Utilizing a Value of Information Framework to Improve Ore Collection and Classification Procedures

One of the major objectives associated with mining is to deliver an expected amount of product to customers on time. Uncertainties inherent to mining can make this goal difficult to meet. In this research we focus on uncertainty in meeting production targets, specifically at the Loussavaara-Kiirunav...

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Bibliographic Details
Main Author: Phillips, Julia A.
Other Authors: COLORADO SCHOOL OF MINES GOLDEN
Format: Text
Language:English
Published: 2006
Subjects:
Geology
Geochemistry and Mineralogy
Metallurgy and Metallography
*MINING ENGINEERING
*IRON
*CLASSIFICATION
*ORES(NONMETALLIC)
*ORES(METAL SOURCES)
*INFORMATION THEORY
DATA BASES
UNCERTAINTY
FLUORESCENCE
QUALITY
ERRORS
VALUE
POTASSIUM
COLLECTION
PHOSPHORUS
ESTERS
RECORDS
CONTAMINANTS
COST MODELS
FEASIBILITY STUDIES
COSTS
PRODUCTION
ECONOMICS
LASERS
Kiruna
Online Access:http://www.dtic.mil/docs/citations/ADA449046
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA449046
Description
Summary:One of the major objectives associated with mining is to deliver an expected amount of product to customers on time. Uncertainties inherent to mining can make this goal difficult to meet. In this research we focus on uncertainty in meeting production targets, specifically at the Loussavaara-Kiirunavarra Aktiebolag (LKAB) company's Kiruna iron ore mine. Uncertainty in ore grade quality is one of the major contributors to the difficulty in meeting production targets. We use a value of information framework (VOI) to consider the economic feasibility of a mine purchasing additional information on extracted ore type to reduce the uncertainty of extracted ore grade quality. The Kiruna mine extracts three ore types during its mining operations. Phosphorus and potassium are the main contaminants of the ore. The first part of this research identifies the existence of different types of ore misclassifications using a database containing ore extraction records from the Kiruna mine. The second part focuses on using these identified misclassification errors and a Kiruna cost model to quantify the cost of the misclassification errors to the Kiruna mine. We assume the principal cost of the errors is reflected in the under-utilization of the ore processing mills. Utilizing a VOI framework, we examine the feasibility of purchasing a laser- induced fluorescence (LIF) analyzer as a source of additional information on extracted ore quality for the Kiruna mine. We find, given certain assumptions, that it is beneficial to the mine to purchase 10 LIFs (one for each production area).

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