(707e) Superstructure-Based Optimization of Membrane-Based Carbon Capture Systems

This work aims to optimize the plant design, process configuration and operating conditions of a post-combustion membrane-based CO₂ capture plant. A new superstructure-based mathematical model is presented, modeling discrete and continuous decision variables to minimize the cost of electricity (COE). The proposed approach tackles a major issue in carbon capture and storage (CCS) for the power generation industry, involving the simultaneous optimization of process configurations and operating conditions (gas temperature, pressure ratio, sweep feed, etc.).

The development of efficient and cost-effective CCS systems is important to mitigate the CO₂ emission in fossil fuel-based power generation. Available technologies for post-combustion carbon capture include liquid-solvents, gas-permeation, and solid sorbent systems. Solvent and Sorbent – based systems can considered mature technologies for CCS, while membrane systems are generally less well-studied.

This work focuses on membrane-based systems for CCS applied to gas permeation, where the membranes with high CO₂ selectivity separate the CO₂ from other components in the flue gas. The main driving force of the process is the partial pressure between the feed and the permeate sides. Membrane systems often consist of multiple stages of membranes and compressors with intercooling. Most of the applications consider membrane models discretizing the membrane area and solving the separation problem for each membrane-section. This often results in a large-scale model typically analyzed through process simulation-based frameworks (ASPEN, gPROMs, etc.)¹ developed primarily for accurately modeling performance as opposed to optimization. Therefore, the number of configurations that can be optimized by conventional simulation-based frameworks is limited.

Significant efforts have been made for developing multi-stage systems, where previous studies focused on optimizing the number of stages and their operation^2,3, instead of analyzing advanced process configurations⁴. Advanced process configurations can lead to important savings in operating and capital costs. This work analyzes advanced process configurations while considering rigorous process models in a mathematical optimization framework.

A set of streams, and multiple processes (membrane stages, mixers, splitters, pumps and compression units with intercooling) is considered. Discrete design decisions determine the plant layout by selecting the optimal number of stages (where each stage consists of a membrane, compressor, pump, and heat exchanger units) and the process configuration. At the operating level, continuous decision variables involve the optimal flows, temperatures, concentrations, pressure ratio, and sweep feed. The proposed model is formulated as a mixed integer nonlinear programming model that minimized the cost of electricity to derive an optimal design and operation of a post-combustion carbon capture plant. The optimization of several process configurations led to lower CCS costs.

[1] Eslick J. C., Tong C., Lee A., Dowling A. W., Mebane D. S. (2015). Optimization under uncertainty with rigorous process models. Proceedings AIChE Annual Meeting 2015.

[2] Hasan M. M., Baliban R. C., Elia J. A., Floudas C. A. (2012). Modeling, Simulation, and Optimization of Postcombustion CO₂ Capture for Variable Feed Concentration and Flow Rate. 1. Chemical Absorption and Membrane Processes. Industrial and Engineering Chemistry Research. 51, 15642-15664.

[3] Arias A. M., Mussati M. C., Mores P. L., Scenna N. J. (2016). Optimization of multi-stage membrane systems for CO₂ capture from flue gas. International Journal of Greenhouse Gas Control. 53, 371-390.

[4] Merkel T. C., Lin H., Wei X., Baker R. (2010). Power plant post-combustion carbon dioxide capture: An opportunity for membranes. Journal of Membrane Science. 359, 126-139.