EventsThe 6th International Electronic Conference on Applied Sciences
Published
This submission belongs to the session S6. Energy, Environmental and Earth Science of the event The 6th International Electronic Conference on Applied Sciences
Published date
03 Dec, 2025
Academic Editor
author-avatarSimeone Chianese
Citation
Jaloliddin Eshbobaev, Adham Norkobilov, Zafar Turakulov, Comparison of intelligent and traditional control systems in wastewater treatment process control, in Proceedings of The 6th International Electronic Conference on Applied Sciences, 9 December–11 December 2025, MDPI: Basel, Switzerland
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Comparison of intelligent and traditional control systems in wastewater treatment process control

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1. Department of Automation and digital control, Tashkent Institute of Chemical Technology, Tashkent 100011, Uzbekistan, Uzbekistan
2. Department of Food engineering, Faculty of Shahrisabz Food Engineering, Tashkent Institute of Chemical Technology, Shahrisabz 181306, Uzbekistan, Uzbekistan
Abstract

Modern wastewater treatment plants face growing operational challenges due to increasingly variable influent compositions, stricter environmental regulations, and rising energy efficiency demands. This study provides a comprehensive evaluation of three advanced control strategies for optimizing wastewater treatment processes: conventional Proportional-Integral-Derivative (PID) control, fuzzy logic control, and the innovative Adaptive Neuro-Fuzzy Inference System (ANFIS). The research specifically focuses on addressing the critical need for intelligent systems capable of managing complex, non-linear relationships in key water quality parameters, particularly Total Dissolved Solids (TDSs) and water hardness concentrations. Through detailed MATLAB/Simulink simulations, we implemented each control methodology in a sophisticated wastewater treatment plant model that accurately replicates real-world operational conditions. The controller’s performance was rigorously assessed using multiple quantitative metrics: settling time, percentage overshoot, steady-state error, and energy consumption efficiency. Experimental results demonstrated that while the conventional PID controller achieved basic regulation, it exhibited significant limitations including 10% overshoot, prolonged 25-second settling time, and noticeable steady-state error. The fuzzy logic approach showed marked improvement, reducing overshoot to less than 1% and settling time to 13 seconds. The ANFIS controller outperformed both alternatives, delivering exceptional control precision with near-zero overshoot (0.2%), rapid 10-second response time, and complete elimination of steady-state error. Furthermore, the ANFIS system demonstrated superior adaptability to process variations while reducing energy consumption by 50% compared to traditional methods. These findings provide compelling empirical evidence that ANFIS-based control systems represent a transformative solution for next generation wastewater treatment infrastructure, offering unmatched performance in terms of both treatment quality and operational efficiency.

Keywords
Wastewater treatment control
water hardness and TDS
PID
fuzzy logic
ANFIS
energy efficiency.
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