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Digital twin of a Fresnel solar collector for solar cooling

Author

Listed:
  • Machado, Diogo Ortiz
  • Chicaiza, William D.
  • Escaño, Juan M.
  • Gallego, Antonio J.
  • de Andrade, Gustavo A.
  • Normey-Rico, Julio E.
  • Bordons, Carlos
  • Camacho, Eduardo F.

Abstract

This work develops digital entities of a commercial Fresnel Solar Collector (FSC) installed in an absorption cooling plant. The objective is to create and validate models that describe the FSC dynamics across its whole operation range during the day and the night. Thus, the temperatures range between operation temperature of 180 °C and almost ambient temperature due to overnight heat losses. In the same sense, the flow range between zero to 13m3/h. The idea is that the digital twin will aid start-up and shut-down optimization and control design reliability. The paper employs two modeling approaches, then evaluates their twinning/adaptation time and performance validation. One model uses phenomenological modeling through Partial Differential Equations (PDE) and parameters identification, and another uses a data-driven technique with Adaptive Neuro-Fuzzy Inference Systems (ANFIS). The available measurement data sets comprise 25 days of operation with a sampling time of 20 s which, after outlier removal, filtering and treatment, resulted in 108416 samples. The validation considers six separate operating days. Results show that both models can twinning/adapt considering measured data. The models present pretty good results and are suitable for control and optimization. Besides, this is the first paper considering the FSC mirror defocus action on dynamic modeling and validation.

Suggested Citation

  • Machado, Diogo Ortiz & Chicaiza, William D. & Escaño, Juan M. & Gallego, Antonio J. & de Andrade, Gustavo A. & Normey-Rico, Julio E. & Bordons, Carlos & Camacho, Eduardo F., 2023. "Digital twin of a Fresnel solar collector for solar cooling," Applied Energy, Elsevier, vol. 339(C).
  • Handle: RePEc:eee:appene:v:339:y:2023:i:c:s0306261923003082
    DOI: 10.1016/j.apenergy.2023.120944
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    References listed on IDEAS

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    1. Ceusters, Glenn & Rodríguez, Román Cantú & García, Alberte Bouso & Franke, Rüdiger & Deconinck, Geert & Helsen, Lieve & Nowé, Ann & Messagie, Maarten & Camargo, Luis Ramirez, 2021. "Model-predictive control and reinforcement learning in multi-energy system case studies," Applied Energy, Elsevier, vol. 303(C).
    2. Machado, Diogo Ortiz & Sánchez, Adolfo J. & Gallego, Antonio J. & de Andrade, Gustavo A. & Normey-Rico, Julio E. & Bordons, Carlos & Camacho, Eduardo F., 2022. "Split-range control for improved operation of solar absorption cooling plants," Renewable Energy, Elsevier, vol. 192(C), pages 361-372.
    3. Machado, Diogo Ortiz & Andrade, Gustavo Artur & Normey-Rico, Julio Elias & Bordons, Carlos, 2022. "Optimal operation of Concentrating Solar Collector fields using exergy-based hierarchical control," Energy, Elsevier, vol. 239(PE).
    4. Brandão, Adriano Silva Martins & da Costa Mendes, Paulo Renato & Normey-Rico, Julio Elias, 2022. "Simplified optical model, aiming strategy and partial defocusing strategy for solar Fresnel collectors," Renewable Energy, Elsevier, vol. 188(C), pages 11-36.
    5. Kincaid, Nicholas & Mungas, Greg & Kramer, Nicholas & Wagner, Michael & Zhu, Guangdong, 2018. "An optical performance comparison of three concentrating solar power collector designs in linear Fresnel, parabolic trough, and central receiver," Applied Energy, Elsevier, vol. 231(C), pages 1109-1121.
    6. Ghilardi, Lavinia Marina Paola & Castelli, Alessandro Francesco & Moretti, Luca & Morini, Mirko & Martelli, Emanuele, 2021. "Co-optimization of multi-energy system operation, district heating/cooling network and thermal comfort management for buildings," Applied Energy, Elsevier, vol. 302(C).
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    Cited by:

    1. Akshay Ranade & Javier Gómez & Andrew de Juan & William D. Chicaiza & Michael Ahern & Juan M. Escaño & Andriy Hryshchenko & Olan Casey & Aidan Cloonan & Dominic O’Sullivan & Ken Bruton & Alan McGibney, 2024. "Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications," Energies, MDPI, vol. 17(8), pages 1-28, April.
    2. Hamza Assia & Houari Merabet Boulouiha & William David Chicaiza & Juan Manuel Escaño & Abderrahmane Kacimi & José Luis Martínez-Ramos & Mouloud Denai, 2023. "Wind Turbine Active Fault Tolerant Control Based on Backstepping Active Disturbance Rejection Control and a Neurofuzzy Detector," Energies, MDPI, vol. 16(14), pages 1-22, July.

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