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Digital twin of an absorption chiller 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

The aim of this study is to create a digital twin of a commercial absorption chiller for control and optimization purposes. The chiller is a complex system that is affected by solar intermittency and non-linearities. The authors use Adaptive Neuro-fuzzy Inference System (ANFIS) to model the chiller’s behavior during transients and part-load events. The chiller is divided into four sub-models, each modeled by ANFIS, and trained and validated using data from 15 days of operation. The ANFIS models are precise, accurate, and fast, with a worst-case Mean Absolute Percentage Error (MAPE) of 3.30% and reduced error dispersion (σE=0.88) and Standard Error (SE=0.01). The models outperformed literature models in terms of MAPE, with MAPEs of 1.12%, 2.21%, and 3.24% for the High Temperature Generator (HTG), absorber + condenser, and evaporator outlet temperatures, respectively. The computational execution time of the model is also a valuable asset, with an average simulation step taking less than 0.20 ms and a total simulation time of 8.9 s for three days of operation. The resulting digital twin is suitable for Model Predictive Control applications and fast what-if analysis and optimization due to its gray-box representation and computational speed.

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 an absorption chiller for solar cooling," Renewable Energy, Elsevier, vol. 208(C), pages 36-51.
    • Handle: RePEc:eee:renene:v:208:y:2023:i:c:p:36-51
    DOI: 10.1016/j.renene.2023.03.048
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    References listed on IDEAS

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    1. Eduardo F. Camacho & Antonio J. Gallego & Juan M. Escaño & Adolfo J. Sánchez, 2019. "Hybrid Nonlinear MPC of a Solar Cooling Plant," Energies, MDPI, vol. 12(14), pages 1-22, July.
    2. Yan, Mingyu & Gan, Wei & Zhou, Yue & Wen, Jianfeng & Yao, Wei, 2022. "Projection method for blockchain-enabled non-iterative decentralized management in integrated natural gas-electric systems and its application in digital twin modelling," Applied Energy, Elsevier, vol. 311(C).
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    6. Lazrak, Amine & Boudehenn, François & Bonnot, Sylvain & Fraisse, Gilles & Leconte, Antoine & Papillon, Philippe & Souyri, Bernard, 2016. "Development of a dynamic artificial neural network model of an absorption chiller and its experimental validation," Renewable Energy, Elsevier, vol. 86(C), pages 1009-1022.
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    1. 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.
    2. Pallavicini, Jacopo & Fedeli, Matteo & Scolieri, Giacomo Domenico & Tagliaferri, Francesca & Parolin, Jacopo & Sironi, Selena & Manenti, Flavio, 2023. "Digital twin-based optimization and demo-scale validation of absorption columns using sodium hydroxide/water mixtures for the purification of biogas streams subject to impurity fluctuations," Renewable Energy, Elsevier, vol. 219(P1).

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