Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform

Berkay Çıtmacı; Junwei Luo; Joon Baek Jang; Prakashan Korambath; Carlos G. Morales-Guio; James F. Davis; Panagiotis D. Christofides

Digital Chemical Engineering (Dec 2022)

Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform

Berkay Çıtmacı,
Junwei Luo,
Joon Baek Jang,
Prakashan Korambath,
Carlos G. Morales-Guio,
James F. Davis,
Panagiotis D. Christofides

Affiliations

Berkay Çıtmacı: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA
Junwei Luo: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA
Joon Baek Jang: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA
Prakashan Korambath: Office of Advanced Research Computing, University of California, Los Angeles, CA 90095-1592, USA
Carlos G. Morales-Guio: Co-corresponding author. Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA.; Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA
James F. Davis: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA; Office of Advanced Research Computing, University of California, Los Angeles, CA 90095-1592, USA
Panagiotis D. Christofides: Corresponding author at: Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095-1592, USA.; Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA; Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095-1592, USA

Journal volume & issue: Vol. 5
p. 100050

Abstract

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The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. However, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to collect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, monitoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an experimental electrochemical reactor that reduces carbon dioxide (CO2) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.

Published in Digital Chemical Engineering

ISSN: 2772-5081 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Technology: Chemical technology: Chemical engineering; Technology: Technology (General): Industrial engineering. Management engineering: Information technology
Website: https://www.journals.elsevier.com/digital-chemical-engineering

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