APPLICATION OF AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM TO CONTROL THE WASTEWATER TREATMENT PROCESS

Authors

Jaloliddin Abdurazzakovich Eshbobaev, Tashkent institute of chemical technology. Address: Navoi Street, 32, Tashkent, Uzbekistan, 100011. E-mail: eshbobayevjalol@gmail.com, Phone: +998938032741;Follow
Bakhodir Tajiddinovich Khamidov, Tashkent institute of chemical technology. Address: Navoi Street, 32, Tashkent, Uzbekistan, 100011. E-mail: bkhamidov32@gmail.com, Phone: +998998166128;Follow
Marcos Torices Fallanza, University of Cantabria. Address: Avda de los Castros, s/n 39005 Santander-Spain. E-mail: fallanzam@unican.es , Phone: +34 942200870;Follow

Abstract

This study explores the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for controlling wastewater treatment processes using ion-exchange resins. It addresses the critical challenges of water scarcity and pollution by enhancing the regulation of water hardness (H) and Total Dissolved Solids (TDS). Using a pilot laboratory device and experimental data from the mixed wastewater of the Kungrad Soda Plant in Uzbekistan, an ANFIS model was developed in MATLAB to automate process control. The model leverages water hardness and TDS as input parameters to regulate the water flow rate by servo valve opening degree, ensuring precise and efficient treatment. Compared to fuzzy logic models, ANFIS achieves a 3–4 second reduction in settling time and significantly lowers error rates. This enhanced control not only optimizes water hardness and TDS regulation but also ensures greater consistency and efficiency. Experimental results indicate that adopting ANFIS for real-time parameter adjustments can reduce energy consumption by up to 1.3%. These findings highlight the potential of ANFIS as a superior solution for controlling dynamic and nonlinear wastewater treatment processes.

First Page

52

Last Page

60

References

1. Vliet, M.T.H. van, Jones, E.R., Flörke, M., Franssen, W.H.P., Hanasaki, N., Wada, Y., Yearsley, J.R. (2021). Global Water Scarcity Including Surface Water Quality and Expansions of Clean Water Technologies. Environ. Res. Lett., 16, 024020. doi:10.1088/1748-9326/abbfc3.
2. Shemer, H., Wald, S., Semiat, R. (2023). Challenges and Solutions for Global Water Scarcity. Membranes, 13, 612. doi:10.3390/membranes13060612.
3. Vijayakumar, G., Rajkumar, M., Chandar, N.R., Selvakumar, P., Duraisamy, R. (2024). Environmentally Friendly TDS Removal from Waste Water by Electrochemical Ion Exchange Batch-Type Recirculation (EIR) Technique. Environ. Sci.: Water Res. Technol., 10, 826-835. doi:10.1039/D3EW00793F.
4. Curto, D., Franzitta, V., Guercio, A. (2021). A Review of the Water Desalination Technologies. Applied Sciences, 11, 670. doi:10.3390/app11020670.
5. Almasoudi, S., Jamoussi, B. (2024). Desalination Technologies and Their Environmental Impacts: A Review. Sustainable Chemistry One World, 1, 100002. doi:10.1016/j.scowo.2024.100002.
6. 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.
7. Ahmed, F.E., Hashaikeh, R., Diabat, A., Hilal, N. (2019). Mathematical and Optimization Modelling in Desalination: State-of-the-Art and Future Direction. Desalination, 469, 114092. doi:10.1016/j.desal.2019.114092.
8. 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, 47 p.
9. 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, 39 p.
10. Ahmadianfar, I., Shirvani-Hosseini, S., He, J., Samadi-Koucheksaraee, A., Yaseen, Z.M. (2022). An Improved Adaptive Neuro Fuzzy Inference System Model Using Conjoined Metaheuristic Algorithms for Electrical Conductivity Prediction. Sci Rep, 12, 4934. doi:10.1038/s41598-022-08875-w.
11. Guerra, M.I.S., De Araújo, F.M.U., De Carvalho Neto, J.T., Vieira, R.G. (2024). Survey on Adaptative Neural Fuzzy Inference System (ANFIS) Architecture Applied to Photovoltaic Systems. Energy Syst, 15, 505-541. doi:10.1007/s12667-022-00513-8.
12. Al-Mayyahi, A., Wang, W., Birch, P. (2014). Adaptive Neuro-Fuzzy Technique for Autonomous Ground Vehicle Navigation. Robotics, 3, 349-370. doi:10.3390/robotics3040349.
13. Aliev, R.A. (2013). Fundamentals of the Fuzzy Logic-Based Generalized Theory of Decisions. Springer.
14. Eshbobaev, J.A., Khamidov, B.T., Norkobilov, A.T., Kodirov, O.S. (2023). Development of the computer model of the waste water treatment process using ion-exchange resins in the Matlab program. Chemical Technology, Control and Management, 63-69. doi:10.59048/2181-1105.1484.
15. Eshbobayev, J.A. (2024). Oqova suvlarni tozalash jarayonini noqat’iy mantiq asosida boshqarish modelini ishlab chiqish. Ilm-fan va innovatsion rivojlanish, 7(2), 72-82.
16. Burn, S., Hoang, M., Zarzo, D., Olewniak, F., Campos, E., Bolto, B., Barron, O. (2015). Desalination Techniques – A Review of the Opportunities for Desalination in Agriculture. Desalination, 364, 2-16. doi:10.1016/j.desal. 2015.01.041.

Recommended Citation

Eshbobaev, Jaloliddin Abdurazzakovich; Khamidov, Bakhodir Tajiddinovich; and Fallanza, Marcos Torices (2025) "APPLICATION OF AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM TO CONTROL THE WASTEWATER TREATMENT PROCESS," Chemical Technology, Control and Management: Vol. 2025: Iss. 1, Article 7.
DOI: https://doi.org/10.59048/2181-1105.1654