The consideration of different amine solvent mixtures is receiving increased attention in post combustion CO2 capture research to improve the chemical absorption/desorption process efficiency and costs. Binary amine mixtures are increasingly considered in aqueous solutions as CO2 capture solvents due to their simultaneously high performance in several important properties (e.g. reaction rate, CO2 capacity etc.). This is necessary to address shortcomings of aqueous solutions of single amines which present favorable behavior in few properties at the expense of strongly undesired effects in numerous others. The selection of the mixture composition (i.e. chemical and physical characteristics of the amines) and concentration (i.e. amount of each amine in the mixture) is clearly a very challenging problem due to a) the highly non-ideal mixture-water-CO2 interactions, b) the availability of countless combinations of amine molecules as potential mixture components and c) the need for combined consideration of several properties as criteria for the selection of mixtures with improved CO2 capture performance. The use of computer-aided tools can help address these challenges through models that enable accurate predictions of the desired mixture properties and systematic procedures to account for the combinatorial complexity. Although such tools have been successfully applied in the investigation of mixtures for non-CO2 capture applications, they have been considered to a very limited extent in the field of CO2 capture. The very few available published works focus on the development of rigorous mixture property prediction models which are indispensable for the accurate representation of mixture-water-CO2 interactions. However, the need to consider multiple mixture components and properties as mixture selection criteria requires intense computational effort and results in complexities that limit the selection to the consideration of very few options.
In this work we propose a systematic framework for the preliminary screening of binary amine mixtures as CO2 capture candidates considering several important properties as selection criteria. The proposed approach consists of several decision making stages which account for solvent-solvent, solvent-solvent-CO2 and solvent-solvent-water interactions using standard group contribution models as well as equations of state and activity coefficient models to account for the vapor and liquid phase non-idealities in several relevant properties. The properties considered as selection criteria capture the molecular chemistry effects on the absorption/desorption process and include mixture solubility, vapor pressure, viscosity, density, bubble point temperature, and melting point temperature. Reactivity with CO2 is also taken into account considering important empirical guidelines. The aim of the proposed approach is to be fast and sufficiently accurate in order to identify few useful amine combinations which may then be evaluated using rigorous prediction models or experiments, while quickly avoiding amine options of poor performance. Mixtures meeting specific performance criteria are considered in successive decision-making stages, hence a set of mixtures gradually emerges which consists of fewer but more effective CO2 capture candidates. The desired property criteria are calculated using predictive models based a) on their potential to reflect on important capture characteristics, b) on the availability of appropriate models for their calculation and c) on the availability of sufficient data so that these models may be applied in a wide range of molecular structures. A multi-criteria assessment methodology is combined with a systematic uncertainty quantification approach to unveil important trade-offs among several important properties and to propose the mixtures that appear to be promising as CO2 capture candidates. Uncertainty is addressed by considering multiple different models to calculate each mixture property employed as a selection criterion. Experimental data retrieved from literature are used to validate the employed property predictions models. The proposed method is applied in 200 mixtures resulting from numerous binary combinations of amines which have been previously investigated in their pure aqueous form as CO2 capture solvents. The obtained results indicate that the proposed framework unveils useful and valid insights regarding the interactions of different amine combinations in the presence of CO2 or water, despite the highly non-ideal mixture characteristics.
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