A coupled reinforcement learning and IDAES process modeling framework for automated conceptual design of energy and chemical systems-Reference-Cited by-同舟云学术

A coupled reinforcement learning and IDAES process modeling framework for automated conceptual design of energy and chemical systems

Published:2023 Issue:10 Volume:2 Page:1735-1751
ISSN:2753-1457
Container-title:Energy Advances
language:en
Short-container-title:Energy Adv.

Author:

Wang Dewei¹^ORCID,Bao Jie¹,Zamarripa-Perez Miguel A.²,Paul Brandon²^ORCID,Chen Yunxiang¹,Gao Peiyuan¹,Ma Tong¹³,Noring Alexander A.²,Iyengar Arun K. S.²,Schwartz Daniel T.⁴,Eggleton Erica E.⁴,He Qizhi¹⁵^ORCID,Liu Andrew¹,Marina Olga A.¹,Koeppel Brian¹,Xu Zhijie¹

Affiliation:

1. Pacific Northwest National Laboratory, Richland, WA, 99352, USA

2. National Energy Technology Laboratory, Pittsburgh, PA, 15236, USA

3. Northeastern University, Boston, MA, 02115, USA

4. University of Washington, Seattle, WA, 98195, USA

5. University of Minnesota, Minneapolis, MN, 55455, USA

Abstract

This study introduces an advanced automated system for designing diverse chemical or electrochemical systems, requiring minimal user expertise, and enabling designing and optimization from scratch.

Funder

Advanced Research Projects Agency - Energy

U.S. Department of Energy

Publisher

Royal Society of Chemistry (RSC)

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Fuel Technology,Renewable Energy, Sustainability and the Environment

Link

http://pubs.rsc.org/en/content/articlepdf/2023/YA/D3YA00310H

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