This work introduces a novel methodology for real-time optimization (RTO) of process systems using reinforcement learning (RL), where optimal decisions in response to external …
We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse field Ising spin …
Mechanical ventilation has been widely implemented to alleviate poor indoor air quality (IAQ) in confined underground public facilities. However, due to time-varying IAQ properties …
M Metzger, G Polakow - IEEE Transactions on Industrial …, 2011 - ieeexplore.ieee.org
The agents and multiagent systems technology is actively researched by the academia and industrial community. However, the technology is particularly popular in the manufacturing …
L Zhu, Y Cui, G Takami, H Kanokogi… - Control Engineering …, 2020 - Elsevier
This paper explores a reinforcement learning (RL) approach that designs automatic control strategies in a large-scale chemical process control scenario as the first step for leveraging …
In this paper, a model-free deep reinforcement learning (DRL) strategy is presented with an artificial neural network (ANN) as reaction simulation environment, to obtain a fed-batch …
We devise a novel technique to control the shape of polymer molecular weight distributions (MWDs) in atom transfer radical polymerization (ATRP). This technique makes use of recent …
L Zhu, G Takami, M Kawahara, H Kanokogi… - Computers & Chemical …, 2022 - Elsevier
Modern process controllers necessitate high quality models and remedial system re- identification upon performance degradation. Reinforcement Learning (RL) can be a …