https://doi.org/10.15255/KUI.2025.032
Published: Kem. Ind. 75 (7-8) (2026) 405–416
Paper reference number: KUI-32/2025
Paper type: Professional paper
Download paper:  PDF

DriniChlor: Towards a Data-driven and Adaptive Chlorination Process in Drinking Water Treatment Systems

V. M. Beluli

Abstract

This study presents the development of an automated system for the management and monitoring of water quality using the Python programming language, focusing on optimising chlorine dosage in water disinfection processes. The proposed module, named DriniChlor, utilises sensors to measure key water parameters such as temperature, dissolved oxygen, and turbidity. The collected data were analysed in real-time to determine the optimal chlorine dosage, ensuring maximum safety and efficiency. The integration of such technologies offers an innovative approach that not only improves water quality but also reduces operational costs and environmental impact. Results indicate that the system is effective in enhancing water treatment processes and is suitable for application in various treatment plants. This development represents a significant step toward the use of automated technologies for the sustainable management of water resources.

This work is licensed under a Creative Commons Attribution 4.0 International License

Keywords

process control engineering, Python, automated water treatment system, chlorine dosage optimisation