Multiphysical field and multiobjective mathematical modeling of grain–oilseed storage: Current status and future trends

Abstract

Storage is an important process involved in the postharvest treatment of grain–oilseed and is necessary for maintaining high quality and ensuring the long-term supply of these commodities in the food industry. Proper storage practices help prevent spoilage, maintain nutritional value, and preserve marketable quality. It is of great interest for storage to investigate flow, heat and mass transfer processes, and quality change for optimizing the operation parameters and ensuring the quality of grain–oilseed. This review discusses the mathematical models developed and applied to describe the physical field, biological field, and quality change during the storage of grain–oilseed. The advantages, drawbacks, and industrial relevance of the existing mathematical models were also critically evaluated, and an organic system was constructed by correlating them. Finally, the future research trends of the mathematical models toward the development of multifield coupling models based on biological fields to control quality were presented to provide a reference for further directions on the application of numerical simulations in this area. Meanwhile, artificial intelligence (AI) can greatly enhance our understanding of the coupling relationships within grain–oilseed storage. AI's strengths in both qualitative and quantitative analysis, as well as its effectiveness, make it an invaluable tool for this purpose.