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首页> 外文期刊>Hemijska industrija >A study of the extraction kinetics of the minerals from the white lady's bedstraw (Galium mollugo L.) by using an artificial neural network
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A study of the extraction kinetics of the minerals from the white lady's bedstraw (Galium mollugo L.) by using an artificial neural network

机译:利用人工神经网络研究白女士稻草(Galium mollugo L.)中矿物质的提取动力学

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The present work deals with the mineral composition of the extracts obtained from lady's bedstraw (Galium mollugo L.) by maceration, extraction under reflux (extraction at the boiling temperature of solvent) and ultrasonic extraction, using atomic absorption spectrometry. The main goals of the work were to evaluate the operation of an artificial neural network (ANN) of the selected topology, to determine the parameters of a kinetic model of unsteady diffusion of minerals through plant material, and to define the yield of minerals as a function of the time of extraction and the yield of total extract (resinoid). The ANN results showed a positive correlation with the experimental data, so they could be used to examine the kinetics of extraction of minerals from lady's bedstraw (G. mollugo), regardless of the extraction technique. The yield of minerals (K, Ca and Mg) was correlated with the time of extraction and the yield resinoid by a polynomial equation of the first order in both variables.
机译:本工作涉及通过浸软,从回流条件下萃取(在溶剂的沸腾温度下萃取)和超声波萃取(使用原子吸收光谱法)从夫人的稻草(甘草)中获得的萃取物的矿物成分。这项工作的主要目的是评估选定拓扑结构的人工神经网络(ANN)的运行,确定矿物在植物材料中不稳定扩散的动力学模型的参数,并将矿物的产量定义为提取时间和总提取物(类树脂)产量的函数。人工神经网络的结果与实验数据呈正相关,因此,无论采用何种提取技术,都可用于检验从女士稻草(G. mollugo)中提取矿物质的动力学。矿物质(K,Ca和Mg)的产量与提取时间和树脂类化合物的产量通过一级变量多项式方程相关。

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