Publications (6)
Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives
Runze Lin, Junghui Chen, Lei Xie +1
In the context of Industry 4.0 and smart manufacturing, the field of process industry optimization and control is also undergoing a digital transformation. With the rise of Deep Re…
Multi-Mode Process Control Using Multi-Task Inverse Reinforcement Learning
Runze Lin, Junghui Chen, Biao Huang +2
In the era of Industry 4.0 and smart manufacturing, process systems engineering must adapt to digital transformation. While reinforcement learning offers a model-free approach to p…
Quantitative Stability for Minimizing Yamabe Metrics with minimal boundary
Runze Lin, Bao Yu
In this paper, we investigate the stability of minimizing Yamabe metrics on compact manifolds with boundary, in the sense introduced by Escobar. We show that if a function nearly m…
Iterative Learning Control-Informed Reinforcement Learning for Batch Process Control
Runze Lin, Ziqi Zhuo, Junghui Chen +2
A significant limitation of Deep Reinforcement Learning (DRL) is the stochastic uncertainty in actions generated during exploration-exploitation, which poses substantial safety ris…
Surrogate Empowered Sim2Real Transfer of Deep Reinforcement Learning for ORC Superheat Control
Runze Lin, Yangyang Luo, Xialai Wu +4
The Organic Rankine Cycle (ORC) is widely used in industrial waste heat recovery due to its simple structure and easy maintenance. However, in the context of smart manufacturing in…
Reinforcement Learning-Driven Plant-Wide Refinery Planning Using Model Decomposition
Zhouchang Li, Runze Lin, Hongye Su +1
In the era of smart manufacturing and Industry 4.0, the refining industry is evolving towards large-scale integration and flexible production systems. In response to these new dema…