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How Can (or Why Should) Process Engineering Aid the Screening and Discovery of Solid Sorbents for CO2 Capture?
Arvind Rajendran1, Sai Gokul Subraveti1,2, Kasturi Nagesh Pai1,3
1Donadeo Innovation Centre for Engineering, University of Alberta, 9211-116 Street NW, Edmonton, AB T6G 1H9, Canada.
Developing computational methods, including the MAPLE framework and physics-informed neural networks, enables efficient, process-informed screening of solid sorbents for carbon dioxide (CO2) capture, accelerating the transition to net-zero targets. This approach integrates material properties with process performance for effective adsorbent selection.
Area of Science:
- Materials Science and Engineering
- Chemical Engineering
- Computational Chemistry
Background:
- Solid sorbent adsorption is a promising alternative to liquid amine absorption for post-combustion CO2 capture.
- Developing new adsorbents, such as zeolites and metal-organic frameworks (MOFs), has generated vast databases but identifying optimal materials remains challenging.
- Adsorbent performance is highly dependent on the deployment process, necessitating process-informed screening, which is computationally intensive.
Purpose of the Study:
- To discuss computational methods for process-based evaluation in adsorbent screening for CO2 capture.
- To present frameworks for both bottom-up (chemistry to engineering) and top-down (engineering to chemistry) screening.
- To highlight the integration of material properties and process performance for effective adsorbent selection and process design.
Main Methods:
- Development of the machine-assisted adsorption process learning and emulation (MAPLE) framework using deep artificial neural networks (ANNs) to predict process-level performance.
- Application of process engineering tools to evaluate the performance and cost limits of pressure vacuum swing adsorption (PVSA) processes.
- Utilizing physics-informed neural networks (PINNS) for rapid solution of complex partial differential equations to optimize adsorption cycles.
Main Results:
- The MAPLE framework provides a validated, reliable method for process-informed screening of large adsorbent databases.
- Process engineering tools can determine the viability and optimal conditions for PVSA processes, even with ideal adsorbents.
- PINNS offer potential for identifying optimal adsorption cycle configurations by efficiently solving complex simulations.
Conclusions:
- Computational methods integrating material science and process engineering are crucial for efficient adsorbent discovery and CO2 capture.
- Strong collaborations between chemists and chemical engineers are essential to accelerate the transition from laboratory discovery to field trials.
- These advancements are vital for meeting net-zero emission targets within the required timeframe.
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