Prediction of geothermal originated boron contamination by deep learning approach: at Western Anatolia Geothermal Systems in Turkey

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dc.authoridHAKLIDIR, FUSUN SERVIN TUT/0000-0002-9469-8870|HAKLIDIR, MEHMET/0000-0003-4985-1116
dc.authorwosidHAKLIDIR, FUSUN SERVIN TUT/ABI-3978-2020
dc.authorwosidHAKLIDIR, MEHMET/ABI-3996-2020
dc.contributor.authorHaklidir, Fusun S. Tut
dc.contributor.authorHaklidir, Mehmet
dc.date.accessioned2024-07-18T20:42:23Z
dc.date.available2024-07-18T20:42:23Z
dc.date.issued2020
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractGeothermal fluids consist of hot water, steam and gases in water-dominated reservoirs. They contain various dissolved major elements such as sodium, potassium, calcium, silica, bicarbonate, carbonate, chlorine, sulphate and minor elements such as boron, fluorine, lithium, iron, arsenic, mercury and bromine at different concentrations in the liquid phase. The concentration of dissolved solids depends on the temperature, gas content, reservoir geology, permeability, water mixing and fluid source of a geothermal system. Some of these species exhibit a toxic effect at high concentrations and require precaution after the discharging of geothermal water. Boron is one of the important constituents and can be observed as boric acid (H3BO3) or HBO2 in the water phase. The concentration of B changes between 10 and 50 ppm in chloride-type fluids and can occur in greater quantities than these values in organic-rich sedimentary rocks in geothermal fluids. Although boron is considered toxic, it is also one of the crude minerals and can be used in different industries, such as oil and gas chemistry, vehicle technologies, agriculture, ceramics, and adhesive and coating, among others. Machine learning is a method of data analytics for identifying patterns in data and using them to automatically make predictions about new data points. Deep learning is a machine learning subset that uses artificial neural networks with multiple layers. Deep learning can automatically learn representations from data without hand-coded rules or domain knowledge; this is the primary difference between deep learning and traditional machine learning techniques. In this study, a deep neural network model has been developed to predict boron concentrations based on hydrogeochemistry data for different geothermal systems. To compare the prediction performance of our proposed deep neural network model, two well-known regression approaches, linear regression and linear support vector machine (SVM), were performed, and the results have been presented. The performance comparison revealed that our deep neural network (DNN) model achieved better prediction performance than traditional machine learning techniques-linear regression and linear SVM.en_US
dc.identifier.doi10.1007/s12665-020-08907-6
dc.identifier.issn1866-6280
dc.identifier.issn1866-6299
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-85083206029en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1007/s12665-020-08907-6
dc.identifier.urihttps://hdl.handle.net/11411/7267
dc.identifier.volume79en_US
dc.identifier.wosWOS:000526182900002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofEnvironmental Earth Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine Learningen_US
dc.subjectDeep Learningen_US
dc.subjectHydrogeochemistryen_US
dc.subjectGeothermalen_US
dc.subjectBoronen_US
dc.subjectMineral Recoveryen_US
dc.subjectRemovalen_US
dc.subjectWateren_US
dc.subjectFluidsen_US
dc.subjectGeochemistryen_US
dc.subjectReinjectionen_US
dc.subjectRecoveryen_US
dc.titlePrediction of geothermal originated boron contamination by deep learning approach: at Western Anatolia Geothermal Systems in Turkeyen_US
dc.typeArticleen_US

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