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Maintenance Strategy Optimization of a Coal-Fired Power Plant Cooling Tower through Generalized Stochastic Petri Nets

Author

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  • Arthur H.A. Melani

    (Mechatronics and Mechanical System Engineering Department, Polytechnic School of the University of São Paulo, São Paulo 05508-030, Brazil)

  • Carlos A. Murad

    (Mechatronics and Mechanical System Engineering Department, Polytechnic School of the University of São Paulo, São Paulo 05508-030, Brazil)

  • Adherbal Caminada Netto

    (Mechatronics and Mechanical System Engineering Department, Polytechnic School of the University of São Paulo, São Paulo 05508-030, Brazil)

  • Gilberto F.M. Souza

    (Mechatronics and Mechanical System Engineering Department, Polytechnic School of the University of São Paulo, São Paulo 05508-030, Brazil)

  • Silvio I. Nabeta

    (Energy and Electrical Automation Department, Polytechnic School of the University of São Paulo, São Paulo 05508-010, Brazil)

Abstract

Determining the ideal size of maintenance staff is a daunting task, especially in the operation of large and complex mechanical systems such as thermal power plants. On the one hand, a significant investment in maintenance is necessary to maintain the availability of the system. On the other hand, it can significantly affect the profit of the plant. Several mathematical modeling techniques have been used in many different ways to predict and improve the availability and reliability of such systems. This work uses a modeling tool called generalized stochastic Petri net (GSPN) in a new way, aiming to determine the effect that the number of maintenance teams has on the availability and performance of a coal-fired power plant cooling tower. The results obtained through the model are confronted with a thermodynamic analysis of the cooling tower that shows the influence of this system’s performance on the efficiency of the power plant. Thus, it is possible to determine the optimal size of the repair team in order to maximize the plant’s performance with the least possible investment in maintenance personnel.

Suggested Citation

  • Arthur H.A. Melani & Carlos A. Murad & Adherbal Caminada Netto & Gilberto F.M. Souza & Silvio I. Nabeta, 2019. "Maintenance Strategy Optimization of a Coal-Fired Power Plant Cooling Tower through Generalized Stochastic Petri Nets," Energies, MDPI, vol. 12(10), pages 1-28, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1951-:d:233190
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Tae-Woo Kim & Yenjae Chang & Dae-Wook Kim & Man-Keun Kim, 2020. "Preventive Maintenance and Forced Outages in Power Plants in Korea," Energies, MDPI, vol. 13(14), pages 1-12, July.
    2. Jakov Batelić & Karlo Griparić & Dario Matika, 2021. "Impact of Remediation-Based Maintenance on the Reliability of a Coal-Fired Power Plant Using Generalized Stochastic Petri Nets," Energies, MDPI, vol. 14(18), pages 1-14, September.
    3. Arthur Henrique de Andrade Melani & Miguel Angelo de Carvalho Michalski & Carlos Alberto Murad & Adherbal Caminada Netto & Gilberto Francisco Martha de Souza, 2022. "Generalized Stochastic Petri Nets for Planning and Optimizing Maintenance Logistics of Small Hydroelectric Power Plants," Energies, MDPI, vol. 15(8), pages 1-16, April.
    4. Emil Cazacu & Lucian-Gabriel Petrescu & Valentin Ioniță, 2022. "Smart Predictive Maintenance Device for Critical In-Service Motors," Energies, MDPI, vol. 15(12), pages 1-16, June.
    5. Renan Favarão da Silva & Marjorie Maria Bellinello & Gilberto Francisco Martha de Souza & Sara Antomarioni & Maurizio Bevilacqua & Filippo Emanuele Ciarapica, 2021. "Deciding a Multicriteria Decision-Making (MCDM) Method to Prioritize Maintenance Work Orders of Hydroelectric Power Plants," Energies, MDPI, vol. 14(24), pages 1-22, December.
    6. Yamano, Shuhei & Nakaya, Takashi & Ikegami, Takashi & Nakayama, Masayuki & Akisawa, Atsushi, 2021. "Optimization modeling of mixed gas engine types with different maintenance spans and costs: Case study OF CCHP to evaluate optimal gas engine operations and combination of the types," Energy, Elsevier, vol. 222(C).

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