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CO 在单乙醇胺、2-氨基-2-甲基-1-丙醇和 2-(丁氨基)乙醇混合胺体系中的溶解度的实验研究与建模方法。

Experimental investigations and the modeling approach for CO solubility in aqueous blended amine systems of monoethanolamine, 2-amino-2-methyl-1-propanol, and 2-(butylamino)ethanol.

机构信息

Clean Energy Technologies Research Institute (CETRI), University of Regina, Regina, SK, Canada.

Department of Chemical Technology, Faculty of Science, Chulalongkorn University, Bangkok, Thailand.

出版信息

Environ Sci Pollut Res Int. 2022 Oct;29(46):69402-69423. doi: 10.1007/s11356-022-20411-x. Epub 2022 May 14.

DOI:10.1007/s11356-022-20411-x
PMID:35567679
Abstract

In this work, new CO solubility data on three types of aqueous amine blends were reported to complement existing databases. The experiments were conducted at temperatures of 313 K (absorption condition) and 363 K (desorption condition). The effect of the MEA concentration on the CO solubility in several amine blends at low CO partial pressure (8 to 50.65 kPa) were studied in this work, including 0.1, 0.3, 0.5 mol/L MEA + 2 mol/L AMP; 0.1, 0.3, 0.5 mol/L MEA + 2 mol/L BEA; and 0.1, 0.3, 0.5 mol/L MEA + 1, 2 mol/L AMP + 1, 2 mol/L BEA. Besides, an additional group of equilibrium CO solubility data were conducted at 298 K in order to estimate the heat of CO absorption of the blended solvents at a temperature range from 298 to 313 K. A new simplified Kent-Eisenberg model was developed for the predictions of blended solvents, and a multilayer neural network model with Levenberg-Marquardt backpropagation algorithm was developed upon five hundred reliable published experimental data. The predictions from two methods are both in good agreement with the experimental CO solubility data.

摘要

在这项工作中,报道了三种类型的水合胺混合物的新 CO 溶解度数据,以补充现有数据库。实验在 313 K(吸收条件)和 363 K(解吸条件)下进行。本工作研究了 MEA 浓度对几种胺混合物在低 CO 分压(8 至 50.65 kPa)下 CO 溶解度的影响,包括 0.1、0.3、0.5 mol/L MEA + 2 mol/L AMP;0.1、0.3、0.5 mol/L MEA + 2 mol/L BEA;以及 0.1、0.3、0.5 mol/L MEA + 1、2 mol/L AMP + 1、2 mol/L BEA。此外,还在 298 K 下进行了一组额外的平衡 CO 溶解度数据,以便估算混合溶剂在 298 至 313 K 温度范围内吸收 CO 的热。开发了一种新的简化肯特-爱森伯格模型来预测混合溶剂,并在五百个可靠的已发表实验数据的基础上,开发了一个具有列文伯格-马夸特反向传播算法的多层神经网络模型。两种方法的预测结果与实验 CO 溶解度数据吻合良好。

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