Exergoeconomic analysis of a geo-thermal power-plant with a comparative optimization using classical, meta-heuristic and reinforcement learning algorithms and sensitivity analysis with machine learning approach

Ahmadi Rahmatabadi, Mahan; Karim, Mohammad Hossein; Talebi, Saeid; Mardani, Mohsen; Hosseinian, Seyed  Hossein; Gharehpetian, Gevork B.

doi:10.22060/aest.2026.25579.1004

Exergoeconomic analysis of a geo-thermal power-plant with a comparative optimization using classical, meta-heuristic and reinforcement learning algorithms and sensitivity analysis with machine learning approach

Document Type : Original Article

Authors

Mahan Ahmadi Rahmatabadi ¹

Mohammad Hossein Karim ²

Saeid Talebi ²

Mohsen Mardani ³

Seyed Hossein Hosseinian ⁴

Gevork B. Gharehpetian ⁴

¹ Department of Energy Engineering and Physics, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

² Department of Energy Engineering and Physics, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.

³ ACECR, Amirkabir University of Technology Branch, Tehran, Iran.

⁴ Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.

10.22060/aest.2026.25579.1004

Abstract

Geothermal energy is a clean and renewable source of energy with low impact on the environment and the ability to provide a continuous source of energy for electricity generation. In the current study, a detailed thermodynamic model of the geothermal power plant is developed, modelled and analyzed using the energy and exergy analysis methods in order to identify the major sources of irreversibility, efficiency reduction, and performance limitation within the geothermal power plant components. To improve the efficiency and performance of the geothermal power plant, a multi-objective optimization strategy using metaheuristic, classical, and reinforcement learning algorithms is implemented to maximize the net power and exergy efficiency, and the investment and operation costs are minimized. The results are useful for the optimal design and development of efficient and cost-effective geothermal power plants using the capabilities of the optimization algorithms to obtain an effective compromise between the thermodynamic and cost-based performance parameters.

Graphical Abstract