关键词: 方捆干燥, 水分传递, 降水速率, 神经元网络, 干燥工艺, 感官品质

Abstract: Because the large harvest of second crop of alfalfa in the north and the meeting with the rainy season, the quality of storage process become worse due to the lack of drying time. The heat-air circulate drying technology can overcome the technical bottleneck of high cost, low efficiency and degraded quality of the bundle drying process. Combined with the heat-air circulate drying characteristics and the picking and bundling technology, this paper studied the reasonable drying technology parameters of the alfalfa bundles. The 5HY-Ⅱ heat-air test bench was used with the temperature and humidity sensor to detect the temperature and humidity of each point in the bundle to explore the temperature and moisture transfer rules in the bundles. Taking the appearance quality and unit energy consumption of the alfalfa as the evaluation index, the orthogonal test design method was used to determine the drying technology parameters of the high moisture alfalfa bundles. The test results indicated that the obtained experimental characteristics of the square bundle drying experiment are divided into preheating stage, constant speed drying stage and stable stage. According to the sensory evaluation of color and odor, the evaluation of the bundle length and the drying temperature and the sensory evaluation index were significant and negatively correlated. According to the unit energy consumption as the evaluation index, the length and density of the bundles had significant effects on the unit energy consumption index and were negatively correlated. Using the Neuro shell combined with the real economic benefit to predict the neural network, the optimal combination of parameters for the heat-air circulate drying technology was obtained, namely: the bundle length was 285.7 mm, the drying process parameter density was 238.38 kg/m3, the drying temperature was 60.48°C, the air velocity was 1.73 m/s, and the energy consumption was 0.203 kJ/kg H2O. The study of the predicted drying technology could be used as a guide for artificially assisted dry storage of alfalfa bundles.

Key words: bundle drying, moisture transfer, neural network, precipitation rate, drying technology, sensory quality