Model-free MIMO control tuning of a chiller process using reinforcement learning

Rosdahl, Christian; Bernhardsson, Bo; Eisenhower, Bryan

doi:10.1080/23744731.2023.2247938

Science and Technology for the Built Environment

2023

DOI: 10.1080/23744731.2023.2247938

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Model-free MIMO control tuning of a chiller process using reinforcement learning

Christian Rosdahl,

Bo Bernhardsson,

Bryan Eisenhower

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2024

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Cited by 3 publications

References 29 publications

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Chiller energy prediction in commercial building: A metaheuristic-Enhanced deep learning approach

Sulaiman,

Mustaffa

2024

Energy

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Chiller energy prediction in commercial building: A metaheuristic-Enhanced deep learning approach

Sulaiman,

Mustaffa

2024

Energy

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Utilizing the Kolmogorov-Arnold Networks for chiller energy consumption prediction in commercial building

Sulaiman,

Mustaffa,

Saealal

et al. 2024

Journal of Building Engineering

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Enhancing Vapor Compression Refrigeration Systems Efficiency via Two-Phase Length and Superheat Evaporator MIMO Control

Estrada,

Córdova-Castillo,

Piedra

2024

Processes

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The present investigation focuses on enhancing the efficiency of a vapor compression refrigeration system (VCRS) by proposing a Multiple Input Multiple Output (MIMO) control strategy based on the evaporator’s two-phase length and superheat temperature. A moving boundary dynamic model for the VCRS is implemented using the Thermosys Matlab Toolbox. The study analyzes the influence of actuation parameters, specifically compressor speed and expansion valve opening, on control parameters, namely two-phase length and superheat temperature. A comprehensive analysis based on the first and second laws of thermodynamics is conducted across a wide range of operating conditions. Simulation results demonstrate that two-phase length can be effectively utilized as a control parameter by selecting operating points that maximize the system efficiency. Additionally, the study reveals that extending the evaporator’s two-phase length to 80–90% of its limit increases system efficiency, enabling a reduction in compressor speed while maintaining the cooling capacity.

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Model-free MIMO control tuning of a chiller process using reinforcement learning

Cited by 3 publications

References 29 publications

Chiller energy prediction in commercial building: A metaheuristic-Enhanced deep learning approach

Chiller energy prediction in commercial building: A metaheuristic-Enhanced deep learning approach

Utilizing the Kolmogorov-Arnold Networks for chiller energy consumption prediction in commercial building

Enhancing Vapor Compression Refrigeration Systems Efficiency via Two-Phase Length and Superheat Evaporator MIMO Control

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