(544c) Computer-Aided Molecular and Process Design: Optimal Solvent Design for CO2 Chemical Absorption Processes Using SAFT-?-Mie Equation of State

In response to the growing demand for carbon dioxide (CO₂) removal, the design of new solvents has been regarded as one of the most important tasks to achieve low energy requirement and better environment performance in post combustion CO₂ capture process by means of chemical absorption [1]. The identification of potential solvents is very challenging due to the combinatorial complexity derived from both solvent parameters and the significant influence of the choice of solvent on the process objectives, meaning that optimal solvents that are best in overall process performance can only be realised by capturing the interactions between the molecular- and process-level decisions simultaneously.

A promising research avenue to accelerate identification of new solvents is the development of Computer-Aided Molecular and Process Design (CAMPD) techniques by which very large
molecular and process domains are explored systematically [2]. In the field of CAMPD, a variety of solution methods have been developed to handle the complexities arising from the need to use large mixed-integer nonlinear structure-property and process models. However, many algorithms suffer from slow convergence or failure to converge to feasible solutions when the integrated solvent and process model renders a significant portion of the search space infeasible. As a result, most researchers have to date resorted to decomposition-based approaches or relied on simplifications or approximatioms of the design problem.

In this work, an optimization framework for the integrated design of an optimal aqueous solvent and CO₂ chemical absorption processes that offers robust convergence to optimal solutions is presented. An equilibrium-stage model that incorporates the SAFT-γ Mie group contribution approach [3] is proposed to provide an appropriate balance between accuracy and predictive capability across a large molecular design space. In order to facilitate the convergence behaviour of the process-molecular model, a tailored initialization strategy developed based on the inside-out algorithm [4] that can overcome the numerical difficulties arising from the complex mass– and heat–balance equations and the highly non-ideal behaviour of water-solvent CO₂ mixtures is introduced. Novel feasibility tests [5] that are capable of recognizing infeasible regions of molecular and process domains are developed and incorporated into an outer approximation framework [6] to increase solution robustness. The performance tests conducted for a set of solvents show that the proposed process model coupled with the initialization approach provides a reliable way of achieving convergence without the need for good initial guesses on process conditions. The efficiency of the proposed algorithm is further highlighted through three case studies of CO₂ chemical absorption processes. The successful completion of 150 optimisation runs demonstrates the robustness and efficiency of the proposed algorithm. The benefits of the simultaneous solution approach are highlighted by comparing the results with those produced by the conventional computer-aided molecular design (CAMD) approach and a decomposition-based CAMPD approach.

[1] Bui, M., Adjiman, C. S., Bardow, A., Anthony, E. J., Boston, A., Brown, S., Fennell, P. S., Fuss, S., Galindo, A., Hackett, L. A., et al., 2018. Carbon capture and storage (CCS): the way forward. Energy & Environmental Science, 11(5), 1062–1176.

[2] Adjiman, C.S., Galindo, A. and Jackson, G., 2014. Molecules matter: the expanding envelope of process design. In Computer Aided Chemical Engineering (Vol. 34, pp. 55-64). Elsevier.

[3] Papaioannou, V., Lafitte, T., Avendano, C., Adjiman, C.S., Jackson, G., Müller, E.A. and Galindo, A., 2014. Group contribution methodology based on the statistical associating fluid theory for heteronuclear molecules formed from Mie segments. The Journal of chemical physics, 140(5), p.054107.

[4] Russell, R., 1983. A flexible and reliable method solves single-tower and crude distillation-column problems. Chemical engineering (New York, NY), 90(21), 52–59.

[5] Gopinath, S., Jackson, G., Galindo, A., Adjiman, C.S., 2016. Outer approximation algorithm with physical domain reduction for computer-aided molecular and separation process design. AIChE Journal 62, 3484–3504

[6] Fletcher, R. and Leyffer, S., 1994. Solving mixed integer nonlinear programs by outer approximation. Mathematical programming, 66(1-3), pp.327-349.