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使用人工神经网络和遗传算法建模和优化三草生产

Milana Pribić¹, Ilija Kamenko², Saša Despotović³

¹Department of Biotechnology, Faculty of Technology Novi Sad, University of Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, Serbia.

Foods (Basel, Switzerland)

|January 26, 2024

概括

这项研究使用人工神经网络 (ANN) 和遗传算法 (GA) 优化了三草的生产. 这种方法成功地提高了 wort 提取物,粘度和自由氨基 (FAN) 含量,证明了其在造中的实际适用性.

科学领域:

造科学造科学
农业化学农业化学
过程优化过程优化

背景情况:

麦杂交的Triticale是一种有前途的造辅助品,因为它具有有利的特性.
优化三草生产需要了解各种加工参数之间的复杂相互作用.
优化造过程的传统方法可能耗时,可能无法捕捉到复杂的关系.

研究的目的:

模拟和优化三粉碎工艺,以改善汁生产.
评估三种,比例,酶度和粉碎法对特征的影响.
实现人工神经网络 (ANN) 和遗传算法 (GA),以优化流程.

主要方法:

在不同比例下研究了两种三种 (制和未制) .
应用了不同的粉碎方案和 Shearzyme® 500 L 酶的度.
使用ANN来建模压缩过程,并使用GA来优化关键变量.
通过实验室规模的混实验验验证了ANN模型的预测.

主要成果:

在GA中确定了三糖比率 (23%),酶比率 (9%),粉碎机制 (1) 和三糖品种 (3) 的最佳输入参数.
在优化条件下,ANN模型预测了 wort 提取物 (8.65%),粘度 (1.52 mPa·s) 和 FAN 含量 (148.32 mg/L).
实验验证与模型预测密切匹配, wort 提取物 (8.63%),粘度 (1.51 mPa·s) 和 FAN (148.88 mg/L).

结论:

综合的ANN和GA方法有效地模拟和优化了三 wort的生产.
该研究证实了计算方法用于提高造效率和质量的实际适用性.
优化酒的生产显示出酒行业的巨大潜力.

关键词:

副部长们是副部长.人工神经网络的人工神经网络遗传算法是一种遗传算法.粉碎粉碎粉碎粉碎粉碎三等级的三等级的三等级.

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