WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Development and Validation of Regression Model via Machine Learning to Estimate Thermal Conductivity and Heat Flow Using Igneous Rocks from the Dikili-Bergama Geothermal Region, Western Anatolia(Pergamon-Elsevier Science Ltd, 2026) Ayzit, Tolga; Sahin, Onur Gungor; Erol, Selcuk; Baba, AlperThermal conductivity is a fundamental parameter that significantly influences the thermal regime of the lithosphere. It plays a crucial role in a variety of geological applications, including geothermal energy exploration, igneous system assessment, and tectonic modeling. In this study, a machine learning approach is used to predict the thermal conductivity of igneous rocks based on the composition of major oxides. A total of 488 samples from different regions of the world were analyzed. The thermal conductivity values ranged from 1.20 to 3.74 Wm(-1) K-1 and the mean value was 2.61 Wm(-1) K-1. The Random Forest (RF) algorithm was used, resulting in a high coefficient of determination (R-2 = 0.913 for training and R-2 = 0.794 for testing) and a root mean square error (RMSE) of 0.112 and 0.179, respectively. Significance analysis of the traits identified SiO2 (>40 %), Na2O (>15 %) and Al2O3 (>10 %) as the most influential predictors. The study presented results from the Western Anatolia region, where felsic rocks had the highest thermal conductivity (mean = 2.69 Wm(-)(1)K(-)(1)) compared to mafic (mean = 2.34 Wm(-)(1)K(-)(1)) and ultramafic rocks (mean = 2.39 Wm(-)(1)K(-)(1)). In addition, the study evaluated the predictive capabilities of machine learning models for the igneous rocks of the Dikili-Bergama region and compared the results with those of saturated models. Using these data, we calculated heat flow values of up to 400 mWm(-2) under saturated conditions in western Anatolia. These results highlight the value of integrating geochemical data with machine learning to improve geothermal resource exploration and lithospheric modeling.Article Citation - WoS: 4Citation - Scopus: 10Characterization of Sb Scaling and Fluids in Saline Geothermal Power Plants: a Case Study for Germencik Region (büyük Menderes Graben, Turkey)(Pergamon-Elsevier Science Ltd, 2021) Tonkul, Serhat; Baba, Alper; Demir, Mustafa M.; Regenspurg, SimonaTurkey is located on the seismically active Alpine-Himalayan belt. Although tectonic activity causes seismicity in the Anatolian plate, it also constitutes an important geothermal energy resource. Today, geothermal energy production is heavily concentrated in Turkey's Western Anatolia region. Graben systems in this region are very suitable for geothermal resources. The Buyuk Menderes Graben (BMG) is an area of complex geology with active tectonics and high geothermal potential power. Germencik (Aydin) is located in the BMG, where the geothermal waters include mainly Na-Cl-HCO3 water types. This study examined the stibnite scaling formed in the preheater system of the Germencik Geothermal Field (GGF). The formation of the stibnite scaling on the preheater system dramatically reduces the energy harvesting of the GGF. Considering the stibnite scaling in the surface equipment, the optimum reinjection temperature was determined as 95 degrees C to prevent stibnite scaling in the GGF.