Paper Title :Optimal Alternative to Change the Production-Well of Geothermal Power Plant by Machine Learning.

Author :Sitthilith Chanthamaly, Anucha Promwungkwa, Kanchit Ngamsanroaj

Article Citation :Sitthilith Chanthamaly ,Anucha Promwungkwa ,Kanchit Ngamsanroaj , (2022 ) " Optimal Alternative to Change the Production-Well of Geothermal Power Plant by Machine Learning. " , International Journal of Mechanical and Production Engineering (IJMPE) , pp. 36-40, Volume-10,Issue-8

Abstract : Abstract - Nowadays, industries around the world have been focused on developing predictive maintenance (PM) methods to enhance operational systems that are constant, reliable, and safe. PM technology has a function that can predict potential failures and enhance the management of machine systems. However, the decision-making process on the PM should concern the cost-effectiveness analysis (economic analysis). Thus, this paper discusses a combination of economic analysis and Machine learning (ML) in optimizing the adoption of PM. The Classification Artificial Neural Network (ANN) Algorithm of ML was selected for the PM process. The results of ML and economic analysis are used to define the optimal PM application. The economic analysis is to calculate the power production rise resulting from the prediction of the Classification ANN Model. The proposed approach is to compare the optimal approach in the decision-making on maintenance strategies. This study shows that the use of the ML algorithm can increase power production in the Geothermal Power Plant by an average of 17% per 4 year, with an energy power increase of about 276,444 kWh/year. Keywords - Machine learning, Artificial Neural Network, predictive maintenance, Economic Analysis, decision-making.

Type : Research paper

Published : Volume-10,Issue-8

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