Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Author: search.filters.author.Baba, AlperSubject: search.filters.subject.Machine Learning

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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, Alper
    Thermal 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: 3
    Citation - Scopus: 4
    Comparison of Conventional and Machine Learning Models for Kinetic Modelling of Biomethane Production From Pretreated Tomato Plant Residues
    (Elsevier, 2025) Fidan, Berrak; Bodur, Fatma-Gamze; Oztep, Gulsh; Gungoren-Madenoglu, Tuelay; Baba, Alper; Kabay, Nalan
    Tomato plant residues (Solanum lycopersicum L.) lack sustainable applications as abundant lignocellulosic biomass after harvest. These residues can be utilized as substrates in anaerobic digestion for biomethane production, generating energy and reducing waste. The purpose of this study was to investigate the sustainable utilization of tomato plant residues for biomethane production at varying conditions and to model biological kinetics. The study aimed to evaluate the effects of varying substrate/inoculum ratios, sulfuric acid pretreatment concentrations, and yeast (Saccharomyces cerevisiae) addition on biogas and biomethane yields under mesophilic conditions (37 degrees C). Maximum biogas and biomethane yields in the studied range were obtained when the substrate/inoculum ratio was 3 (g substrate/g inoculum), the sulfuric acid concentration used for residue pretreatment was 2 %v/v, and the substrate/yeast ratio was 10 (g substrate/g yeast). The yeast ratio of 10 increased the cumulative biogas and biomethane production by 96.5 and 128.9%, respectively. Conventional models (Modified Gompertz, Cone, First-order, Logistic) and Machine Learning models (Support Vector Machine and Neural Network) were compared for biological kinetics. Machine Learning models were also observed to give good fitting results similar to conventional models. Results suggest that Machine Learning models (RMSE: 2.5833-12.0500) are reliable methods like conventional kinetic models (RMSE: 2.1796-13.4880) for forecasting biomethane production in anaerobic digestion processes and Machine Learning models can be applied without needing prior understanding of biomethane production kinetics.