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

Abstract

On the application of a nature-inspired stochastic evolutionary algorithm to constrained multiobjective beer fermentation optimisation General rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately and investigate your claim. AbstractFermentation is an essential step in beer brewing, often acting as the system bottleneck due to the time consuming nature of the process stage (duration >120hrs), 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 hours has been highlighted, coupled with a moderate improvement in ethanol content.

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