Intelligent Control of Drying with Machine Vision and Machine Learning

Thermal drying is a lengthy and energy consuming process that impacts product microstructure and quality. The challenge of the drying is how to achieve the best product quality with minimal energy expenses. Currently used drying methods assume constant temperature during the entire drying process, resulting in unnecessary energy losses and deterioration of product quality. The long-term goal of my research program is to provide a scientific foundation for automated control and optimization of drying to improve process efficiency and product quality. Due to the central role of product quality, the process optimization requires better understanding of the process-induced changes in product quality. Over the past 10 years, my research has been focused on the automated monitoring of quality attributes, such as texture, color, density, porosity, shrinkage and moisture content, using machine vision. As the result, it was discovered that thermal sensitivity of biomaterials depends on moisture content, which requires intelligent (adaptive) control. The objectives of this five-year project are: (i) to identify changes in thermal sensitivity of biomaterials during drying; (ii) to establish optimization criteria and control strategies to improve product quality. The first objective will include numerical study of drying factors on the quality of heat-sensitive biomaterials, such as fruits, vegetables, and mushrooms (Years 1-3). As a result, new knowledge on product quality transformations in drying will be acquired. The second objective will be achieved by synthesis of mathematical and inference models, which will link drying factors to specific quality attributes to establish criteria for optimization (Year 4). Optimization domain will be determined by using response surface methodology. This knowledge will be used in the design of an adaptive control system, which will allow continuous adjustment of drying conditions with respect to product quality (Year 5). The rationale for this research is to discover benefits of adaptive intelligent control compared to commonly used programmable drying. We expect that integration of machine vision and machine learning with a decision-making framework will significantly improve performance of drying system. The scientific outcome of this project is discovering the fundamental knowledge about the integration of machine vision and machine learning into intelligent control system. The novelty of the proposed research is the paradigm shift from pre-scheduled programmable drying to adaptive control to achieve desirable product characteristics. This pioneering research at the interface of machine vision, intelligent control and optimization will benefit engineering theory and create a stimulating environment for the training of many HQPs. The development of this novel technology will significantly contribute to a sustainable innovation portfolio in Canada by improving drying technologies and reducing carbon footprint.