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首页> 美国卫生研究院文献>Journal of Food Science and Technology >Microwave–vacuum drying of sour cherry: comparison of mathematical models and artificial neural networks
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Microwave–vacuum drying of sour cherry: comparison of mathematical models and artificial neural networks

机译：微波真空干燥酸樱桃：数学模型和人工神经网络的比较

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摘要

Drying characteristics of sour cherries were determined using microwave vacuum drier at various microwave powers (360, 600, 840, 1200 W) and absolute pressures (200, 400, 600, 800 mbars). In addition, using the artificial neural networks (ANN), trained by standard Back-Propagation algorithm, the effects of microwave power, pressure and drying time on moisture ratio (MR) and drying rate (DR) were investigated Based on the evaluation of experimental data fitting with semi-theoretical and empirical models, the Midilli et al. model was selected as the most appropriate one. Furthermore, the ANN model was able to predict the moisture ratio and drying rate quite well with determination coefficients (R2) of 0.9996, 0.9961 and 0.9958 for training, validation and testing, respectively. The prediction Mean Square Error of ANN was about 0.0003, 0.0071 and 0.0053 for training, validation and testing, respectively. This parameter signifies the difference between the desired outputs (as measured values) and the simulated values by the model. The good agreement between the experimental data and ANN model leads to the conclusion that the model adequately describes the drying behavior of sour cherries, in the range of operating conditions tested.
机译：使用微波真空干燥机在各种微波功率（360、600、840、1200 W）和绝对压力（200、400、600、800 mbar）下测定酸樱桃的干燥特性。此外，使用人工神经网络（ANN），通过标准反向传播算法训练，研究了微波功率，压力和干燥时间对水分比（MR）和干燥速率（DR）的影响。与半理论和经验模型拟合的数据，Midilli等。模型被选为最合适的模型。此外，人工神经网络模型能够很好地预测水分比和干燥速率，其训练，验证和测试的测定系数（R 2 ）分别为0.9996、0.9961和0.9958。对于训练，验证和测试，ANN的预测均方误差分别约为0.0003、0.0071和0.0053。此参数表示所需输出（作为测量值）与模型模拟值之间的差异。实验数据和人工神经网络模型之间的良好一致性得出结论，该模型充分描述了在测试的操作条件范围内酸樱桃的干燥行为。

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