油茶籽热风干燥特性及不同模型研究

Study on the Three-factor Characteristics of Hot-air Drying of Camellia Seed

应用穿流式热风干燥平台,探究在干燥过程中不同因素(温度、风速、堆积厚度)对油茶籽干燥效率的影响以及不同拟合模型对油茶籽热风干燥过程预测的优劣程度。同时,对同一批干燥条件下的油茶籽采用两种干燥模型对干燥过程进行拟合,分别建立干燥数学模型和BP神经网络模型。结果表明:热风温度是最重要的影响因素,堆积厚度次之。在温度(50、65、80℃)、风速(0.5、1、2m/s)和堆积厚度(2、5、10cm)条件下,运用BP神经网络模型和数学模型两个进行拟合效果比较,发现BP神经网络模型比数学模型能更好地适应油茶籽的干燥过程,BP神经网络模型计算的中间参数相对于数学函数计算的参数更精确,说明BP模型能较准确地预测油茶籽三因素下的干燥过程及含水率。

Using the through-flow hot-air drying platform,explore the influence of different factors(temperature,wind speed,accumulation thickness)on the drying efficiency of Camellia oil seeds during the drying process.At the same time,different drying models are used to fit the drying process,and a three-factor drying mathematical model and a BP neural network model are established.The results showed that the temperature of hot air had a greater influence on the drying rate of Camellia oleifera seeds,followed by the thickness of the accumulation.Under the conditions of temperature(50,65,80℃),wind speed(0.5,1,2m/s)and accumulation thickness(2,5,10cm),the BP neural network model and the mathematical model are used to compare the fitting effects.It is found that the BP neural network model is better adapted to the drying process of camellia seeds than the mathematical model,indicating that the BP model can more accurately predict the drying process and moisture content of camellia seeds under the three factors.