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首页> 外文期刊>The Journal of Supercritical Fluids >Application of artificial neural network in predicting the extraction yield of essential oils of Diplotaenia cachrydifolia by supercritical fluid extraction
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Application of artificial neural network in predicting the extraction yield of essential oils of Diplotaenia cachrydifolia by supercritical fluid extraction

机译：人工神经网络在超临界流体萃取预测双枝叶挥发油提取率中的应用

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摘要

In this study, a three-layer artificial neural network (ANN) model was investigated to predict the extraction yield of essential oils from Diplotaenia cachrydifolia by supercritical fluid extraction. A multilayer feed-forward neural network trained with an error back-propagation algorithm was employed for developing a predictive model. The input parameters chosen of the model are pressure, temperature, extraction time and modifier volume while the extraction yield of essential oils is the output. The Levenberg-Marquardt (LM) algorithm was used to train ANN. The results showed that a network with five hidden neurons was highly accurate in predicting the extraction yield of essential oils of D. cachrydifolia. The mean squared error (MSE) and coefficient of determination (R2) between the actual and predicted values were determined as 0.0014 and 0.9983 for training, 0.0184 and 0.9542 for validation and 0.0221 and 0.9703 for testing date sets. The main-components that were extracted with SFE were dillapiole (30.2%), limonene (13.7%) and α-calacorene (20.1%).
机译：在这项研究中，研究了一种三层人工神经网络（ANN）模型，以预测超临界流体萃取从Diplotaenia cachrydifolia中提取精油的产量。利用错误反向传播算法训练的多层前馈神经网络用于开发预测模型。该模型选择的输入参数为压力，温度，萃取时间和改性剂体积，而精油的萃取产率为输出。 Levenberg-Marquardt（LM）算法用于训练ANN。结果表明，由五个隐藏的神经元组成的网络在预测杜鹃花精油的提取率方面非常准确。实际值和预测值之间的均方误差（MSE）和确定系数（R2）被确定为训练的0.0014和0.9983，确认的确定为0.0184和0.9542，测试日期集的确定为0.0221和0.9703。用SFE提取的主要成分是地拉比尔（30.2％），li烯（13.7％）和α-cal草烯（20.1％）。

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