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

机译:自然随机进化算法在约束多目标啤酒发酵优化中的应用

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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.
机译:发酵是啤酒酿造中必不可少的步骤,由于过程阶段的耗时特性(持续时间> 120小时),啤酒通常是系统的瓶颈,在此过程中需要在可达到的乙醇浓度和所需的批处理时间之间进行权衡。为了探索这种折衷,我们采用了一种多目标植物繁殖算法(草莓算法),以识别温度操作以提高发酵性能。所采用的方法成功地产生了沿帕累托前沿存在的有利温度曲线族。这些输出配置文件的子集可以同时减少批处理时间并增加产物乙醇浓度,同时满足对发酵罐中产生的副产物的限制,与当前的工业实践相比,这代表了显着的改进。潜在的批处理时间减少了12小时以上,同时乙醇含量适度提高。

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