Dual-Drive Window Control Method for Continuous Grain Drying Based on Water Potential Accumulation

In agricultural production, numerous nonlinear, time-lagged, and continuously disturbed deterministic processes require effective regulation. This study proposed a window AI control method driven by mechanism and data, which was studied and applied in continuous grain drying process based on the concepts of micro-environment absolute water potential and water potential accumulation. A control system for continuous grain drying was established based on absolute water potential accumulation, and three sets of experiments were conducted: constant temperature drying (water potential accumulation control window, hot air temperatures of three drying sections: 40 °C, 40 °C, 40 °C; relative humidity: 35–40%), increasing temperature drying (water potential accumulation control window, hot air temperatures of three drying sections: 35 °C, 40 °C, 45 °C; relative humidity: 35–40%), and a control experiment (equivalent accumulated temperature control window, hot air temperatures of three drying sections: 40 °C, 40 °C, 40 °C; relative humidity: 35–40%). The results showed that the outlet moisture content ranged from 15.08% to 15.86%, 15.30% to 15.91%, and 15.10% to 15.95%, respectively. The outlet moisture control accuracy ranged from −0.42% to 0.36%, −0.2% to 0.41%, and −0.4% to 0.45%, respectively. Analysis of grain quality indicators (damage percentage, germination percentage, fatty acid value) and microscopic structure revealed the following order: increasing temperature drying > constant temperature drying > control experiment. Compared with the control experiment adopting the equivalent accumulated temperature window control method, the proposed method exhibited higher control accuracy and stability. By integrating coupled temperature and humidity parameters into the variables, the quality of dried grains was effectively guaranteed.