| dc.creator |
Umucu, Yakup,
1978-
author
12160 |
|
| dc.creator |
Gündüz, Lütfullah,
1966-
thesis advisor
11443 |
|
| dc.creator |
Bozkurt, Murat Mümtaz Volkan.
thesis advisor
12163 |
|
| dc.creator |
Süleyman Demirel Üniversitesi.
Fen Bilimleri Enstitüsü.
Maden Mühendisliği Anabilim Dalı.
10415
issuing body |
|
| dc.date |
2011. |
|
| dc.identifier |
http://tez.sdu.edu.tr/Tezler/TF01597.pdf |
|
| dc.description |
In this thesis, the grindability properties of different Limestone samples from Afyon and Muğla regions are investigated at batch grinding conditions based on a kinetic model. For this purpose, firstly, five different mono-sized fractions were prepared between 1,7mm and 0,106mm formed by a .2 sieve series. Si and Bi,j (breakage distribution function and related model parameter) equations were determined from the size distributions at different grinding times, and the model parameters ( Sİ, at, ., . and .J ) different filling ratios (fc=0.072, 0.096, 0.12 and 0.14), ball filling loads (%20, %30, % 35 and %40), and mill speeds ( %65, %75, %85). Experimentally determined data were statistically compared with data obtained using model parameter from MODSIM simulation program. The final stage of the study, Artificial Neural Networks (ANN) using the MATLAB computer software program is primarily made MODSIM.s process was carried out similarly. Then, using data that can be obtained more easily by using artificial neural networks, the simulation was carried out operations. Key Words: Grinding, kinetic model, artificial neural networks (ANN). |
|
| dc.description |
Tez (Doktora) - Süleyman Demirel Üniversitesi, Fen Bilimleri Enstitüsü, Maden Mühendisliği Anabilim Dalı, 2011. |
|
| dc.description |
Kaynakça var. |
|
| dc.description |
In this thesis, the grindability properties of different Limestone samples from Afyon and Muğla regions are investigated at batch grinding conditions based on a kinetic model. For this purpose, firstly, five different mono-sized fractions were prepared between 1,7mm and 0,106mm formed by a .2 sieve series. Si and Bi,j (breakage distribution function and related model parameter) equations were determined from the size distributions at different grinding times, and the model parameters ( Sİ, at, ., . and .J ) different filling ratios (fc=0.072, 0.096, 0.12 and 0.14), ball filling loads (%20, %30, % 35 and %40), and mill speeds ( %65, %75, %85). Experimentally determined data were statistically compared with data obtained using model parameter from MODSIM simulation program. The final stage of the study, Artificial Neural Networks (ANN) using the MATLAB computer software program is primarily made MODSIM.s process was carried out similarly. Then, using data that can be obtained more easily by using artificial neural networks, the simulation was carried out operations. Key Words: Grinding, kinetic model, artificial neural networks (ANN). |
|
| dc.language |
tur |
|
| dc.publisher |
Isparta : SDÜ Fen Bilimleri Enstitüsü, |
|
| dc.subject |
Süleyman Demirel Üniversitesi |
|
| dc.title |
Kireçtaşı örneğinin yapay sinir ağları ile öğütme işlemlerinin modellenmesi ve kinetik modelle kıyaslanması = Modeling of grinding process with artificial neural network on the base of limestone sample and comparison with the kinetic model / |
|
| dc.type |
text |
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