Abstract Although very important for analysis of drying processes, physics-based models are limited in terms of their prediction ability and in most cases are unsuitable for real-time process control and optimization of industrial drying. In this paper, we provide an overview of the machine learning (ML) techniques and the state-of-the-art ML applications in drying of food and biomaterials. The applications include but not limited to data-driven models, nonlinear control and multi-objective optimization. The advantages of integration of ML with machine vision for real-time observation of product quality and fine-tuning control strategies are briefly discussed. Future research should focus on the integration of ML software tools with sensors to measure process and product variables. In addition, the drying research community should contribute towards building of open-source datasets, which is extremely important to leverage the power of ML algorithms. Integration of sensors, process analysis and software engineering will enable the development of “intelligent” drying systems.

中文翻译：

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干燥中的机器学习

摘要 虽然物理模型对于干燥过程的分析非常重要，但其预测能力有限，在大多数情况下不适合工业干燥的实时过程控制和优化。在本文中，我们概述了机器学习 (ML) 技术和最先进的机器学习在食品和生物材料干燥方面的应用。应用包括但不限于数据驱动模型、非线性控制和多目标优化。简要讨论了 ML 与机器视觉集成在实时观察产品质量和微调控制策略方面的优势。未来的研究应该集中在 ML 软件工具与传感器的集成上，以测量过程和产品变量。此外，干燥研究社区应该为构建开源数据集做出贡献，这对于利用 ML 算法的力量非常重要。传感器、过程分析和软件工程的集成将使“智能”干燥系统的开发成为可能。