On the application of a nature-inspired stochastic evolutionary algorithm to constrained multiobjective beer fermentation optimisation

tFermentation is an essential step in beer brewing, often acting as the system bottleneck due to the time-consuming nature of the process stage (duration >120 h), where a trade-off exists between attainableethanol concentration and required batch time. To explore this trade-off we employ a multi-objectiveplant propagation algorithm (the Strawberry algorithm), for identifying temperature manipulationsfor improved fermentation performance. The methodology employed successfully produces familiesof favourable temperature profiles which exist along the Pareto front. A subset of these output pro-files can simultaneously reduce batch time and increase product ethanol concentration while satisfyingconstraints on by-products produced in the fermenters, representing significant improvements incomparison with current industrial practice. A potential batch time reduction of over 12 h has beenhighlighted, coupled with a moderate improvement in ethanol content.