| Autor: |
B.K. Sahoo, S. De, B.C. Meikap |
| Jazyk: |
angličtina |
| Rok vydání: |
2017 |
| Předmět: |
|
| Zdroj: |
International Journal of Mining Science and Technology, Vol 27, Iss 2, Pp 379-386 (2017) |
| Druh dokumentu: |
article |
| ISSN: |
2095-2686 |
| DOI: |
10.1016/j.ijmst.2017.01.022 |
| Popis: |
Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level (900 W) in microwave oven. The microwave exposure times were fixed at 60 s and 120 s. A rheology characteristic for microwave pre-treatment of coal-water slurry (CWS) was performed in an online Bohlin viscometer. The non-Newtonian character of the slurry follows the rheological model of Ostwald de Waele. The values of n and k vary from 0.31 to 0.64 and 0.19 to 0.81 Pa⋅sn, respectively. This paper presents an artificial neural network (ANN) model to predict the effects of operational parameters on apparent viscosity of CWS. A 4-2-1 topology with Levenberg-Marquardt training algorithm (trainlm) was selected as the controlled ANN. Mean squared error (MSE) of 0.002 and coefficient of multiple determinations (R2) of 0.99 were obtained for the outperforming model. The promising values of correlation coefficient further confirm the robustness and satisfactory performance of the proposed ANN model. Keywords: Microwave pre-treatment, Coal-water slurry, Apparent viscosity, Artificial neural network, Back propagation algorithm |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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