Dynamic Scheduling of Ethylene Cracking Furnaces System Leveraging Deep Reinforcement Learning

The profitability and resilience of traditional scheduling algorithms for ethylene steam cracking systems in the face of supply chain fluctuations are comparatively weak. To address this issue, a dynamic ethylene scheduling framework based on deep reinforcement learning is proposed in this study. Th...

Full description

Saved in:
Bibliographic Details
Published inComputer Aided Chemical Engineering Vol. 53; pp. 2989 - 2994
Main Authors Li, Haoran, Wei, Yixin, Qiu, Tong
Format Book Chapter
LanguageEnglish
Published 2024
Subjects
deep reinforcement learning
dynamic scheduling
ethylene
dynamic scheduling
ethylene
deep reinforcement learning
Online AccessGet full text

Cover

More Information
Summary:The profitability and resilience of traditional scheduling algorithms for ethylene steam cracking systems in the face of supply chain fluctuations are comparatively weak. To address this issue, a dynamic ethylene scheduling framework based on deep reinforcement learning is proposed in this study. Through a comparative analysis with literature cases, this framework demonstrates a notable enhancement of 5.7% in daily revenue, showcasing strong resilience to supply chain fluctuations.
ISBN:9780443288241
0443288240
ISSN:1570-7946
DOI:10.1016/B978-0-443-28824-1.50499-3