Analysis of Mass Transfer Performance of Monoethanolamine-Based CO2 Absorption in a Packed Column Using Artificial Neural Networks

详细信息    查看全文

• 作者：Kaiyun Fu ; Guangying Chen ; Zhiwu Liang ; Teerawat Sema ; Raphael Idem ; Paitoon Tontiwachwuthikul
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2014
• 出版时间：March 19, 2014
• 年：2014
• 卷：53
• 期：11
• 页码：4413-4423
• 全文大小：502K
• 年卷期：v.53,no.11(March 19, 2014)
• ISSN：1520-5045

文摘

Exact and reliable estimation of mass transfer performance is very important for the design, simulation, and optimization of CO₂ absorption in a packed column. In this study, two types of artificial neural networks (ANNs), namely back-propagation neural network and radial basis function network, were applied to predict the mass-transfer performance of CO₂ absorption into aqueous monoethanolamine (MEA) in packed columns (containing Berl saddles, Pall rings, IMTP random packing, and 4A Gempack, Sulzer DX structured packing, respectively) from input variables. These variables were inert gas flow rate, liquid flow rate, solution concentration, liquid CO₂ loading, CO₂ mole fraction, temperature, and total packing area, which were considered to predict the targeted output mass transfer variables. The predicted results from ANN were validated against experimental data as well as compared with results from well-known correlations in terms of the volumetric mass flux, CO₂ mole fraction, and temperature profiles along the height of the packed column. The comparisons between the predicted and experimental results showed that the proposed ANN models performed very well in predicting mass transfer performance of CO₂ absorption into aqueous MEA in a packed column.