Current facilities for CO2 capture from industrial gas streams use solvents based on an aqueous solution of monoethanolamine (MEA), whose primary disadvantage relates to the cost of their implementation. The project goal is to discover improved solvents by means of molecular modeling of the reactive absorption of CO2 in a large class of alternative aqueous alkanolamine solvents. This in silico approach is a more cost-effective pathway than the use of experiments alone, due to the time and expense of testing the large number of possibile solvent candidates.
Our methodology proceeds by developing mathematical models (force field models) describing the iinteractions of the molecules of the species involved, and constructing computationally efficient algorithms to model the equilibrium properties of the reactive absorption system. These models have a relatively small number of parameters, which can be estimated theoretically. Once constructed, given an appropriate computational algorithm they can be used to predict virtually any property of the solution. This makes the models truly predictive in principle, requiring little or no experimental data. The main properties of interest are the CO2 solubility in the solvent, its heat of absorption, and the viscosity of the CO2 loaded solvent.
The primary problem in implementing this approach is the lack of a computationally efficient algorithm to calculate the equilibrium composition of the complex reacting mixture in the dense liquid solutions that occur in the CO2 loaded solvent in equilibrium with its coexisting vapour phase. The project 's goal is to develop and implement new algorithms for this purpose. The results will be implemented in macroscopic thermodynamic models that can be rapidly implemented in chemical process simulation software for the design of the industrial CO2 capture processes.