arXiv subjects

Runze Lin

Publications and source records attributed to Runze Lin.

6 recordsLinked to original sources

arXiv2026

Quantitative Stability for Minimizing Yamabe Metrics with minimal boundary

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 minimizes the Yamabe energy, then the associated conformal metric is quantitatively close to a minimizing Yamabe metric within its conformal class. Moreover, this closeness is controlled by an appropriate power of the Yamabe energy deficit.

math.DG↗

arXiv2026

Iterative Learning Control-Informed Reinforcement Learning for Batch Process Control

A significant limitation of Deep Reinforcement Learning (DRL) is the stochastic uncertainty in actions generated during exploration-exploitation, which poses substantial safety risks during both training and deployment. In industrial process control, the lack of formal stability and convergence guarantees further inhibits adoption of DRL methods by practitioners. Conversely, Iterative Learning Control (ILC) represents a well-established autonomous control methodology for repetitive systems, particularly in batch process optimization. ILC achieves desired control performance through iterative refinement of control laws, either between consecutive batches or within individual batches, to compensate for both repetitive and non-repetitive disturbances. This study introduces an Iterative Learning Control-Informed Reinforcement Learning (IL-CIRL) framework for training DRL controllers in dual-layer batch-to-batch and within-batch control architectures for batch processes. The proposed method incorporates Kalman filter-based state estimation within the iterative learning structure to guide DRL agents toward control policies that satisfy operational constraints and ensure stability guarantees. This approach enables the systematic design of DRL controllers for batch processes operating under multiple disturbance conditions.

eess.SY↗

arXiv2025

Multi-Mode Process Control Using Multi-Task Inverse Reinforcement Learning

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 process control, its applications are limited by the dependence on accurate digital twins and well-designed reward functions. To address these limitations, this paper introduces a novel framework that integrates inverse reinforcement learning (IRL) with multi-task learning for data-driven, multi-mode control design. Using historical closed-loop data as expert demonstrations, IRL extracts optimal reward functions and control policies. A latent-context variable is incorporated to distinguish modes, enabling the training of mode-specific controllers. Case studies on a continuous stirred tank reactor and a fed-batch bioreactor validate the effectiveness of this framework in handling multi-mode data and training adaptable controllers.

eess.SY↗

arXiv2025

Reinforcement Learning-Driven Plant-Wide Refinery Planning Using Model Decomposition

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 demands, this paper presents a novel optimization framework for plant-wide refinery planning, integrating model decomposition with deep reinforcement learning. The approach decomposes the complex large scale refinery optimization problem into manageable submodels, improving computational efficiency while preserving accuracy. A reinforcement learning-based pricing mechanism is introduced to generate pricing strategies for intermediate products, facilitating better coordination between submodels and enabling rapid responses to market changes. Three industrial case studies, covering both single-period and multi-period planning, demonstrate significant improvements in computational efficiency while ensuring refinery profitability.

eess.SY↗

arXiv2024

Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives

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 Reinforcement Learning (DRL), its application in process control has attracted widespread attention. However, the extremely low sample efficiency and the safety concerns caused by exploration in DRL hinder its practical implementation in industrial settings. Transfer learning offers an effective solution for DRL, enhancing its generalization and adaptability in multi-mode control scenarios. This paper provides insights into the use of DRL for process control from the perspective of transfer learning. We analyze the challenges of applying DRL in the process industry and the necessity of introducing transfer learning. Furthermore, recommendations and prospects are provided for future research directions on how transfer learning can be integrated with DRL to enhance process control. This paper aims to offer a set of promising, user-friendly, easy-to-implement, and scalable approaches to artificial intelligence-facilitated industrial control for scholars and engineers in the process industry.

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arXiv2023

Surrogate Empowered Sim2Real Transfer of Deep Reinforcement Learning for ORC Superheat Control

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 the process industry, traditional model-based optimization control methods are unable to adapt to the varying operating conditions of the ORC system or sudden changes in operating modes. Deep reinforcement learning (DRL) has significant advantages in situations with uncertainty as it directly achieves control objectives by interacting with the environment without requiring an explicit model of the controlled plant. Nevertheless, direct application of DRL to physical ORC systems presents unacceptable safety risks, and its generalization performance under model-plant mismatch is insufficient to support ORC control requirements. Therefore, this paper proposes a Sim2Real transfer learning-based DRL control method for ORC superheat control, which aims to provide a new simple, feasible, and user-friendly solution for energy system optimization control. Experimental results show that the proposed method greatly improves the training speed of DRL in ORC control problems and solves the generalization performance issue of the agent under multiple operating conditions through Sim2Real transfer.

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