Predicting finished product properties in mining industry from pre-extraction data
| Autor: | T. J. Howard, Jim Everett |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Data collection
Computer science media_common.quotation_subject food and beverages Regression analysis Geotechnical Engineering and Engineering Geology computer.software_genre Crusher Data set Product (business) Geochemistry and Petrology Linear regression Earth and Planetary Sciences (miscellaneous) Quality (business) Data mining Predictability computer media_common |
| Zdroj: | Applied Earth Science. 120:137-147 |
| ISSN: | 1743-2758 0371-7453 |
| Popis: | An essential requirement of product quality control in the mining industry is to be able to reliably predict key quality properties of finished product from the data available before the extraction of the ore. From a production viewpoint, the unit of data collection is generally the input and output data set for each shift of crusher production but could be any period where mine pre-crusher data can be reliably matched with product data. Linear regression models can be used to predict crush grades from blast grades, even where the crush material is blended from multiple sources or pits for each of which differing regression models might apply. The best model for any application will be a balance between required predictability, available data and the tolerance of the business for complex models. The regression modelling approach has several advantages over the classic method of run of mine crusher trials. The models can use any predictor variable such as grade, geotype and in situ density provided... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|