We propose a Computer Aided Molecular Design (CAMD) method which employs optimization to support the synthesis and selection of high performance molecules for use in process systems and to guide experimental efforts. The method can be used to address challenging applications where a) the desired molecules exhibit phase and chemical equilibrium, b) numerous combinations of molecules need to be evaluated, and c) multiple criteria must be considered to capture the effects of molecular chemistry on the process system performance. The method is applied to the design of solvents in chemical absorption processes for the separation of carbon dioxide (CO 2 ) from gas streams. The molecular design problem is first approached via a fast screening stage where molecules are evaluated based on the simultaneous consideration of multiple performance indices pertaining to thermodynamics, reactivity and sustainability. A few high-performance solvents are further evaluated using an advanced group contribution equation of state to predict reliably the highly non-ideal equilibrium behavior of solvent-water-CO 2 mixtures. Several promising novel solvents for CO 2 capture are proposed and can now be assessed experimentally. The proposed method can readily be applied to other chemical absorption processes to accelerate the identification of novel solvents.
AbstractThe identification of improved carbon dioxide (CO 2 ) capture solvents remains a challenge due to the vast number of potentially-suitable molecules. We propose an optimization-based computer-aided molecular design (CAMD) method to identify and select, from hundreds of thousands of possibilities, a few solvents of optimum performance for CO 2 chemisorption processes, as measured by a comprehensive set of criteria. The first stage of the approach consists in a fast screening stage where solvent structures are evaluated based on the simultaneous consideration of important pure component properties reflecting thermodynamic, kinetic, and sustainability behaviour. The impact of model uncertainty is considered through a systematic method that employs multiple models for the prediction of performance indices. In a second stage, high-performance solvents are further selected and evaluated using a more detailed thermodynamic model, namely the group-contribution statistical associating fluid theory for square well potentials (SAFT-γ SW), to predict accurately the highly non-ideal chemical and phase equilibrium of the solvent-water-CO 2 mixtures. The proposed CAMD method is applied to the design of novel molecular structures and to the screening of a dataset of commercially available amines. New molecular structures and commercially-available compounds that have received little attention as CO 2 capture solvents are successfully identified and assessed using the proposed approach. We recommend that these solvents given priority in experimental studies to identify new compounds.