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首页> 外文期刊>The Journal of Supercritical Fluids >Comparison between neural network and mathematical modeling of supercritical CO2 extraction of black pepper essential oil
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Comparison between neural network and mathematical modeling of supercritical CO2 extraction of black pepper essential oil

机译：黑胡椒精油超临界CO2萃取神经网络与数学模型的比较

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摘要

A feed-forward multi-layer neural network with Levenberg-Marquardt training algorithm was developed to predict yield for supercritical carbon dioxide extraction of black pepper essential oil. Since yield of extraction strongly depends on five independent variables including residence time, supercritical carbon dioxide temperature and pressure, particle size and supercritical carbon dioxide mass flux per unit mass of substratum, these five inputs were devoted to the network. Different networks were trained and tested with different network parameters using training and testing data sets. Using validating data set the network having the highest regression coefficient (r(2)) and the lowest mean square error was selected. To confirm the network generalization, an independent data set was used and the predictability of the network was statistically assessed. Statistical analyses showed that the neural network predictions had an excellent agreement (r(2) = 0.9698) with experimental data. Furthermore, a mass transfer based mathematical model was developed for constant rate period and diffusion-controlled regime of supercritical carbon dioxide extraction. The proposed model was numerically solved using modified Euler's and finite difference methods. Comparing predicted results of the neural network model and the mathematical model to experimental data indicated that the neural network model had better predictability than the mathematical model. (C) 2005 Elsevier B.V. All rights reserved.
机译：开发了一种采用Levenberg-Marquardt训练算法的前馈多层神经网络，以预测黑胡椒精油超临界二氧化碳萃取的收率。由于提取的收率在很大程度上取决于五个独立变量，包括停留时间，超临界二氧化碳温度和压力，粒径和每单位质量基质的超临界二氧化碳质量通量，因此这五个输入专门用于网络。使用训练和测试数据集，使用不同的网络参数对不同的网络进行了训练和测试。使用验证数据集，选择具有最高回归系数（r（2））和最低均方误差的网络。为了确认网络的一般性，使用了独立的数据集，并对网络的可预测性进行了统计评估。统计分析表明，神经网络预测与实验数据具有极好的一致性（r（2）= 0.9698）。此外，建立了基于传质的数学模型，用于恒定速率周期和超临界二氧化碳萃取的扩散控制方案。使用改进的欧拉法和有限差分法对提出的模型进行了数值求解。将神经网络模型和数学模型的预测结果与实验数据进行比较表明，神经网络模型比数学模型具有更好的可预测性。 （C）2005 Elsevier B.V.保留所有权利。

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