Optimization of hot air drying process of corn using genetic algorithm and response surface methodology

To study the effect of temperature and humidity on the drying indexes and quality indexes of corn and to optimize the corn drying process, an orthogonal experiment of hot air drying with variable temperature and humidity was designed. The drying process of the actual dryer was simulated by taking the gradient of hot air temperature (x) and the gradient of absolute humidity (y) as the experimental factors. The results showed that the drying indexes (drying time, average drying rate, accumulated temperature) reached optimal values under the drying conditions of cooling and dehumidifying; among them, the optimal value of x was 10 °C (50 °C, 40 °C, and 30 °C), and the optimal value of y was 8 g/m3 (25 g/m3, 17 g/m3, and 9 g/m3). The quality indexes (percentage of damage, fatty acid value, germination rate) reached the optimal values under the drying conditions of heating and humidifying; among them, the optimal value of y for the three quality indicators was 8 g/m3 (9 g/m3, 17 g/m3, and 25 g/m3), but the optimal value of x were different, the optimal value of x were 2.65 °C (39.35 °C, 42 °C, and 44.65 °C), 2.24 °C (39.76 °C, 42 °C, and 44.24 °C) and 6.33 °C (35.67 °C, 42 °C, and 48.33 °C). A genetic algorithm was used to transform the multi-objective function into one index, and the results show that: when x was 2.26°C (39.74°C, 42.00°C, and 44.26°C), y was 4.84 g/m3 (12.16 g/m3, 17.00 g/m3, and 21.84 g/m3), the comprehensive characteristics of the drying process can be optimized.