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https://doi.org/10.2139/ssrn.4664189

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inferredcites →https://doi.org/10.1002/aic.12672openalex-extracted
Economic model predictive control of nonlinear process systems using Lyapunov techniques
inferredcites →https://doi.org/10.1002/aic.690480912openalex-extracted
Linear control of nonlinear systems: Interplay between nonlinearity and feedback
inferredcites →https://doi.org/10.1007/978-1-84628-635-3openalex-extracted
Advanced Control of Industrial Processes: Structures and Algorithms
inferredcites →https://doi.org/10.1007/s00521-023-08560-7openalex-extracted
A maintenance planning framework using online and offline deep reinforcement learning
inferredcites →https://doi.org/10.1016/0009-2509(92)80011-zopenalex-extracted
A numerical method for determining optimal parameter values in forced periodic operation
inferredcites →https://doi.org/10.1016/j.autcon.2022.104128openalex-extracted
Comparison of online and offline deep reinforcement learning with model predictive control for thermal energy management
inferredcites →https://doi.org/10.1016/j.compchemeng.2004.07.016openalex-extracted
Challenges in the new millennium: product discovery and design, enterprise and supply chain optimization, global life cycle assessment
inferredcites →https://doi.org/10.1016/j.compchemeng.2014.10.009openalex-extracted
Variant and invariant states for chemical reaction systems
inferredcites →https://doi.org/10.1016/j.compchemeng.2017.10.008openalex-extracted
Machine learning: Overview of the recent progresses and implications for the process systems engineering field
inferredcites →https://doi.org/10.1016/j.compchemeng.2020.106886openalex-extracted
A review On reinforcement learning: Introduction and applications in industrial process control
inferredcites →https://doi.org/10.1016/j.compchemeng.2021.107252openalex-extracted
Process systems engineering – The generation next?
inferredcites →https://doi.org/10.1016/j.energy.2022.125290openalex-extracted
Data-driven Offline Reinforcement Learning for HVAC-systems
inferredcites →https://doi.org/10.1016/j.ins.2023.03.019openalex-extracted
Offline reinforcement learning for industrial process control: A case study from steel industry
inferredcites →https://doi.org/10.1016/j.jprocont.2007.05.007openalex-extracted
Economic assessment of advanced process control – A survey and framework
inferredcites →https://doi.org/10.1016/j.jprocont.2022.08.002openalex-extracted
Meta-reinforcement learning for the tuning of PI controllers: An offline approach
inferredcites →https://doi.org/10.1080/10643389.2023.2183699openalex-extracted
Reinforcement learning applied to wastewater treatment process control optimization: Approaches, challenges, and path forward
inferredcites →https://doi.org/10.1109/mcas.2009.933854openalex-extracted
Reinforcement learning and adaptive dynamic programming for feedback control
inferredcites →https://doi.org/10.1109/tnnls.2023.3250269openalex-extracted
A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems
inferredcites →https://doi.org/10.1609/aaai.v36i4.20393openalex-extracted
DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning
inferredcites →https://doi.org/10.2307/1969955openalex-extracted
Contributions to Stability Theory
inferredcites →https://doi.org/10.48550/arxiv.2005.01643openalex-extracted
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
inferredcites →https://doi.org/10.48550/arxiv.2204.05618openalex-extracted
When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?
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