How Can (or Why Should) Process Engineering Aid the Screening and Discovery of Solid Sorbents for CO2 Capture?

ConspectusAdsorption using solid sorbents is emerging as a serious contender to amine-based liquid absorption for postcombustion CO2 capture. In the last 20+ years, significant efforts have been invested in developing adsorption processes for CO2 capture. In particular, significant efforts have been invested in developing new adsorbents for this application. These efforts have led to the generation of hundreds of thousands of (hypothetical and real) adsorbents, e.g., zeolites and metal-organic frameworks (MOFs). Identifying the right adsorbent for CO2 capture remains a challenging task. Most studies are focused on identifying adsorbents based on certain adsorption metrics. Recent studies have demonstrated that the performance of an adsorbent is intimately linked to the process in which it is deployed. Any meaningful screening should thus consider the complexity of the process. However, simulation and optimization of adsorption processes are computationally intensive, as they constitute the simultaneous propagation of heat and mass transfer fronts; the process is cyclic, and there are no straightforward design tools, thereby making large-scale process-informed screening of sorbents prohibitive.This Account discusses four papers that develop computational methods to incorporate process-based evaluation for both bottom-up (chemistry to engineering) screening problems and top-down (engineering to chemistry) inverse problems. We discuss the development of the machine-assisted adsorption process learning and emulation (MAPLE) framework, a surrogate model based on deep artificial neural networks (ANNs) that can predict process-level performance by considering both process and material inputs. The framework, which has been experimentally validated, allows for reliable, process-informed screening of large adsorbent databases. We then discuss how process engineering tools can be used beyond adsorbent screening, i.e., to estimate the practically achievable performance and cost limits of pressure vacuum swing adsorption (PVSA) processes should the ideal bespoke adsorbent be made. These studies show what conditions stand-alone PVSA processes are attractive and when they should not be considered. Finally, recent developments in physics-informed neural networks (PINNS) enable the rapid solution of complex partial differential equations, providing tools to potentially identify optimal cycle configurations. Ultimately, we provide areas where further developments are required and emphasize the need for strong collaborations between chemists and chemical engineers to move rapidly from discovery to field trials, as we do not have much time to fulfill commitments to net-zero targets.

Is Carbon Capture and Storage (CCS) Really So Expensive? An Analysis of Cascading Costs and CO2 Emissions Reduction of Industrial CCS Implementation on the Construction of a Bridge.

Carbon capture and storage (CCS) is an essential technology to mitigate global CO2 emissions from power and industry sectors. Despite the increasing recognition of its importance to achieve the net-zero target, current CCS deployment is far behind targeted ambitions. A key reason is that CCS is often perceived as too expensive. The costs of CCS have however traditionally been looked at from the industrial plant perspective, which does not necessarily reflect the end user's one. This paper addresses the incomplete view by investigating the impact of implementing CCS in industrial facilities on the overall costs and CO2 emissions of end-user products and services. As an example, we examine the extent to which an increase in costs of raw materials (cement and steel) due to CCS impacts the costs of building a bridge. Results show that although CCS significantly increases cement and steel costs, the subsequent increment in the overall bridge construction cost remains marginal (∼1%). This 1% cost increase, however, enables a deep reduction in CO2 emissions (∼51%) associated with the bridge construction. Although more research is needed in this area, this work is the first step to a better understanding of the real cost and benefits of CCS.

Prediction of MOF Performance in Vacuum Swing Adsorption Systems for Postcombustion CO2 Capture Based on Integrated Molecular Simulations, Process Optimizations, and Machine Learning Models.

Postcombustion CO2 capture and storage (CCS) is a key technological approach to reducing greenhouse gas emission while we transition to carbon-free energy production. However, current solvent-based CO2 capture processes are considered too energetically expensive for widespread deployment. Vacuum swing adsorption (VSA) is a low-energy CCS that has the potential for industrial implementation if the right sorbents can be found. Metal-organic framework (MOF) materials are often promoted as sorbents for low-energy CCS by highlighting select adsorption properties without a clear understanding of how they perform in real-world VSA processes. In this work, atomistic simulations have been fully integrated with a detailed VSA simulator, validated at the pilot scale, to screen 1632 experimentally characterized MOFs. A total of 482 materials were found to meet the 95% CO2 purity and 90% CO2 recovery targets (95/90-PRTs)-365 of which have parasitic energies below that of solvent-based capture (∼290 kWhe/MT CO2) with a low value of 217 kWhe/MT CO2. Machine learning models were developed using common adsorption metrics to predict a material's ability to meet the 95/90-PRT with an overall prediction accuracy of 91%. It was found that accurate parasitic energy and productivity estimates of a VSA process require full process simulations.

Can a computer "learn" nonlinear chromatography?: Physics-based deep neural networks for simulation and optimization of chromatographic processes.

The design and optimization of chromatographic processes is essential for enabling efficient separations. To this end, hyperbolic partial differential equations (PDEs) along with nonlinear adsorption isotherms must be solved using computationally expensive numerical solvers to understand, simulate, and design the complex behavior of solute movement in chromatographic columns. In this study, physics-based artificial neural network framework for adsorption and chromatography emulation (PANACHE) is used to simulate and optimize chromatographic processes in a computationally faster and reliable manner. The proposed approach relies on learning the underlying PDEs in the form of a physics-constrained loss function to improve the accuracy of process simulations. The effectiveness of this approach is demonstrated by considering the complex dynamics of binary solute mixtures for generic pulse injections subjected to different isotherm systems, namely, the four cases of the generalized Langmuir isotherms. Unique neural network models were developed for each isotherm and the models accurately predicted the spatiotemporal concentrations of solute mixture in chromatographic columns for an arbitrary feed concentrations and injection volumes by facilitating up to 250 times computational speed-ups. Moreover, the neural network models were incorporated with process optimization routines to precisely determine the optimal injection volumes to enable baseline separation of solute components of the feed mixture.

Computational fluid dynamics study of viscous fingering in supercritical fluid chromatography.

Axi-symmetric numerical simulations are carried out to study the dynamics of a plug introduced through a mixed-stream injection in supercritical fluid chromatographic columns. The computational fluid dynamics model developed in this work takes into account both the hydrodynamics and adsorption equilibria to describe the phenomena of viscous fingering and plug effect that contribute to peak distortions in mixed-stream injections. The model was implemented into commercial computational fluid dynamics software using user-defined functions. The simulations describe the propagation of both the solute and modifier highlighting the interplay between the hydrodynamics and plug effect. The simulated peaks showed good agreement with experimental data published in the literature involving different injection volumes (5 µL, 50 µL, 1 mL and 2 mL) of flurbiprofen on Chiralpak AD-H column using a mobile phase of CO2 and methanol. The study demonstrates that while viscous fingering is the main source of peak distortions for large-volume injections (1 mL and 2 mL) it has negligible impact on small-volume injections (5 µL and 50 µL). Band broadening in small-volume injections arise mainly due to the plug effect.