Rice drying synthesis is an essential operation that has to be done carefully and cost-effectively. Fast drying call cause fissuring, which lowers the market rice of the rice grains. Multipass drying systems are therefore used to bring the moisture content to desired levels gradually. To determine the best configuration of units and their corresponding operating conditions that maximize rice quality and minimize energy consumption, empirical models are used. However, empirical models have limited ranges of validity. Moreover, different mathematical models are possible for the same synthesis problem. This paper proposes it generalized disjunctive programming (GDP) framework for the synthesis problem of rice drying, ill order to increase the overall range of applicability of the empirical models and establish a consistent solution strategy. The proposed framework is investigated and tested oil several case studies. Different drying strategies resulted from solving the synthesis problem with different empirical models, providing us with a broader vision of the mechanism of rice drying processes. The results indicate that the GDP framework call facilitate the modelling of the synthesis problem and increase the efficiency of optimization algorithms.