DEVELOPMENT OF AN ANN-BASED PREDICTIVE MODEL FOR THE EFFICIENT TREATMENT OF INDUSTRIAL WASTEWATER

Authors

Alisher Khudoyberdi ugli Rakhimov, Karshi State Technical University. Address: 20, Shahrisabz street, Shahrisabz, Uzbekistan, 181306. E-mail: alisherrakhimov35@gmail.com, Phone: +998993015567;Follow
Hasan Siddiq ugli Murodov, Karshi State Technical University. Address: 20, Shahrisabz street, Shahrisabz, Uzbekistan, 181306. E-mail: muradovhasan@gmail.com, Phone: +998995962486;Follow
Farrukh Bakhtiyorovich Igitov, Tashkent institute of chemical technology. Address: Navoi Street, 32, Tashkent, Uzbekistan, 100011. E-mail: igitovfarrux@gmail.com, Phone: +998995382862;Follow
Shuhrat Ahmadjonovich Mutalov, Angren university. Address: Fleyshmaxer ko‘chasi, 2-uy, Toshkent vil, Angren sh. 110200. E-mail: mutalovshuhrat@gmail.com, Phone: +9989465845988;
Jaloliddin Abdurazzakovich Eshbobaev, Tashkent institute of chemical technology. Address:.Navoi Street, 32, Tashkent, Uzbekistan, 100011. E-mail: eshbobayevjalol@gmail.com, Phone: +998938032741.

Abstract

The effective removal of free chlorine ions from industrial wastewater is critical to ensure process safety, minimize equipment corrosion, and protect aquatic environments. In this study, a laboratory-scale activated carbon filtration system was developed and tested to treat chlorine-contaminated water collected from sludge collectors at industrial facilities. A total of 200 experimental trials were conducted under varied operational conditions, including changes in flow rate, initial chlorine concentration, pressure, pH, temperature, and activated carbon dosage. The resulting dataset was used to train a predictive model based on a feedforward backpropagation artificial neural network (ANN) implemented in MATLAB. The ANN model demonstrated excellent performance, achieving a mean squared error (MSE) of 9.63 × 10⁻⁵ and a high regression coefficient (R = 0.9665) across all data. The model’s predictive capability was validated through detailed performance plots, error histograms, and regression analysis. The results confirm that the ANN can reliably estimate residual chlorine concentrations based on key process variables, making it a valuable tool for optimizing activated carbon filter operations and enhancing intelligent control in industrial wastewater treatment systems. This research highlights the potential of integrating AI-based modeling with traditional water purification technologies to improve process efficiency and environmental sustainability.

First Page

71

Last Page

78

References

1. Karef, S., Azlaoui, M., Boussaid, K., Batana, F. Z., Bruzzoniti, M. C., Fodili, M., Kettab, A. (2025). Fertilizer potential and social perception of the agricultural reuse of sewage sludge and treated wastewater. Desalination and Water Treatment, 322, 101186, doi:10.1016/j.dwt.2025.101186.
2. Elsaid, K., Sayed, E.T., Abdelkareem, M.A., Mahmoud, M.S., Ramadan, M., Olabi, A.G. (2020). Environmental impact of emerging desalination technologies: A preliminary evaluation. Journal of Environmental Chemical Engineering, 8(5), 104099, doi:10.1016/j.jece.2020.104099.
3. Ahmed, F.E., Khalil, A., Hilal, N. (2021). Emerging desalination technologies: Current status, challenges and future trends. Desalination, 517, 115183, doi:10.1016/j.desal.2021.115183.
4. Mahadeva, R., Manik, G., Verma, O. P., Sinha, S. (2018). Modelling and simulation of desalination process using artificial neural network: A review. Desalination and Water Treatment, 122, 351-364, doi:10.5004/dwt.2018.23106.
5. Awerbuch, L. (1989). Desalination technology: An overview. In The Politics of Scarcity; Routledge.
6. Curto, D., Franzitta, V., Guercio, A. (2021). A review of the water desalination technologies. Applied Sciences, 11(2), 670, doi:10.3390/app11020670.
7. Ayaz, M., Namazi, M.A., Din, M.A.U., Ershath, M.I.M., Mansour, A., Aggoune, E.H.M. (2022). Sustainable seawater desalination: Current status, environmental implications and future expectations. Desalination, 540, 116022, doi:10.1016/j.desal.2022.116022.
8. Malaeb, L., Ayoub, G.M. (2011). Reverse osmosis technology for water treatment: State of the art review. Desalination, 267(1), 1-8, doi:10.1016/j.desal.2010.09.001.
9. Eshbobaev, J., Norkobilov, A., Turakulov, Z., Khamidov, B., Kodirov, O. (2023). Field trial of solar-powered ion-exchange resin for the industrial wastewater treatment process. In Proceedings of the ECP 2023; MDPI, p. 47.
10. Do Thi, H.T., Pasztor, T., Fozer, D., Manenti, F., Toth, A. J. (2021). Comparison of desalination technologies using renewable energy sources with life cycle, PESTLE, and multi-criteria decision analyses. Water, 13(21), 3023, doi:10.3390/w13213023.
11. Eshbobaev, J., Norkobilov, A., Usmanov, K., Khamidov, B., Kodirov, O., Avezov, T. (2024). Control of wastewater treatment processes using a fuzzy logic approach. In Proceedings of the The 3rd International Electronic Conference on Processes; MDPI, p. 39.
12. Dastorani, M.T., Moghadamnia, A., Piri, J., Rico-Ramirez, M. (2010). Application of ANN and ANFIS models for reconstructing missing flow data. Environmental Monitoring and Assessment, 166(1-4), 421-434, doi:10.1007/s10661-009-1012-8.
13. Solaimany-Aminabad, M., Maleki, A., Hadi, M. (2013). Application of artificial neural network (ANN) for the prediction of water treatment plant influent characteristics. Journal of Advances in Environmental Health Research, 1(2), 89-100, doi:10.22102/jaehr.2013.40130.
14. Saraswathi, R., Saseetharan, M.K., Suja, S. (2012). ANN-based predictive model for performance evaluation of paper and pulp effluent treatment plant. International Journal of Computer Applications in Technology, 45(4), 280-289, doi:10.1504/IJCAT.2012.051128.
15. Jawad, J., Hawari, A. H., Javaid Zaidi, S. (2021). Artificial neural network modeling of wastewater treatment and desalination using membrane processes: A review. Chemical Engineering Journal, 419, 129540, doi:10.1016/j.cej.2021.129540.
16. Zeigler, B. P., Praehofer, H., Kim, T. G. (2000). Theory of modeling and simulation. Academic Press.

Recommended Citation

Rakhimov, Alisher Khudoyberdi ugli; Murodov, Hasan Siddiq ugli; Igitov, Farrukh Bakhtiyorovich; Mutalov, Shuhrat Ahmadjonovich; and Eshbobaev, Jaloliddin Abdurazzakovich (2025) "DEVELOPMENT OF AN ANN-BASED PREDICTIVE MODEL FOR THE EFFICIENT TREATMENT OF INDUSTRIAL WASTEWATER," Chemical Technology, Control and Management: Vol. 2025: Iss. 3, Article 9.
DOI: https://doi.org/10.59048/2181-1105.1685