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Total and partial uptakes of multicomponent vapor-gas mixtures on 13X zeolite at 343K: Experimental and modeling study

机译:343K沸石在13x沸石上的多组分气体混合物的总摄取,实验和建模研究

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In this work, GERG2008 EoS embedded in volumetric-gravimetric system was developed to allocate multicomponent partial/individual components uptakes in the mixture. The refined arrangement may overlay the current theoretical anticipated outcomes and interchange it with experimental and more trustworthy selective adsorption outcomes. 13X zeolite was utilized as a solid adsorbent for binary and ternary CO2:CH4:H2O mixtures adsorption. Premixed and preloaded water vapor was studied at 343 K and up to 10 bar. Artificial neural network (ANN) modeling was engaged to predict binary and ternary mixtures. ANN results disclosed decent promise with experimental data. Besides, simulated formations utilizing ANN model replicated high consistency. The testified outcomes magnificently identified particular components behavior in ternary and higher multicomponent mixtures. (C) 2017 Elsevier Inc. All rights reserved.
机译:在这项工作中,开发了在体积重量系统中嵌入体积重量系统的Gerg2008 EOS,以分配混合物中的多组分部分/单个组分。 精致的布置可以覆盖当前的理论预期结果并用实验和更可靠的选择性吸附结果汇编它。 将13x沸石用作二元和三元CO 2的固体吸附剂:CH 4:H 2 O混合物吸附。 在343 k和最多10巴中研究预混合和预载水蒸气。 人工神经网络(ANN)建模订婚以预测二元和三元混合物。 ANN结果披露了实验数据的体面承诺。 此外,利用ANN模型的模拟形成复制了高一致性。 作证结果略微鉴定了三元和更高的多组分混合物中的特定组分行为。 (c)2017年Elsevier Inc.保留所有权利。

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