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Richard D, Jang J, Çıtmacı B, Luo J, Canuso V, Korambath P, Morales-Leslie O, Davis JF, Malkani H, Christofides PD, Morales-Guio CG. Smart manufacturing inspired approach to research, development, and scale-up of electrified chemical manufacturing systems. iScience 2023; 26:106966. [PMID: 37378322 PMCID: PMC10291476 DOI: 10.1016/j.isci.2023.106966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2023] Open
Abstract
As renewable electricity becomes cost competitive with fossil fuel energy sources and environmental concerns increase, the transition to electrified chemical and fuel synthesis pathways becomes increasingly desirable. However, electrochemical systems have traditionally taken many decades to reach commercial scales. Difficulty in scaling up electrochemical synthesis processes comes primarily from difficulty in decoupling and controlling simultaneously the effects of intrinsic kinetics and charge, heat, and mass transport within electrochemical reactors. Tackling this issue efficiently requires a shift in research from an approach based on small datasets, to one where digitalization enables rapid collection and interpretation of large, well-parameterized datasets, using artificial intelligence (AI) and multi-scale modeling. In this perspective, we present an emerging research approach that is inspired by smart manufacturing (SM), to accelerate research, development, and scale-up of electrified chemical manufacturing processes. The value of this approach is demonstrated by its application toward the development of CO2 electrolyzers.
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Affiliation(s)
- Derek Richard
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Joonbaek Jang
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Berkay Çıtmacı
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Junwei Luo
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Vito Canuso
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Prakashan Korambath
- Office of Advanced Research Computing, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Olivia Morales-Leslie
- Office of Advanced Research Computing, University of California, Los Angeles, Los Angeles, CA 90095, USA
- CESMII, Los Angeles, CA 90095, USA
| | - James F. Davis
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Office of Advanced Research Computing, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | | | - Panagiotis D. Christofides
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Carlos G. Morales-Guio
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
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Çıtmacı B, Luo J, Jang JB, Morales-Guio CG, Christofides PD. Machine learning-based ethylene and carbon monoxide estimation, real-time optimization, and multivariable feedback control of an experimental electrochemical reactor. Chem Eng Res Des 2023. [DOI: 10.1016/j.cherd.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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