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食品干燥模型的最新进展:经验到多尺度物理信息的神经网络的经验
Aluth Durage Hiruni Tharaka Wijerathne1, Mohammad U H Joardder1,2, Zachary G Welsh1
1School of Mechanical, Medical, and Process Engineering, Faculty of Engineering, Queensland University of Technology (QUT), Brisbane, Queensland, Australia.
概括
了解食品干燥是改善粮食安全的关键. 本综述探讨了模拟技术,从实证到先进的物理信息神经网络 (PINN),以增强食品的保存和减少浪费.
科学领域:
- 食品科学 食品科学 食品科学
- 化学工程是化学工程的重要组成部分.
- 计算建模 计算建模
背景情况:
- 粮食不安全是一个全球性问题,食品保存技术如干燥对于提高粮食安全和减少浪费至关重要.
- 干燥可以从水果和蔬菜中去除大量的水分,但会导致复杂的结构变化,影响稳定性和质量.
- 精确建模这些干燥引起的变化对于优化过程至关重要.
研究的目的:
- 对传统的干燥建模技术进行全面的文献审查.
- 探索物理信息神经网络 (PINN) 模型在食品干燥方面的潜力.
- 确定克服当前干燥建模方法的局限性的策略.
主要方法:
- 对食品干燥的实证,基于物理的计算和数据驱动的机器学习模型的文献综述.
- 分析每个建模方法的优缺点.
- 探索混合PINN方法,将物理原理与机器学习相结合.
主要成果:
- 经验模型提供了简单性,但缺乏通用性和物理洞察力.
- 基于物理的模型提供高分辨率,但计算密集.
- 纯数据驱动的模型在计算上要求较低,但在稀疏的数据上扎.
- 皮恩模型是一个有前途的混合方法,将物理定律与数据驱动学习结合起来.
结论:
- 传统的干燥建模技术在准确性,概括性或计算成本方面存在固有的局限性.
- 皮恩模型为食品干燥模拟和优化提供了显著的进步潜力.
- 对PINN应用的进一步研究可以导致改善食品保存策略和减少浪费.
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