Amirkabir University of Technology Advances in Energy Sciences and Technologies 3115-9117 1 4 2026 04 01 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 324 339 6034 10.22060/aest.2026.25579.1004 EN Mahan Ahmadi Rahmatabadi Department of Energy Engineering and Physics, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran Mohammad Hossein Karim Department of Energy Engineering and Physics, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. Saeid Talebi Department of Energy Engineering and Physics, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. Mohsen Mardani ACECR, Amirkabir University of Technology Branch, Tehran, Iran. Seyed Hossein Hosseinian Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. Gevork B. Gharehpetian Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. Journal Article 2026 01 06 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. Geo-Thermal Power Plant Exergoeconomy Machine learning Reinforcement Learning https://aest.aut.ac.ir/article_6034_78421a2e0e1168e5cd1b7a8d23773ce6.pdf