Works matching DE "SUNGUN Copper Mine (Iran)"
Results: 20
Removal of Toxic Metal Ions from Sungun Acid Rock Drainage Using Mordenite Zeolite, Graphene Nanosheets, and a Novel Metal-Organic Framework.
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- Mine Water & the Environment, 2016, v. 35, n. 1, p. 18, doi. 10.1007/s10230-015-0327-7
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Simulation Based Investigation of Different Fleet Management Paradigms in Open Pit Mines-A Case Study of Sungun Copper Mine / Symulacje I Badania Różnych Paradygmatów Wykorzystania Floty Pojazdów I Urządzeń W Kopalniach Odkrywkowych. Studium Przypadku: Kopalnia Miedzi W Sungun
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- Archives of Mining Sciences, 2015, v. 60, n. 1, p. 195, doi. 10.1515/amsc-2015-0013
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Selection of Most Proper Blasting Pattern in Mines Using Linear Assignment Method: Sungun Copper Mine / Wybór Najodpowiedniejszego Schematu Prowadzenia Prac Strzałowych W Kopalni Miedzi Sungun Z Użyciem Metody Przyporządkowania Liniowego
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- Archives of Mining Sciences, 2015, v. 60, n. 1, p. 375, doi. 10.1515/amsc-2015-0025
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Using PROMETHEE Method in Prioritizing of Plant Species Planting to Reclamation of Sungun Copper Mine.
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- EurAsian Journal of Biosciences, 2017, v. 11, n. 1, p. 1
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Change of support based on modified localized uniform conditioning and direct block multivariate simulation on the Sungun porphyry deposit, Iran.
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- Bollettino di Geofisica Teorica ed Applicata, 2017, v. 58, n. 2, p. 115, doi. 10.4430/bgta0188
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Development of a New Index to Assess the Rock Mass Drillability.
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- Geotechnical & Geological Engineering, 2013, v. 31, n. 5, p. 1477, doi. 10.1007/s10706-013-9672-0
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Choosing the Most Proper Plant Types in Reclamation of an Open-Pit Mine.
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- Journal of Mining Science, 2022, v. 58, n. 1, p. 151, doi. 10.1134/S1062739122010185
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Selecting the most suitable blasting pattern using AHP-TOPSIS method: Sungun copper mine.
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- Journal of Mining Science, 2013, v. 49, n. 6, p. 967, doi. 10.1134/S1062739149060178
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Copper recovery improvement by reducing the loss of copper minerals into the tailings of scavenger flotation circuit – Part II.
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- International Journal of Mining & Geo-Engineering, 2024, v. 58, n. 1, p. 31, doi. 10.22059/IJMGE.2023.358812.595065
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FUZZY AHP METHOD FOR PLANT SPECIES SELECTION IN MINE RECLAMATION PLANS: CASE STUDY SUNGUN COPPER MINE.
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- Iranian Journal of Fuzzy Systems, 2014, v. 11, n. 5, p. 23
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APPLICATION OF D-OPTIMAL DESIGN FOR OPTIMIZING COPPER-MOLYBDENUM SULPHIDES FLOTATION.
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- Physicochemical Problems of Mineral Processing, 2016, v. 52, n. 1, p. 252, doi. 10.5277/ppmp160122
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Multiple-point geostatistical simulation of dykes: application at Sungun porphyry copper system, Iran.
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- Stochastic Environmental Research & Risk Assessment, 2014, v. 28, n. 7, p. 1913, doi. 10.1007/s00477-014-0857-8
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Operating Environment-Based Availability Importance Measures for Mining Equipment (Case Study: Sungun Copper Mine).
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- Journal of Failure Analysis & Prevention, 2017, v. 17, n. 1, p. 56, doi. 10.1007/s11668-016-0205-z
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Constraining the timing of porphyry mineralization in northwest Iran in relation to Lesser Caucasus and Central Iran; Re–Os age data for Sungun porphyry Cu–Mo deposit.
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- International Geology Review, 2017, v. 59, n. 12, p. 1561, doi. 10.1080/00206814.2017.1285258
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Development of an evaluation system for blasting patterns to provide efficient production.
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- Journal of Intelligent Manufacturing, 2017, v. 28, n. 4, p. 975, doi. 10.1007/s10845-015-1036-6
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Classification of reserve in Sungun mine based on Archimedean copulas estimates.
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- Arabian Journal of Geosciences, 2022, v. 15, n. 23, p. 1, doi. 10.1007/s12517-022-10976-9
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Development of Rock Engineering Systems-Based Models for Flyrock Risk Analysis and Prediction of Flyrock Distance in Surface Blasting.
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- Rock Mechanics & Rock Engineering, 2014, v. 47, n. 4, p. 1291, doi. 10.1007/s00603-013-0460-1
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An RES-Based Model for Risk Assessment and Prediction of Backbreak in Bench Blasting.
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- Rock Mechanics & Rock Engineering, 2013, v. 46, n. 4, p. 877, doi. 10.1007/s00603-012-0298-y
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Particle swarm optimization approach for forecasting backbreak induced by bench blasting.
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- Neural Computing & Applications, 2017, v. 28, n. 7, p. 1855, doi. 10.1007/s00521-016-2182-2
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Development of a novel flyrock distance prediction model using BPNN for providing blasting operation safety.
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- Neural Computing & Applications, 2016, v. 27, n. 3, p. 699, doi. 10.1007/s00521-015-1889-9
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