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

Abstract Fermentation 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 attainable ethanol concentration and required batch time. To explore this trade-off we employ a multi-objective plant propagation algorithm (the Strawberry algorithm), for identifying temperature manipulations for improved fermentation performance. The methodology employed successfully produces families of favourable temperature profiles which exist along the Pareto front. A subset of these output profiles can simultaneously reduce batch time and increase product ethanol concentration while satisfying constraints on by-products produced in the fermenters, representing significant improvements in comparison with current industrial practice. A potential batch time reduction of over 12 h has been highlighted, coupled with a moderate improvement in ethanol content.

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