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Development and demonstration of advanced predictive and prescriptive algorithms to control industrial installation

Author

Listed:
  • Adamczyk, Wojciech
  • Myöhänen, Kari
  • Klajny, Marcin
  • Kettunen, Ari
  • Klimanek, Adam
  • Ryfa, Arkadiusz
  • Białecki, Ryszard
  • Sładek, Sławomir
  • Zdeb, Janusz
  • Budnik, Michał
  • Peczkis, Grzegorz
  • Przybyła, Grzegorz
  • Gładysz, Paweł
  • Pawlak, Sebastian
  • Zhou, Min-min
  • Jachymek, Piotr
  • Andrzejczyk, Marek

Abstract

This paper explores the use of sophisticated, predictive AI algorithms for monitoring and optimizing industrial installations of CFB power plant. The effectiveness of the system was shown by applying it to a circulating fluidized bed (CFB 1300) power unit. A customized optimization algorithm was developed to manage the oxygen distribution within the combustion chamber. Implementing the developed control system methodology adjusted the fuel distribution, which in turn impacted the overall performance of the boiler. The approach was evaluated under different boiler operating scenarios, including simulated fuel line malfunctions. The devised methodology enables a reduction of approximately 17% in oxygen distribution imbalance within the combustion chamber when a failure was detected. Furthermore, the optimization algorithms facilitate a seamless adjustment in fuel loads, maintaining the necessary oxygen and temperature distribution at the control plane. Moreover, the capabilities of this system were demonstrated for the automatic identification of malfunctions within two crucial parts of the power unit. The initial issue pertains to a fault in the internal phase insulator of the block transformer, and the second occurrence involved the proactive identification of a membrane wall leak over 13 h before its failure.

Suggested Citation

  • Adamczyk, Wojciech & Myöhänen, Kari & Klajny, Marcin & Kettunen, Ari & Klimanek, Adam & Ryfa, Arkadiusz & Białecki, Ryszard & Sładek, Sławomir & Zdeb, Janusz & Budnik, Michał & Peczkis, Grzegorz & Prz, 2024. "Development and demonstration of advanced predictive and prescriptive algorithms to control industrial installation," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224034261
    DOI: 10.1016/j.energy.2024.133648
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    1. Bartnicki, Grzegorz & Klimczak, Marcin & Ziembicki, Piotr, 2023. "Evaluation of the effects of optimization of gas boiler burner control by means of an innovative method of Fuel Input Factor," Energy, Elsevier, vol. 263(PD).
    2. De Lorenzi, Andrea & Gambarotta, Agostino & Marzi, Emanuela & Morini, Mirko & Saletti, Costanza, 2022. "Predictive control of a combined heat and power plant for grid flexibility under demand uncertainty," Applied Energy, Elsevier, vol. 314(C).
    3. Shi, Yan & Zhong, Wenqi & Chen, Xi & Yu, A.B. & Li, Jie, 2019. "Combustion optimization of ultra supercritical boiler based on artificial intelligence," Energy, Elsevier, vol. 170(C), pages 804-817.
    4. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    5. Marco Savastano & Carlo Amendola & Francesco Bellini & Fabrizio D’Ascenzo, 2019. "Contextual Impacts on Industrial Processes Brought by the Digital Transformation of Manufacturing: A Systematic Review," Sustainability, MDPI, vol. 11(3), pages 1-38, February.
    6. 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).
    7. Adamczyk, Wojciech P. & Myöhänen, Kari & Hartge, Ernst-Ulrich & Ritvanen, Jouni & Klimanek, Adam & Hyppänen, Timo & Białecki, Ryszard A., 2018. "Generation of data sets for semi-empirical models of circulated fluidized bed boilers using hybrid Euler-Lagrange technique," Energy, Elsevier, vol. 143(C), pages 219-240.
    8. Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Zhao, Guanjia & Ma, Suxia, 2023. "Data-driven modeling-based digital twin of supercritical coal-fired boiler for metal temperature anomaly detection," Energy, Elsevier, vol. 278(PA).
    9. 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).
    10. Bhagwan, N. & Evans, M., 2023. "A review of industry 4.0 technologies used in the production of energy in China, Germany, and South Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    11. Grochowalski, Jaroslaw & Jachymek, Piotr & Andrzejczyk, Marek & Klajny, Marcin & Widuch, Agata & Morkisz, Pawel & Hernik, Bartłomiej & Zdeb, Janusz & Adamczyk, Wojciech, 2021. "Towards application of machine learning algorithms for prediction temperature distribution within CFB boiler based on specified operating conditions," Energy, Elsevier, vol. 237(C).
    12. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    13. Diz, Sergio de López & López, Roberto Martín & Sánchez, Francisco Javier Rodríguez & Llerena, Edel Díaz & Peña, Emilio José Bueno, 2023. "A real-time digital twin approach on three-phase power converters applied to condition monitoring," Applied Energy, Elsevier, vol. 334(C).
    14. Lin, Boqiang & Huang, Chenchen, 2023. "Nonlinear relationship between digitization and energy efficiency: Evidence from transnational panel data," Energy, Elsevier, vol. 276(C).
    15. Bogdanov, Dmitrii & Ram, Manish & Aghahosseini, Arman & Gulagi, Ashish & Oyewo, Ayobami Solomon & Child, Michael & Caldera, Upeksha & Sadovskaia, Kristina & Farfan, Javier & De Souza Noel Simas Barbos, 2021. "Low-cost renewable electricity as the key driver of the global energy transition towards sustainability," Energy, Elsevier, vol. 227(C).
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