Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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Article Citation - WoS: 5Citation - Scopus: 7Hydrogeochemistry and Environmental Properties of Geothermal Fields. Case Study: Balçova, Izmir-Turkey(Taylor and Francis Ltd., 2012) Çakın, Ayça; Gökçen Akkurt, Gülden; Gökçen Akkurt, Gülden; Baba, Alper; Baba, Alper; Eroğlu, Ahmet Emin; 04.01. Department of Chemistry; 03.03. Department of Civil Engineering; 03.06. Department of Energy Systems Engineering; 03. Faculty of Engineering; 04. Faculty of Science; 01. Izmir Institute of TechnologyBalcova Geothermal Field hosts the largest geothermal district heating system of Turkey and a number of shallow groundwater wells that are used for irrigation of the agricultural activities. The present study aims to assess the influence of geothermal fluid on groundwater by determining the hydrogeochemical properties of the water resources. A sampling program was conducted for 10 months and samples were collected from geothermal and groundwater wells including re-injected fluid. Trace and major elements, and the types of waters were determined. The results of groundwater analysis showed that the concentrations of some toxic species, such as arsenic, boron, and fluoride, exceeded the limits of drinking water standards set by TSE, EPA, and WHO.Conference Object Citation - WoS: 1Citation - Scopus: 6Genetic Algorithm-Artificial Neural Network Model for the Prediction of Germanium Recovery From Zinc Plant Residues(Taylor and Francis Ltd., 2002) Akkurt, Sedat; Özdemir, Serhan; Özdemir, Serhan; Tayfur, Gökmen; Akkurt, Sedat; 03.09. Department of Materials Science and Engineering; 03.03. Department of Civil Engineering; 03.10. Department of Mechanical Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyA multi-layer, feed-forward, back-propagation learning algorithm was used as an artificial neural network (ANN) tool to predict the extraction of germanium from zinc plant residues by sulphuric acid leaching. A genetic algorithm (GA) was used for the selection of training and testing data and a GA-ANN model of the germanium leaching system was created on the basis of the training data. Testing of the model yielded good error levels (r2 = 0.95). The model was employed to predict the response of the system to different values of the factors that affect the recovery of germanium and the results facilitate selection of the experimental conditions in which the optimum recovery will be achieved.