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Atikokan Digital Twin: Machine learning in a biomass energy system

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  • Spinti, Jennifer P.
  • Smith, Philip J.
  • Smith, Sean T.

Abstract

The Atikokan Generating Station, operated by Ontario Power Generation, has a 200 MW, biomass-fired tower boiler that operates on a dispatch schedule with a five-minute cycle. The boiler is generally operated in the range of 40–100 MW using two of five burner levels. In order to optimize boiler performance, we propose the implementation of a unique digital twin. Our digital twin abstraction couples Bayesian inference from science-based models and from observations (machine learning) with decision theory to predict operating-variable set points that optimize the physical asset (the boiler) in the presence of uncertainty (artificial intelligence). We focus this paper on the continuous Bayesian machine learning part of the Atikokan Digital Twin; we discuss decision theory in a companion paper. We identify and learn about 12 operational, model, and measured-output parameters and their uncertainties from high-fidelity, science-based simulations of the Atikokan boiler and from the observed measurements at the power plant. Since the goal of the Atikokan Digital Twin is to implement it online in real time, we require fast function evaluations for the quantities of interest extracted from the simulations in the Bayesian analysis. We use Gaussian process regression/interpolation to create accurate, robust surrogate models. We define the Bayesian priors and likelihood function and solve for the posterior distributions of the 12 parameters. We then propagate these distributions (i.e., parameters with uncertainty) into the predicted distributions of 790 quantities of interest to learn about the relative importance of various sources of error including experimental, model, and operating-parameter errors.

Suggested Citation

  • Spinti, Jennifer P. & Smith, Philip J. & Smith, Sean T., 2022. "Atikokan Digital Twin: Machine learning in a biomass energy system," Applied Energy, Elsevier, vol. 310(C).
  • Handle: RePEc:eee:appene:v:310:y:2022:i:c:s0306261921016627
    DOI: 10.1016/j.apenergy.2021.118436
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    References listed on IDEAS

    as
    1. 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.
    2. Adamczyk, Wojciech P. & Isaac, Benjamin & Parra-Alvarez, John & Smith, Sean T. & Harris, Derek & Thornock, Jeremy N. & Zhou, Minmin & Smith, Philip J. & Żmuda, Robert, 2018. "Application of LES-CFD for predicting pulverized-coal working conditions after installation of NOx control system," Energy, Elsevier, vol. 160(C), pages 693-709.
    3. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
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    Citations

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    Cited by:

    1. 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.
    2. Bâra, Adela & Oprea, Simona-Vasilica, 2024. "Enabling coordination in energy communities: A Digital Twin model," Energy Policy, Elsevier, vol. 184(C).
    3. 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).
    4. 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).
    5. 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).
    6. 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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