Abstract
Wastewater treatment systems (WTSs) have as an objective to efficiently treat the wastewater flows that they receive. Wastewater treatment plants (WWTP) process optimization is generally based on biological process optimization, which is usually related to the quantity and quality of the WWTP inflows. The inflow contributions sometimes destabilize the biological system due to flow and nutrient load limitations, with a higher impact in small and decentralized systems. Their management is a complex task due to the multiple constituents and different flows, but it should be the first step for a general, optimal and comprehensive optimization.Ant colony optimization (ACO) has demonstrated the ability to solve these complex problems, as metaheuristic methodologies, using an iterative and probabilistic procedure. This work proposes to solve the treatment influent composition by using two ACO algorithms. Both algorithms apply bounded pheromone trails to resolve the failures in the search for an optimal solution limited by inflow constraints. The results present high efficacy in maximizing the total wastewater inflow that fulfils all the constraints and improving the WWTP management with different inflows and constraints.
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