Machine learning in drying

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.