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

State of the fieldRecent advances in water treatment technologies have increasingly focused on automation, data-driven control, and intelligent monitoring systems.The integration of digital tools and programming languages such as Python has allowed for the development of flexible, open-source platforms capable of processing real-time sensor data for water quality assessment.Modern systems typically combine sensors for temperature, DO, NTU, and residual chlorine, providing continuous feedback to optimise disinfection and operational efficiency.