(479e) Integrated Solvent and Chemical Absorption Process Design for the Separation of CO2 from Flue Gas | AIChE

(479e) Integrated Solvent and Chemical Absorption Process Design for the Separation of CO2 from Flue Gas


Lee, Y. S. - Presenter, Imperial College London
Galindo, A., Imperial College London
Jackson, G., Imperial College London
Adjiman, C. S., Imperial College London
Carbon capture and storage (CCS) technologies play a vital role in response to the growing demand for carbon dioxide (CO2) removal. Currently, the chemical absorption of CO2 using amine-based solvents is regarded as one of the most promising techniques due to the versatility of the implementation of the process across a wide range of industrial applications [1]. The conventional amine-based solvents used for this process are monoethanolamine (MEA), diethanolamine (DEA), and methyldiethanolamine (MDEA). However, there are several challenges for the use of such solvents in the CO2 capture process, so that achieving high performance is hindered — for example, the high energy requirement for solvent regeneration; the low absorption capacity of many solvents; the high environmental and health impacts associated with solvent degradation, corrosivity, and solvent loss. To overcome these drawbacks, substantial research efforts have been undertaken to develop new solvents in order to improve the overall performance of the process. The identification of potential solvents, however, is very challenging due to (1) the combinatorial complexity derived from both solvent parameters and process conditions; (2) the strong mutual interaction between the molecular-level decisions and processes that need to be considered simultaneously [2].

In this context, Computer-Aided Molecular and Process Design (CAMPD) has emerged as a powerful and systematic technique that can accelerate the identification of molecule candidates by making it possible to explore in silico a very large space of possibilities. In CAMPD approaches, not only is the inter-dependency between the properties of the molecule and the process performance captured, but it is also possible to assess the optimal overall performance of a process as a function of the fluid [3]. Given the importance of CAMPD, a variety of solution methods have been developed to handle the complexities that arise from the large number of possible molecules and from the inherent non-linearity and non-convexity of structure-property and process models. However, most algorithms are prone to failing to generate a feasible solution when the integrated solvent-process model entails a large-scale mixed-integer nonlinear formulation and more importantly, when a significant portion of the search space is infeasible. Identifying the feasible range of process variables, which would make it possible to avoid infeasibilities, is difficult because it requires knowledge of the properties of the solvents and their phase behaviour in the process a priori. In addition it may be entirely infeasible to satisfy separation requirements with particular solvent candidates. Thus, the development of a robust algorithm that allows the exploration of a large design space without unnecessary difficulties is essential.

In this work, a robust optimization framework for the integrated design of an optimal aqueous solvent and CO2 chemical absorption processes is presented. The algorithm incorporates tailored feasibility tests [4] into an outer-approximation method [5] for the solution of CAMPD problems. The design of the feasibility tests focuses on recognizing the feasible domain based on: (1) physicochemical properties of the pure solvent; (2) analysis of the phase behaviour of mixtures of the solvent, water and carbon dioxide carried out using the Helmholtz free Energy Lagrangian Dual (HELD) algorithm [6]; (3) capability of solvent mixture to meet the target separation degree. The proposed optimization approach provides a reliable way to converge to an optimal solution by removing infeasible process conditions and molecular structures from the search space, and to reduce the computational cost by updating constraints on the operating conditions automatically.

The efficiency of the proposed algorithm is highlighted through two different case studies of CO2 chemical absorption processes. A process model is developed and the SAFT-γ-Mie group contribution equation of state [7] is applied to facilitate the reliable prediction of physicochemical interactions in the water-solvent-CO2 mixtures. For each case study, an economic criterion is used to evaluate the performance of the solvent/process and identify optimal designs.

[1] Rochelle, G.T., 2009. Amine scrubbing for CO2 capture. Science, 325 (5948), pp.1652-1654.

[2] Papadopoulos, A.I., Badr, S., Chremos, A., Forte, E., Zarogiannis, T., Seferlis, P., Papadokonstantakis, S., Galindo, A., Jackson, G. and Adjiman, C.S., 2016. Computer-aided molecular design and selection of CO 2 capture solvents based on thermodynamics, reactivity and sustainability. Molecular Systems Design & Engineering, 1(3), pp.313-334.

[3] 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.

[4] 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

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

[6] Pereira, F.E., Jackson, G., Galindo, A. and Adjiman, C.S., 2012. The HELD algorithm for multicomponent, multiphase equilibrium calculations with generic equations of state. Computers & chemical engineering, 36, pp.99-118.

[7] 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.