Abstract
This article shows the results of heat and mass transfer coefficients estimation in a fluidized bed drying obtained through two independent metaheuristics, particle swarm optimization (PSO) and genetic algorithms (GAs). Upon estimating parameters, we aimed to minimize errors between the experimental data provided by Vitor (Modeling of Biomass Drying in Fluidized Bed, D.Sc. Thesis, 2003) and those calculated through a three-phase drying differential-algebraic model. The computational results showed that the two metaheuristics chosen were suitable to estimate the drying parameters proposed here. When the two metaheuristics are compared, the PSO shows slightly better results in much shorter computational times. The coefficient of heat transfer estimated here is compared to results obtained from other experiments and proves to be quite adequate.