electrolyte nrtl
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Author(s):  
Xiaoshuai Yuan ◽  
Chin Feng Ng ◽  
Nikolic Heather ◽  
Kunlei Liu

A thermodynamically consistent model for the carbon dioxide (CO2) absorption in aqueous alkanolamine system is of great importance in the research and development of a CO2 capture process. To facilitate the development of thermodynamic models, linear Gibbs free energy, enthalpy, and heat capacity relationships using well-known amines as reference are used to correlate the standard reference state properties of ionic species with those of molecular species in the electrolyte system, which has been approved to provide a reliable and consistent way to estimate required parameters when there is minimal or no appropriate experimental data available. The proposed relationships have been applied to the development of an electrolyte Non-Random Two Liquid (NRTL) activity coefficient model for CO2 absorption in aqueous 1-amino-2-propanol (A2P) solution, as an example to demonstration the methodology. With limited vapor-liquid equilibrium data and other thermodynamic properties, the parameters in the electrolyte NRTL model are identified with good accuracy.


2021 ◽  
Vol 329 ◽  
pp. 115524
Author(s):  
Yu-Jeng Lin ◽  
Nazir Hossain ◽  
Chau-Chyun Chen

Author(s):  
Chunyan Ma ◽  
Yanxin Wang ◽  
Yunhao Sun ◽  
Xiaohua Lu ◽  
Xiaoyan Ji

In this work, the excess Gibbs free energy models, i.e., non-random two-liquid (NRTL) model, electrolyte NRTL model, and electrolyte NRTL model including new strategies (association or hydration), were used to describe the macroscale properties and interpret the microstructure, clarifying the role of association and hydration in model development, and the enthalpy of mixing of three imidazolium-based IL-H2O systems containing the same cation but different sizes of anions, i.e., Cl−, Br−, and I− were measured for the first time to provide systematic data for model development. The models were developed and evaluated based on the newly measured data and the osmotic coefficient from the literature. The model reflecting the intrinsic mechanism of dissociation and hydration competition gives the best modeling results. The real ionic strength predicted from the identified model was quantitatively correlated with the electrical conductivities.


2019 ◽  
Vol 272 ◽  
pp. 01004 ◽  
Author(s):  
Eni Oko ◽  
Toluleke E. Akinola ◽  
Chin-Hung Cheng ◽  
Meihong Wang ◽  
Jian Chen ◽  
...  

The solvent-based carbon capture process is the most matured and economical route for decarbonizing the power sector. In this process, aqueous monoethanolamine (MEA) is commonly used as the solvent for CO2 scrubbing from power plant and industrial flue gases. Generally, aqueous MEA with 30 wt% (or less) concentration is considered the benchmark solvent. The CO2 solubility data in aqueous MEA solution, used for modelling of the vapour-liquid equilibria (VLE) of CO2 in MEA solutions, are widely published for 30 wt% (or less) concentration. Aqueous MEA with higher concentrations (from 40 to 100 wt%) is considered in solvent-based carbon capture designs with techniques involving process intensification (PI). PI techniques could improve the process economics and operability of solvent-based carbon capture. Developing PI for application in capture process requires CO2 solubility data for concentrated MEA solutions. These data are however limited in literature. The modelling of the vapour-liquid equilibria (VLE) of CO2 in MEA solutions for PI-based solvent capture techniques involving stronger MEA solution of about 80 wt% concentration requires solubility data at the concentration. In this study, the data for 80 wt% MEA is presented for 40,60, 100 and 120oC. The experimental technique and analytical procedure in this study were validated by comparing the measurements for 30 wt% MEA with data from the literature. The data from this study can be fitted to VLE models such as electrolyte NRTL, extended UNIQUAC etc. which is an important component of solvent-based capture model using MEA as the solvent. More accurate VLE models will improve the prediction accuracy of capture level, rich loading etc. using PI-based solvent-based capture model.


2018 ◽  
Vol 125 ◽  
pp. 159-171 ◽  
Author(s):  
Sina Hassanjani Saravi ◽  
Soraya Honarparvar ◽  
Chau-Chyun Chen

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