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Modeling of Spearmint Oil Extraction in a Packed Bed Using SC-CO2

机译:使用SC-CO2在填充床中提取薄荷油的建模

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摘要

The packed bed extraction of spearmint oil using supercritical carbon dioxide was studied by a two-phase mass transfer model on the basis of desorption and diffusion. Unsteady-state mass balance for solute in supercritical and in solid phases led to two partial differential equations that were solved numerically using a linear equilibrium relationship. The model has four parameters, axial dispersion, mass transfer, and diffusion and desorption coefficients. Diffusion and desorption coefficients were used as the model tuning parameters and the others were predicted applying existing experimental correlations. The tuning parameters were calculated by the fitting error between 5 and 15% by the genetic algorithm method. In addition, this model was compared with a model that did not account for the desorption rate, according to the model suggested by Goodarznia and Eikani (G&E). Moreover, the effects of operational parameters such as pressures, temperatures, CO2 flow rates, and mean particle sizes on the extraction yield were evaluated. In order to obtain experimental data for spearmint oil, a facility was designed and constructed to conduct the experimental part of this study. The two models were also applied to the literature's experimental data for rosemary leaves, grape seeds, peanuts, and tomato seeds. Comparison of the results of the proposed model with results from the G&E model indicated that the proposed model had better predictability. Also, good agreement of the proposed model results and the experimental data confirmed the basic hypothesis of the model and the importance of the desorption rate.
机译:在解吸和扩散的基础上,通过两相传质模型研究了超临界二氧化碳填充床对薄荷油的萃取作用。溶质在超临界和固相中的非稳态质量平衡导致了两个偏微分方程,这些方程通过线性平衡关系进行数值求解。该模型具有四个参数,轴向分散,传质以及扩散和解吸系数。扩散系数和解吸系数用作模型调整参数,并使用现有的实验相关性预测其他参数。通过遗传算法在5%到15%之间的拟合误差计算出调节参数。此外,根据Goodarznia和Eikani(G&E)建议的模型,将该模型与不考虑解吸速率的模型进行了比较。此外,评估了操作参数(如压力,温度,CO2流量和平均粒径)对萃取收率的影响。为了获得留兰香油的实验数据,设计并建造了一个设施来进行这项研究的实验部分。这两个模型也被用于迷迭香叶,葡萄种子,花生和番茄种子的文献实验数据。将该模型的结果与G&E模型的结果进行比较表明,该模型具有更好的可预测性。而且,所提出的模型结果与实验数据的良好一致性证实了模型的基本假设以及解吸速率的重要性。

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