TY - JOUR
T1 - Deep reinforcement learning for process design
T2 - Review and perspective
AU - Gao, Qinghe
AU - Schweidtmann, Artur M.
PY - 2024
Y1 - 2024
N2 - The transformation toward renewable energy and feedstock supply in the chemical industry requires new conceptual process design approaches. Recently, deep reinforcement learning (RL), a subclass of machine learning, has shown the potential to solve complex decision-making problems and aid sustainable process design. However, its suitability in static process design still needs to be examined. We discuss the advantages and disadvantages of RL for process design. Then, we survey state-of-the-art research through three major elements: (1) information representation, (2) agent architecture, and (3) environment and reward. Moreover, we discuss perspectives on underlying challenges and promising future works to unfold the full potential of RL for process design in chemical engineering.
AB - The transformation toward renewable energy and feedstock supply in the chemical industry requires new conceptual process design approaches. Recently, deep reinforcement learning (RL), a subclass of machine learning, has shown the potential to solve complex decision-making problems and aid sustainable process design. However, its suitability in static process design still needs to be examined. We discuss the advantages and disadvantages of RL for process design. Then, we survey state-of-the-art research through three major elements: (1) information representation, (2) agent architecture, and (3) environment and reward. Moreover, we discuss perspectives on underlying challenges and promising future works to unfold the full potential of RL for process design in chemical engineering.
UR - http://www.scopus.com/inward/record.url?scp=85187954048&partnerID=8YFLogxK
U2 - 10.1016/j.coche.2024.101012
DO - 10.1016/j.coche.2024.101012
M3 - Review article
AN - SCOPUS:85187954048
SN - 2211-3398
VL - 44
JO - Current Opinion in Chemical Engineering
JF - Current Opinion in Chemical Engineering
M1 - 101012
ER -