forecasting system--application to textile logistics," European Journal of Operational Research, Elsevier, vol. 161(1), pages 275-284, February.
    7. Arshia Amiri & Ulf-G Gerdtham, 2012. "Granger Causality Between Exports, Imports and GDP in France: Evidance from Using Geostatistical Models," Economic Research Guardian, Weissberg Publishing, vol. 2(1), pages 43-59, May.
    8. Geraint Johnes, 2000. "Up Around the Bend: Linear and nonlinear models of the UK economy compared," International Review of Applied Economics, Taylor & Francis Journals, vol. 14(4), pages 485-493.
    9. Anna Kuzminova & Mariia Dmitrenko & Anton Mazur & Sergey Ermakov & Anastasia Penkova, 2021. "Novel Pervaporation Membranes Based on Biopolymer Sodium Alginate Modified by FeBTC for Isopropanol Dehydration," Sustainability, MDPI, vol. 13(11), pages 1-25, May.
    10. Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
    11. de Lucio, Juan, 2021. "Estimación adelantada del crecimiento regional mediante redes neuronales LSTM," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 49, pages 45-64.
    12. Mioara CHIRITA & Daniela SARPE, 2011. "Usefulness of Artificial Neural Networks for Predicting Financial and Economic Crisis," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 44-48.
    13. Daniel Santin, 2008. "On the approximation of production functions: a comparison of artificial neural networks frontiers and efficiency techniques," Applied Economics Letters, Taylor & Francis Journals, vol. 15(8), pages 597-600.
    14. Yi-Jen Mon, 2022. "Vision Robot Path Control Based on Artificial Intelligence Image Classification and Sustainable Ultrasonic Signal Transformation Technology," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    15. Pei En Lee, 2019. "The Empirical Study of Investor Sentiment on Stock Return Prediction," International Journal of Economics and Financial Issues, Econjournals, vol. 9(2), pages 119-124.
    16. Lolli, F. & Gamberini, R. & Regattieri, A. & Balugani, E. & Gatos, T. & Gucci, S., 2017. "Single-hidden layer neural networks for forecasting intermittent demand," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 116-128.
    17. C. Orsenigo & C. Vercellis, 2018. "Anthropogenic influence on global warming for effective cost-benefit analysis: a machine learning perspective," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 45(3), pages 425-442, September.
    18. Icen Yoosefdoost & Abbas Khashei-Siuki & Hossein Tabari & Omolbani Mohammadrezapour, 2022. "Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1191-1215, March.
    19. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    20. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents’ expectations. Different patterns of anticipation of the 2008 financial crisis”," AQR Working Papers 201508, University of Barcelona, Regional Quantitative Analysis Group, revised Mar 2015.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15353-:d:977072. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.