Surrogate-Based Process Modeling Optimization: Application to Modular Carbon Capture System Via Vacuum Pressure Swing Adsorption

Post-combustion carbon capture has been proposed as a way to combat climate change and reduce carbon emission. There currently exist different technologies for carbon capture, but carbon capture is still associated with high cost and low energy efficiency [1]. One promising technology to overcome the aforementioned challenge is solid adsorption. Several adsorbent materials have been developed and evaluated for the adsorption-based technology [2]. Recent studies have determined that the performance of an adsorbent is inherently linked to the adsorption process. Therefore, the process configuration and the adsorbent should be considered simultaneously when designing the optimal solid adsorption system [3].

Optimization-based process design can be used to determine the optimal adsorption system by simultaneously considering the adsorbent and the process operating conditions. Accounting for all complex interactions between design decisions, optimization-based approach is superior to heuristics-based approach. However, one challenge of optimization-based process design is that the resulting optimization problem is difficult to solve when high-fidelity simulation is used to represent an adsorption system. Thus, traditional equation-based approach may not be viable. The use of surrogate-based optimization has been proposed to reduce the complexity of the optimization problem [4, 5]. Surrogate-based optimization involves replacing complex computer simulations with surrogate models; the surrogate model is then optimized to determine the best modular design.

In this work, we propose a surrogate-based optimization strategy for the design of a modular carbon capture system. We specifically focus on a Vacuum Pressure-Swing Adsorption (VPSA) cycle for a bed packed with thermally-modulated fiber composites [6]. The application of this class of structured contactor allows improved mass transfer, minimal pressure drop, and intrinsic thermal management, therefore intensifying carbon capture efficiency. We consider several existing adsorbents and determine the optimal modular design via the use of surrogate-based optimization. The performance of a modular design is analyzed by using metrics such as purity, recovery, energy requirement, and productivity. Furthermore, using Principal Component Analysis, we illustrate the relationship between adsorbent performance and process operation and demonstrate how we can gain insight into adsorbent performance.

1. Bollini, P., et al., Oxidative Degradation of Aminosilica Adsorbents Relevant to Postcombustion CO2 Capture. Energy & Fuels, 2011. 25(5): p. 2416-2425.
2. Leperi, K.T., et al., Development of a General Evaluation Metric for Rapid Screening of Adsorbent Materials for Postcombustion CO2 Capture. ACS Sustainable Chemistry & Engineering, 2019. 7(13): p. 11529-11539.
3. Subramanian Balashankar, V. and A. Rajendran, Process Optimization-Based Screening of Zeolites for Post-Combustion CO2 Capture by Vacuum Swing Adsorption. ACS Sustainable Chemistry & Engineering, 2019. 7(21): p. 17747-17755.
4. Boukouvala, F., M.M.F. Hasan, and C.A. Floudas, Global optimization of general constrained grey-box models: new method and its application to constrained PDEs for pressure swing adsorption. Journal of Global Optimization, 2017. 67(1): p. 3-42.
5. Kim, S.H. and F. Boukouvala, Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques. Optimization Letters, 2019.
6. DeWitt, S.J.A., et al., Development of Phase-Change-Based Thermally Modulated Fiber Sorbents. Industrial & Engineering Chemistry Research, 2019. 58(15): p. 5768-5776.