(451e) Multi-Scale Modeling of Amine-Based Water-Free Solvents for CO2 Capture

The utility of aqueous amines for the chemical absorption of CO₂ has been well established over the past decades with its successful utilization in other separation applications. However, the transition of this technology for full-scale CO₂ capture systems remains hindered with the high solvent regeneration energy requirements due to the presence of large quantities of water in the solvent. A possible remedy is through either partially (water-lean) or fully (water-free) replacing water with alternative organic diluents with lower thermal properties than water [1]. These novel solvents have the potential of reducing the high regeneration energy requirements associated with the utilization of aqueous mines [2]. However, demonstration of the potentiality of these solvents remains limited to lab-scale application, focused on a limited set of performance criteria such as absorption capacity, and energy of regeneration. Additionally, the limited availability of experimental data required for detailed process modeling and simulation, hinders the techno-economic evaluation of these solvents to accurately assess their potential over their aqueous counterparts. Therefore, devising a consistent and reliable thermodynamic model capable of accurately describing the CO₂ solubility, thermophysical and transport properties of amine-based water-free solvents for representative techno-economic evaluation is highly needed.

In this contribution, we demonstrate the application of a multi-scale modeling framework to assess the potentiality of amine-based water free solvents for CO₂ capture. The multi-scale framework is built in a consistent manner establishing a direct link between the molecular structure of the solvent and its performance on industry-scale. The core of the multi-scale modeling framework relies on the application of a robust molecular-based equation of state (EoS), namely, soft-SAFT EoS [3,4], to provide all required thermodynamic information needed for an accurate process model and techno-economic evaluation, such as CO₂ solubility, solvent viscosity, density, surface tension, etc. Investigated solvents include combination of the chemical solvent monoethanolamine (MEA), with a wide array of organic diluents such as alcohols, glycols, glymes, and polar aprotic solvents. Molecular parameters of all the investigated substances are either taken from previous contributions or developed in this work and used in a transferable manner for describing the solubility of CO₂ in the selected solvents at conditions of relevance for industrial applications. The CO₂ chemisorption process in the examined solvents is done by using a scheme of implicit reactions to describe the formation of carbamate resulting from the chemical reactions between CO₂ and the examined amines [5,6]. This approach eliminates the need to specify detailed equilibrium reactions, while significantly reducing the number of parameters required to capture the CO₂ solubility, allowing the application of the model in a predictive manner over a broad range of conditions unavailable from experimental data.

Once these solvents were fully characterized using available experimental data, the thermodynamic model was used in a predictive manner to provide required thermodynamic information for detailed process model and simulation for a post-combustion capture unit. This enabled the assessment of the performance of these solvents in terms of technical indicators such as absorption capacity, and energy consumption, along with economic indicators inclusive of capital, operating costs, and total annualized costs.

The collection of these results demonstrates that once the robust molecular model is developed and validated, it can be used as a predictive tool, combined with process modeling and simulation, as a reliable tool to assess the techno-economic feasibility of alternative solvents for CO₂ capture, providing a direct link between micro-level features and industry-level performance.

This work is partially funded by Khalifa University of Science and Technology (RC2-2019-007). Computational resources from the Research and Innovation Center on CO₂ and H₂ (RICH Center) are gratefully acknowledged.

References

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