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首页> 外文期刊>Australian Journal of Crop Science >Modeling of sesame seed dehydration energy requirements by a soft-computing approach
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Modeling of sesame seed dehydration energy requirements by a soft-computing approach

机译：通过软计算方法对芝麻种子脱水能量需求进行建模

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Thermodynamic models and Soft-computing method of Neural Network (NN) were used for computation of sesame seed dehydration energy (heat and entropy). The NN method for prediction of the Equilibrium Moisture Content (EMC) of sesame seed was utilized. The heat of sorption of sesame seed is predicted by a mathematical model. After well training of the NN model, predictive power of the model was found to be high (R2=0.99). A regression model was also developed for prediction of entropy of sorption. At moisture content of about 11% (d.b.) the heat and entropy of sorption of sesame seed were decreased, smoothly, and they became highest at moisture content of about 8% (d.b.). Dehydration energy values of sesame seeds was very low compared with the other agricultural products. Computation of dehydration energy would be useful in the simulation of dried sesame seed storage.
机译：使用热力学模型和神经网络的软计算方法来计算芝麻脱水能（热量和熵）。利用神经网络方法预测芝麻的平衡水分含量（EMC）。芝麻的吸附热是通过数学模型预测的。在对NN模型进行良好训练之后，发现该模型的预测能力很高（R2 = 0.99）。还开发了回归模型来预测吸附熵。水分含量约11％（d.b.）时，芝麻的热量和吸附熵平稳降低，在水分含量约8％（d.b.）时最高。与其他农产品相比，芝麻的脱水能值非常低。脱水能量的计算将在模拟干芝麻种子存储中很有用。

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