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Investigation of the potential for the use ofquantitative mineralogical data to create simplifiedmodels for gravity separation devices

Başlık çevirisi mevcut değil.

  1. Tez No: 710537
  2. Yazar: AHMET YAŞAR YAMANTAŞ
  3. Danışmanlar: Belirtilmemiş.
  4. Tez Türü: Yüksek Lisans
  5. Konular: Belirtilmemiş.
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2020
  8. Dil: İngilizce
  9. Üniversite: Unıversıty Of Exeter
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: Belirtilmemiş.

Özet

Özet yok.

Özet (Çeviri)

Tungsten is a metal with unique physical properties and is on the critical raw materials list of the European Union. Due to the increasing demand for this metal and its limited resources, it is essential to enrich it in optimum conditions with high recoveries. One of the most important processing methods for tungsten is the gravity separation method. A modelling method to be developed for this method is critical in terms of increasing tungsten recoveries. The most important factors affecting the separation in the gravity separation method are particle size, density and shape. QEMSCAN® method can estimate the particle size and density of materials. In this study, quantitative mineralogical data provided by QEMSCAN® were used as input to create simplified modelling approaches for gravity separation devices. As input to these, data collected from the Drakelands mine during the survey study and the data of tests undertaken in the laboratory using synthetically generated ores were used. Partition curves of the devices were created using the input data, quadratic equations were added to these curves then three different general model equations were created. Density and particle size were used as parameters in the equations. This study showed that it is possible to estimate the recoveries of particle size/density classes relatively accurately using the models created. Although there was inconsistency, generally the most accurate results were achieved with the first modelling equation. It was determined that normalisation study is significant for these approaches and the applied methods can be modified depending on the situations, for example by reducing the highest density class error. On the other hand, the Microsoft Excel solver add-in used to optimise the model equations was not always successful in optimisation. This situation has limited identifying the best modelling approach. Also, the models predicted more accurately in some devices, but not so successfully in others. Therefore, it is concluded that these models are not suitable for all cases without further study

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