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Keywords

Artificial intelligence applications, Gas Turbines, Operational efficiency, Cost Reduction, Management

Document Type

Research Paper

Abstract

This study aims to review the use of artificial intelligence applications in the management of gas turbine stations and their impact on enhancing and raising the efficiency of these stations, including managing the stations themselves, then improving operational efficiency, predicting faults, and developing strategies for road maintenance and precautionary maintenance while reducing the cost through a methodology that is a combination. One of several methodologies describes the factors that influence the enhancement of operational efficiency and management of turbine plants using artificial intelligence applications. The quantitative methodology in collecting data and studies that included the subject and the analytical and comparative methods in comparing studies and analyzing the most critical results reached, as the article relies on an analysis of scientific literature and recent studies to clarify the potential benefits and challenges associated with the application of artificial intelligence in this field. The review discusses the artificial intelligence tools employed, including machine learning and neural networks, and highlights future innovations that may enhance the efficiency of turbine systems. The study concludes by discussing current limitations and providing recommendations for research and development in this promising field. Most studies have indicated that artificial intelligence applications play a significant role in enhancing the management of gas turbine plants, increasing operational efficiency by 3 to 5%, and reducing operating costs by 8 to 15%.

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Highlights

Using artificial intelligence applications in the management of gas turbine stations. Turbines are subjected to high humidity, corrosion, and extreme temperatures. Increased vibrations and stress on bearings, causing premature failures. Preventive maintenance to avoid sudden failures through the scheduling of regular maintenance.

DOI

10.30684/etj.2025.157633.1904

First Page

536

Last Page

545

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