Modeling of in-bin reverse-airflow ear -maize drying: Optimizing capacity and cost

Maize is an important food and feed crop. An optimum harvest of maize requires high-quality seed. Seed-maize is harvested as ear maize at 30--35% (w.b.) moisture content in order to avoid field losses. For safe storage, it is necessary to reduce the moisture content to 12--13%. An ear-maize dryer is an in-bin dryer in which the airflow is reversed sometime during the drying cycle. The lack of automatic control of the drying-air temperature, the air-reversal time, and the total drying time often results in under- or over-drying. The goal of this study was to develop simulation and control models for maximizing drying capacity or minimizing drying cost of in-bin ear-maize dryers.;The ear-maize drying process was simulated using a modified version of the MSU in-bin drying model. Bed shrinkage was incorporated in the model as a function of drying time and moisture content. The In-Bin Reverse-Airflow Ear-Maize Drying Model was validated with experimental data from 24 experiments conducted at a commercial seed-maize processing facility.;The Complex Optimization Method was used to obtain the optimal values of the initial bed-depth, the up-air and the down-air temperatures, the up-air and down-air airflows, and the reversal moisture content for maximizing the capacity or for minimizing the cost of in-bin drying process at different initial moisture contents.;Comparing the drying model to the experimental data yielded standard errors of prediction of 2% for the transient local moisture content and 9 h for the total drying time. The wet-bulb temperature had the largest effect on the capacity and on the cost.;The optimization results showed that if a commercial 24-bin seed-maize dryer is operated under an optimum-capacity scenario, given 30% initial ear-maize moisture content, it will dry 24% more than when it is operated under minimum-cost control. However, the maximum-capacity option costs 36% more than the minimum-cost scenario.