Minimizing profile error when estimating the sieve-size distribution of iron ore pellets using ordinal logistic regression
✍ Scribed by Tobias Andersson; Matthew J. Thurley
- Publisher
- Elsevier Science
- Year
- 2011
- Tongue
- English
- Weight
- 559 KB
- Volume
- 206
- Category
- Article
- ISSN
- 0032-5910
No coin nor oath required. For personal study only.
✦ Synopsis
Size measurement of pellets in industry is usually performed by manual sampling and sieving techniques. Automatic on-line analysis of pellet size based on image analysis techniques would allow non-invasive, frequent and consistent measurement. We evaluate the statistical significance of the ability of commonly used size and shape measurement methods to discriminate among different sieve-size classes using multivariate techniques. Literature review indicates that earlier works did not perform this analysis and selected a sizing method without evaluating its statistical significance. Backward elimination and forward selection of features are used to select two feature sets that are statistically significant for discriminating among different sievesize classes of pellets. The diameter of a circle of equivalent area is shown to be the most effective feature based on the forward selection strategy, but an unexpected five-feature classifier is the result using the backward elimination strategy. The discrepancy between the two selected feature sets can be explained by how the selection procedures calculate a feature's significance and that the property of the 3D data provides an orientational bias that favours combination of Feret-box measurements. Size estimates of the surface of a pellet pile using the two feature sets show that the estimated sieve-size distribution follows the known sievesize distribution.