Proceedings of International Conference on Applied Innovation in IT  ·  2026/07/22  ·  Vol. 14  ·  Issue 4  ·  pp. 1111–1116
AI-Based Optimization of Biological Wastewater Treatment Processes in Smart Water Systems
Bakhrom Khamdamov, Azizbek Satvoldiyev and Alexey Shcheludyakov
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Abstract
The rapid growth of urbanization and industrial activities has significantly increased the complexity and volume of wastewater, posing serious challenges to conventional biological treatment systems. This study proposes an artificial intelligence (AI)-based optimization framework for enhancing the performance, energy efficiency, and operational stability of biological wastewater treatment processes within smart water systems. Machine learning models, including artificial neural networks and ensemble learning algorithms, were developed to predict key process variables such as chemical oxygen demand (COD) removal efficiency, dissolved oxygen concentration, and sludge volume index under varying influent and environmental conditions. The proposed framework integrates real-time sensor data, digital control strategies, and adaptive feedback mechanisms to dynamically adjust aeration rates and biomass retention time. Experimental results demonstrate that the AI-driven system improves treatment efficiency, reduces energy consumption, and enhances process resilience against hydraulic and organic load fluctuations. The findings highlight the potential of intelligent optimization techniques to support sustainable and cost-effective wastewater management in smart city infrastructures.
Keywords
Artificial Intelligence Biological Wastewater Treatment Smart Water Systems Machine Learning Optimization Energy Efficiency Process Automation Sustainable Water Management Digital Control
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