Optimization of Corn Malt Drying by Use of a Genetic Algorithm

Abstract

This work aimed at the kinetic simulation and drying process optimization of corn malt using a genetic algorithm (GA) for estimation of temperature and time parameters, in order to maintain the maximum activity of α- and β-amylases enzymes in the obtained product. Thus, the germinated corn seeds were dried at 54–76°C in an air convective dryer, and from time to time the moisture content and enzymatic activity were measured. Simulation and optimization of the drying process was made by use of a GA method, an optimization technique inspired by the biological natural selection process. These experimental data were used to fit the models. Results showed that seeds were dried after 3–5 h of the drying process. Among the used models, the kinetic model of water diffusion into corn seeds showed the best fit. In addition, drying temperature and time showed an exponential influence on the enzymatic activity. Optimizations by GA showed that at 54°C and between 5.6 and 6.4 h of the drying process, values of specific activity of 5.26 ± 0.06 SKB/mg and 15.69 ± 0.10% of remaining moisture in corn malt were found.