Capabilities and Limitations of Predictive Engineering Theories for Multicomponent Adsorption

Multicomponent adsorption of gas mixtures on diverse solid surfaces is important in many applications. However, there are still many questions on the practical applicability of the available theories, especially for polar systems. In this work, we consider three well-known theories suitable for the prediction of multicomponent adsorption with parameters obtained solely from correlating single gas/solid data. We have tested them over an extensive database with emphasis on polar systems (both gases and solids). The three theories are the multicomponent Langmuir, the ideal adsorbed solution theory (IAST), and the multicomponent potential adsorption theory (MPTA). We have not attempted to improve/modify the methods in any way but have used them in their original form, as the purpose of our work is to illustrate the capabilities and inherent limitations of the models for predicting multicomponent adsorption. We have ensured that the description of single gas/solid systems is as accurate as possible, but besides this, the calculations for multicomponent systems are straight predictions. The work revealed on one side that all three theories yield for some systems similar predictions, with IAST and MPTA performing overall better than the multicomponent Langmuir. On the other hand, it is also shown that all the three theories, despite the good results in some cases, have serious limitations particularly for water and to some extent also for certain polar solids. Both strengths and weaknesses of the three models are discussed

Ternary Vapor–Liquid Equilibrium Measurements and Modeling of Ethylene Glycol (1) + Water (2) + Methane (3) Systems at 6 and 12.5 MPa

Novel technologies in the field of subsea gas processing include the development of natural gas dehydration facilities, which may operate at high pressure due to their proximity to reservoirs. For the qualification and design of these processing units, ternary vapor–liquid equilibrium data are required to validate the thermodynamic models used in the design process. For this purpose, 16 new ternary data points were measured for ethylene glycol (1) + water (2) + methane (3) at 6.0 and 12.5 MPa with temperatures ranging from 288 to 323 K and glycol content above 90 wt %. Glycol in gas (<i>y</i>₁), water in gas (<i>y</i>₂), and methane solubility (<i>x</i>₃) were measured with relative experimental uncertainties (<i>u</i>_r(<i>x</i>) = <i>u</i>(<i>x</i>)/<i>|x|</i>) below 12%, depending on the type of data. The Cubic-Plus-Association (CPA) equation of state was used to model the data. Literature pure component and binary interaction parameters were used. It was found that the model provides a good qualitative description of the experimental data for <i>y</i>₁ and <i>y</i>₂, while a significant over-prediction occurs for <i>x</i>₃. The modeling errors for CPA ranged between 5–40% average absolute relative deviation

Vapor–Liquid Equilibrium Measurements and Cubic-Plus-Association Modeling of Triethylene Glycol + Water + Methane Systems at 6.0 and 12.5 MPa

As a part of a series of studies that aim to expand the experimental database used to assist the design of novel technologies in the field of subsea gas processing, 18 new vapor–liquid equilibrium data points were measured for the system triethylene glycol (1) + water (2) + methane (3) at 6.0 and 12.5 MPa, temperature range between 288 and 323 K, and a glycol content above 95 wt %. The new data include both gas [glycol (y1) and water (y2)] and liquid [methane solubility (x3)] phase composition, with relative experimental uncertainties below 18%. It was observed that one of the experimental data sets available in the literature is not in agreement with the experimental data measured in this work. Furthermore, an important target was to reevaluate the cubic-plus-association (CPA) equation-of-state modeling capability, which has been previously used for triethylene glycol–methane systems. CPA using a 4C association scheme for TEG and one interaction parameter per binary has provided a good description of the newly measured data, with the average absolute relative deviation ranging between 9 and 43%. Binary interaction parameters regressed solely from the corresponding binary data were used for all ternary predictions with CPA