Bakhshipour Adel, Zareiforoush Hemad, Bagheri Iraj
Department of Agricultural Mechanization Engineering Faculty of Agricultural Sciences University of Guilan Rasht Iran.
Food Sci Nutr. 2020 Nov 12;9(1):532-543. doi: 10.1002/fsn3.2022. eCollection 2021 Jan.
Drying characteristics of stevia leaves were investigated in an infrared (IR)-assisted continuous-flow hybrid solar dryer. Drying experiments were conducted at the inlet air temperatures of 30, 40, and 50°C, air inlet velocities of 7, 8, and 9 m/s, and IR lamp input powers of 0, 150, and 300 W. The results indicated that inlet air temperature and IR lamp input power had significant effect on drying time ( < .05). A comparative study was performed among mathematical, Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy System (ANFIS) models for predicting the experimental moisture ratio (MR) of stevia leaves during the drying process. The ANN model was the most accurate MR predictor with coefficient of determination (R), root mean squared error (RMSE), and chi-squared error (χ) values of 0.9995, 0.0005, and 0.0056, respectively, on test dataset. These values of the ANFIS model on test dataset were 0.9936, 0.0243, and 0.0202, respectively. Among the mathematical models, the Midilli model was the best-fitted model to experimental MR values in most of the drying conditions. It was concluded that artificial intelligence modeling is an effective approach for accurate prediction of the drying kinetics of stevia leaves in the continuous-flow IR-assisted hybrid solar dryer.
在一台红外（IR）辅助连续流混合太阳能干燥机中研究了甜叶菊叶片的干燥特性。在进气温度为30、40和50°C、进气速度为7、8和9米/秒以及红外灯输入功率为0、150和300瓦的条件下进行了干燥实验。结果表明，进气温度和红外灯输入功率对干燥时间有显著影响（<0.05）。对数学模型、人工神经网络（ANNs）模型和自适应神经模糊系统（ANFIS）模型进行了比较研究，以预测甜叶菊叶片在干燥过程中的实验含水率（MR）。在测试数据集上，ANN模型是最准确的MR预测模型，其决定系数（R）、均方根误差（RMSE）和卡方误差（χ）值分别为0.9995、0.0005和0.0056。测试数据集上ANFIS模型的这些值分别为0.9936、0.0243和0.0202。在数学模型中，Midilli模型在大多数干燥条件下是与实验MR值拟合最好的模型。得出的结论是，人工智能建模是准确预测连续流红外辅助混合太阳能干燥机中甜叶菊叶片干燥动力学的有效方法。
