The principal aim of this study was the use and evaluation of dynamic modeling techniques to identify mathematical models for cereal drying rates and quality changes that best describe thin-layer malt drying data. Seven thin-layer malt drying experiments were performed at the Great Western Malting pilot facilities, Vancouver, WA. Malt moisture content, temperature, β-amylase activity, endo-barley-β-glucanase activity, and color were all monitored as the malt was dried with air at various temperature and relative humidity values. A constrained direct search optimization method was used to fit available drying, enzyme deactivation, and color formation models to the data obtained by minimizing the error between predicted and experimental values. Because the deactivation of β- amylase observed during the kiln experiments was less than the error involved in β-amylase measurement, β-amylase modeling efforts were dropped from the study. The end result is a computer simulation of the malt kilning process that can predict malt drying rates, color formation, and endo-barley-β-glucanase deactivation based on the drying air temperature, relative humidity, and time spent in the kiln. Further research is suggested towards modeling malt drying rates at high moisture contents, (above 40%) analysis of drying model applicability when drying conditions fall outside those encountered in this study, and development of assay procedures and models so that the fate of other important malt quality indicators during kiln drying can be predicted. / Graduation date: 1993