Modeling and optimization of activated sludge bioreactors for wastewater treatment taking into account spatial inhomogeneities. (June 2017) - Crespo, M.; Ivorra, B.; Ramos, A.M.; Rapaport, A.
Abstract : Highlights: We aim to depollute a lake, connected to a reactor, by choosing a suitable input flow. We consider tubular reactors with inhomogeneities due to diffusion and flow phenomena. We compare our results with those obtained with perfectly-mixed bioreactors. We observe that input flows must be slower when inhomogeneity is considered. With inhomogeneity, optimal controls for well-mixed reactors may avoid the target. Abstract: In this work, we study optimal and suboptimal control strategies for the treatment of a polluted water resource by using aside a continuous bioreactor. The control consists in choosing the inlet volumetric flow rate for filling the bioreactor with contaminated water from a considered resource (lake, reservoir, water-table, …). The treated outflow returns to the resource. We tackle an optimization problem which aims to minimize the time needed to reach a prescribed minimal value of contamination in the resource by choosing the input flow. Next, we study the influence of inhomogeneities of concentrations in the bioreactor, considering a system based on partial differential equations which describe its dynamics. We show that applying the optimal feedback control derived for perfectly mixed bioreactor does not allow to reach the target with small diffusion parameters as it drives the bioreactor to washout (the bioreactor equilibrium with no biomass). In this case, a suboptimal feedback (which reaches the target in finite time) is obtained with the help of a Hybrid Genetic Algorithm. Furthermore, we consider that the fluid flow velocity of the water entering into the bioreactor follows either a uniform or a nonuniform profile, showing that the optimal volumetric flow rates obtained with the uniform profile are not optimal if the profile is nonuniform, even when high diffusion coefficients are considered in the model.