UV water treatment control: A hybrid genetic algorithm for modeling and optimization

Water treatment systems face increasing challenges due to pollution, limited resources, and the need for energy efficient disinfection methods. This work proposes a novel integrated approach that combines an improved nonlinear dynamic model of a UV disinfection system with a multi-objective genetic algorithm (GA) for optimal control. The dynamic model was enhanced by tuning transfer function parameters to accurately replicate experimental behaviors of UV dose and bacterial reduction. These model outputs were then directly embedded into the GA's objective function to simultaneously optimize UV intensity and flow rate, forming a tightly coupled dynamic modeling optimization framework. Unlike conventional approaches based on static or decoupled models, the proposed strategy enables control oriented and energy aware optimization. Experimental validations demonstrate a strong correlation with simulated results, achieving up to 14% energy savings without compromising disinfection efficiency. This hybrid modeling optimization strategy provides a robust and flexible framework for real time control and energy efficient operation of UV based water treatment systems.