The ultraviolet water disinfection process has numerous advantages in comparison with other water treatments. This study deals with this process’s optimal control and presents all its stages, from problem statement to analysis of results. We consider a typical disinfection water system and data presented in the literature. In this case, the control inputs are the water flow rate and the UV light intensity. A nonlinear dynamic model for the UV disinfection system is constructed and generates a method to calculate the bacterial reduction. An optimal control problem (OCP) is stated using the dynamic model and two optimal criteria: the disinfection and energy criterion. The optimal solutions are yielded by a metaheuristic algorithm based on hybrid topology particle swarm optimization. The algorithm determines the optimal flow rate and light intensity for an imposed bacterial reduction. The implemented program was carried out for a wide range of input values for comprehensive analysis. The analysis of the results establishes when an optimal solution exists and the extent to which the disinfection or energy criterion is fulfilled. The method to calculate the bacterial reduction, OCP statement, optimization algorithm, and results’ analysis may be considered findings of this work.

When an optimal control problem requires an important computational effort, a metaheuristic algorithm (MA) can be a useful approach. An MA is conceived to solve a specific optimal control problem having a characteristic objective function. This algorithm solely yields only an optimal offline solution. The desideratum to have a closed-loop implementation can be fulfilled through a supplementary “tool”, the Receding Horizon Control (RHC) structure. This paper addresses a particular case and integrates Evolutionary Algorithms into the RHC structure. The main objective is to propose a general harmonization between the Controller of the closed loop and the Evolutionary Algorithm. Some details concerning the implementation of the closed loop and Controller are described. The impact of the RHC’s prediction technique upon the control sequences’ encoding is also analyzed. Two general structure Controllers are proposed, one of them conceived to cope with restrictive time constraints. Practical ideas have been illustrated through a case study: the well-known optimal control of a fed-batch reactor for ethanol production. This time, our implementation achieves a closed-loop solution. The results from the programs and simulation series validate the Controllers, EAs, and the closed-loop structure. Generally speaking, the association between RHC and EA can be a realistic solution to optimal process control.