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Innovative Approaches of Optimization Methods Used in Geothermal Power Plants: Artificial Neural Networks and Genetic Algorithms

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  • Özgür Özer

    (Department of Mechanical Engineering, Graduate School of Natural and Applied Sciences, Pamukkale University, 20160 Pamukkale, Türkiye)

  • Harun Kemal Öztürk

    (Department of Mechanical Engineering, Faculty of Engineering, Pamukkale University, 20160 Pamukkale, Türkiye)

Abstract

In this study, a general description of geothermal power plants is provided, and the optimization methods used are summarized. Following the review of these optimization methods, the advantages of heuristic methods and the success of the developed models are demonstrated. The challenges in optimizing geothermal systems, including the limitations due to their complexity and the use of multiple parameters, are discussed. Heuristic methods, particularly the widely used artificial neural networks and genetic algorithms, are explained in general terms. Recent studies highlight that the combined use of artificial neural networks and genetic algorithms can produce faster and more consistent results. This demonstrates the benefits of using advanced methods for geothermal resource utilization and power plant optimization. An innovative optimization method has been developed using the operational data of an ORC geothermal power plant in the city of Izmir. The computational method, using genetic algorithms with artificial neural networks as the fitness function, has identified the optimal operating conditions, achieving a 39.41% increase in net power output. The plant’s gross power generation has increased from 4943 kW to 6624 kW.

Suggested Citation

  • Özgür Özer & Harun Kemal Öztürk, 2025. "Innovative Approaches of Optimization Methods Used in Geothermal Power Plants: Artificial Neural Networks and Genetic Algorithms," Energies, MDPI, vol. 18(2), pages 1-26, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:311-:d:1565350
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    References listed on IDEAS

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    2. Madhawa Hettiarachchi, H.D. & Golubovic, Mihajlo & Worek, William M. & Ikegami, Yasuyuki, 2007. "Optimum design criteria for an Organic Rankine cycle using low-temperature geothermal heat sources," Energy, Elsevier, vol. 32(9), pages 1698-1706.
    3. Zhou, Chunwei & Liu, Gang & Liao, Shengming, 2024. "Probing dominant flow paths in enhanced geothermal systems with a genetic algorithm inversion model," Applied Energy, Elsevier, vol. 360(C).
    4. Podlasek, Szymon & Jankowski, Marcin & Bałazy, Patryk & Lalik, Krzysztof & Figaj, Rafał, 2024. "Application of ANN control algorithm for optimizing performance of a hybrid ORC power plant," Energy, Elsevier, vol. 306(C).
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