Richard Derek, Jang Joonbaek, Çıtmacı Berkay, Luo Junwei, Canuso Vito, Korambath Prakashan, Morales-Leslie Olivia, Davis James F, Malkani Haresh, Christofides Panagiotis D, Morales-Guio Carlos G
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.
iScience. 2023 May 29;26(6):106966. doi: 10.1016/j.isci.2023.106966. eCollection 2023 Jun 16.
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 CO electrolyzers.
随着可再生电力在成本上与化石燃料能源具有竞争力，且环境问题日益受到关注，向电气化化学和燃料合成途径的转变变得越来越可取。然而，传统上电化学系统需要数十年才能达到商业规模。扩大电化学合成过程的难度主要源于难以在电化学反应器中同时解耦和控制本征动力学以及电荷、热量和质量传输的影响。要有效解决这个问题，需要将研究方法从小数据集方法转变为一种通过人工智能（AI）和多尺度建模实现数字化能够快速收集和解释大型、参数化良好的数据集的方法。从这个角度出发，我们提出一种受智能制造（SM）启发的新兴研究方法，以加速电气化化学制造过程的研究、开发和扩大规模。这种方法在用于开发CO电解槽方面的应用证明了其价值。
