Modelling and Dynamic Optimization of beer fermentation towards optimal flavor and operation

Abstract Beer production is an elaborate process, where, from the same critical ingredients, a greatly complex mixture of compounds is obtained depending on the operating conditions. The different combinations of these species, also called off- and on-flavors, are responsible for each beer’s distinctive taste. Improving any step of the beer production process will significantly influence the success of a brewery. Therefore, it is essential to carefully describe the beer production process’s different stages to optimize process conditions and beer flavor. To do so, in this work, a comprehensive fermentation model and optimization problem were formulated to determine how an up-to-date industrial beer production should optimally operate. To achieve this, a dynamic optimization problem using a Genetic Algorithm is formulated and implemented to maximize the conversion of substrate to ethanol and minimize fermentation time while setting the final acceptable concentrations of the by-products as strict constraints. Results show that each by-product affects beer flavor as well as process performance in a unique manner. Therefore, we believe that this work contributes to a comprehensive understanding of the impact of different off- and on-flavors on the optimal beer flavor profile and optimizing process conditions.