Deep reinforcement learning approach to optimize the driving performance of shield tunnelling machines
This paper proposes a deep reinforcement learning (DRL)-based model as a valuable tool
to improve the performance of the driving system (ie thrust force and cutterhead torque) of a …
to improve the performance of the driving system (ie thrust force and cutterhead torque) of a …
Real-time optimization using reinforcement learning
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 …
systems using reinforcement learning (RL), where optimal decisions in response to external …
Reinforcement learning using quantum Boltzmann machines
We investigate whether quantum annealers with select chip layouts can outperform classical
computers in reinforcement learning tasks. We associate a transverse field Ising spin …
computers in reinforcement learning tasks. We associate a transverse field Ising spin …
A deep reinforcement learning-based autonomous ventilation control system for smart indoor air quality management in a subway station
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 …
(IAQ) in confined underground public facilities. However, due to time-varying IAQ properties …
A survey on applications of agent technology in industrial process control
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 …
industrial community. However, the technology is particularly popular in the manufacturing …
[HTML][HTML] Scalable reinforcement learning for plant-wide control of vinyl acetate monomer process
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 …
strategies in a large-scale chemical process control scenario as the first step for leveraging …
Machine-learning-based simulation and fed-batch control of cyanobacterial-phycocyanin production in Plectonema by artificial neural network and deep reinforcement …
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 …
artificial neural network (ANN) as reaction simulation environment, to obtain a fed-batch …
Tuning the molecular weight distribution from atom transfer radical polymerization using deep reinforcement learning
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 …
(MWDs) in atom transfer radical polymerization (ATRP). This technique makes use of recent …
Alleviating parameter-tuning burden in reinforcement learning for large-scale process control
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 …
identification upon performance degradation. Reinforcement Learning (RL) can be a …