| 摘要: |
| 为探讨热风干燥技术对香菇的干燥效果，并建立最优的水分比预测模型，在干燥介质温度为45、50、55、60 ℃，相对湿度为25%、30%、35%、40%，风速为2、3、4、5 m/s，单位载重量为2、4、6、8 kg/m2的工况下，对香菇干燥过程中的干燥机理和工艺参数进行试验研究。利用试验得到的水分比数据，进行传统干燥动力学模型Lewis、Page、Modified Page、Henderson、Wang和人工神经网络干燥动力学模型研究，并用未参与拟合的数据对得到的模型分别进行验证。结果表明:1)当相对湿度由40%降低到25%时，干燥时间随之由12.5 h缩短到9.5 h，干燥速率和有效水分扩散系数随相对湿度的升高而减小；2)运用试验数据对干燥动力学模型进行拟合，发现Modifited Page模型为最佳香菇干燥动力学模型；3)基于粒子群算法优化建立了人工神经网络模型，并将其预测值和Modifited Page模型预测值与试验结果分别进行对比，发现人工神经网络模型预测值与试验值的平均相对误差仅为5.57%，较Modifited Page模型的平均相对误差16.57%更优。人工神经网络模型较传统拟合方法建模效率高，可以精确地描述干燥过程，为干燥过程提供较优的解决方案、操作条件和过程控制。 |
| 关键词: 香菇 热风干燥 含水率 干燥动力学模型 人工神经网络 预测模型 |
| DOI：10.11841/j.issn.1007-4333.2022.04.12 |
| 投稿时间：2021-08-07 |
| 基金项目:北京石油化工学院重要科研成果培育项目(BIPTACF-002)；北京市教委科研计划(KZ202110017026) |
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| Characteristics of Lentinus edodes hot-air drying and its kinetic model |
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YANG Yilin1,DING Junxiong1,WU Xiaohua1*,WANG Peng1,SUN Dongliang1,ZHANG Zhentao2,LI Dong3
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| (1. School of Mechanical Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China;2. Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China;3. College of Engineering, China Agricultural University, Beijing 100083, China) |
| Abstract: |
| In order to explore the drying characteristics of hot air drying technology on shiitake mushrooms and establish an optimal moisture ratio prediction model, the drying mechanism and process parameters in the drying process of shiitake mushrooms were experimentally studied under the temperatures of the drying medium 45, 50, 55 and 60 ℃, the relative humidity levels of 25%、30%、35%、40%, the wind speeds of 2, 3, 4 and 5 m/s and unit loads of 2, 4, 6, 8 kg/m2. Using the water ratio data obtained from the experiment, the traditional drying kinetic models, Lewis, Page, Modified Page, Henderson, Wang and the artificial neural network drying kinetic model were studied. The model developed was verified with the data that did not participate in the fitting. The results showed that: 1)When the relative humidity was reduced from 40% to 25%, the drying time was shortened from 12. 5 h to 9. 5 h, and the drying rate and effective moisture diffusion coefficient decrease with the increase of relative humidity; 2)The test data was fitted to the drying kinetic model, and it was found that the Modified Page model was the best mushroom drying kinetic model; 3)An artificial neural network model was established based on particle swarm optimization, and its predicted value and the Modified Page model predicted value were compared with the experimental values. It was found that the average relative error between the predicted value of the artificial neural network model and the experimental value was only 5. 57%, which was better than the 16. 57% average relative error by the Modified Page model. In conclusion, the artificial neural network model has higher modeling efficiency than traditional fitting methods. It can accurately describe the drying process, and provide better solutions, operating conditions and process control for the drying process. |
| Key words: shiitake hot air drying moisture content drying kinetic model artificial neural networks predictive model |
