Development of an optimal operation strategy in a sequential batch reactor (SBR) through mixed-integer particle swarm dynamic optimization (PSO)

Dynamic optimization in SBRs represents an enormous challenge in order to save time and energy. As the non-convexities presented by these systems limit the application of deterministic techniques, stochastic contributions to meet global optimization become crucial. A PSO algorithm in order to minimize the aeration demand in a SBR was developed. The network size, sequencing and stages duration, were assumed as the decision variables for the dynamic MINLP problem. Two kinds of PSO algorithms (relaxed and mixed-integer) were applied in order to find the best way for taking into account the mixed-integer nature. Stochastic optimization improved the results obtained from a sequential shooting method/NLP, and mixed-integer PSO resulted in the best structure solving the MINLP. Despite that, and in order to assure the most robust and reliable solution, the assessment of both PSO formulations must be considered. PSO results have given an optimal operation policy of easy implementation.