Abstract
Process intensification can afford considerable benefits with respect to economics, sustainability and/or safety but also presents increased decision making challenges with respect to computational efficiency and flexibility across multiple temporal and spatial scales. Distributed decision making, that is, localized yet coordinated decision making among constituent subsystems, is a promising approach to alleviating these challenges. Determination of these subsystems is at the heart of the distributed paradigm. This paper gives a summary of recent developments and future directions in distributed decision making for intensified systems, specifically with respect to optimization, control and monitoring, with emphasis on methods for obtaining high quality decompositions for such problems based on network theory. It also discusses integrated renewable energy and chemical production, a new and promising domain of large-scale process intensification, in the context of systems engineering challenges and opportunities.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 75-81 |
| Number of pages | 7 |
| Journal | Current Opinion in Chemical Engineering |
| Volume | 25 |
| DOIs | |
| State | Published - Sep 2019 |
Bibliographical note
Funding Information:This work was funded in part by the National Science Foundation (NSF-CBET); in part by the Khalifa University of Science, Technology and Research, Abu Dhabi, UAE; in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000804; and in part by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Legislative-Citizen Commission on Minnesota Resources (LCCMR/ML 2015, CH 76, SEC 2, SUBD 07A). The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
Funding Information:
This work was funded in part by the National Science Foundation (NSF-CBET) ; in part by the Khalifa University of Science, Technology and Research, Abu Dhabi, UAE ; in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy , under Award Number DE-AR0000804; and in part by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Legislative-Citizen Commission on Minnesota Resources (LCCMR/ML 2015, CH 76, SEC 2, SUBD 07A). The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
Publisher Copyright:
© 2018 Elsevier Ltd
