Atikokan Digital Twin: Machine learning in a biomass energy system
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DOI: 10.1016/j.apenergy.2021.118436
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References listed on IDEAS
- Min, Qingfei & Lu, Yangguang & Liu, Zhiyong & Su, Chao & Wang, Bo, 2019. "Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry," International Journal of Information Management, Elsevier, vol. 49(C), pages 502-519.
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Cited by:
- Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
- Bâra, Adela & Oprea, Simona-Vasilica, 2024. "Enabling coordination in energy communities: A Digital Twin model," Energy Policy, Elsevier, vol. 184(C).
- Ning, Jiajun & Xiong, Lixin, 2024. "Analysis of the dynamic evolution process of the digital transformation of renewable energy enterprises based on the cooperative and evolutionary game model," Energy, Elsevier, vol. 288(C).
- Yu, Jianxi & Petersen, Nils & Liu, Pei & Li, Zheng & Wirsum, Manfred, 2022. "Hybrid modelling and simulation of thermal systems of in-service power plants for digital twin development," Energy, Elsevier, vol. 260(C).
- Spinti, Jennifer P. & Smith, Philip J. & Smith, Sean T. & Díaz-Ibarra, Oscar H., 2023. "Atikokan Digital Twin, Part B: Bayesian decision theory for process optimization in a biomass energy system," Applied Energy, Elsevier, vol. 334(C).
- Aliyon, Kasra & Rajaee, Fatemeh & Ritvanen, Jouni, 2023. "Use of artificial intelligence in reducing energy costs of a post-combustion carbon capture plant," Energy, Elsevier, vol. 278(PA).
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Keywords
Bayesian machine learning; Digital twin; Uncertainty quantification; Science-based models; Biomass boiler;All these keywords.
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