Energy and moisture source-term based distributive (luikov) parameter model to simulate rapeseed hot-air drying

Efficient rapeseed drying is critical for preserving oil quality, reducing post-harvest losses and minimizing energy use during storage. However, variability in drying conditions often results in non-uniform moisture removal. This study aims to develop and validate an improved predictive model for hot-air drying of rapeseed by modifying the classical Luikov distributed parameter model (DPM). This study examines a modified Luikov-based DPM incorporating transient energy and moisture source terms to more accurately simulate the heat and mass transfer phenomena during rapeseed (Brassica napus L.) drying in a hot-air static bed dryer. Experiments were conducted at three drying air temperatures (40 °C, 50 °C and 60 °C) and three initial moisture contents (11 %, 16 % and 21 % dry basis). The modified model integrates the Clausius-Clapeyron-based differential heat of sorption that captures moisture dynamics and heat transfer more realistically than the conventional DPM.

Experimental results showed that increasing the drying air temperature significantly accelerated moisture removal, with the highest drying rates observed at 60 °C. The modified Luikov model was found to be 5 % more effective than the conventional DPM model in simulating the drying kinetics of rapeseed. The model accurately captured heat and mass transfer phenomena, closely correlating with experimental data, with an improved correlation coefficient (R²) of 97 %. These findings demonstrate the potential of the improved model to optimize drying conditions, reduce energy consumption, and maintain seed quality.