Control of Blasting for Ore Blending and Autogenous Mill Performance at Hibbing Taconite Company- Preliminary Findings

VanDelinder, Peter R. ; Eloranta, Jack W. ; Orobona, Michael J. T.
Organization: Society for Mining, Metallurgy & Exploration
Pages: 16
Publication Date: Jan 1, 2004
Ore blending for process optimization is becoming an increasing factor for managing cost and productivity. Economic mining and processing a very low value ore in an extremely competitive market results in constant cost-cummng initiatives. The inherent conflict between the lowest possible mining cost and the lowest possible milling cost is reviewed here in the context of blasting for autogenous mills. Iron ore blending focuses on a number of properties-liberation values, weight recovery and concentrate silica being the most important. Other chemical and metallurgical factors are also commonly controlled. Recent research has fueled an emerging interest in blending on physical ore properties as well. Fully autogenous mills rely on the large, competent fraction of crushed feed to act as grinding media. Previous test work indicated that Hibtac mills require specific amounts of 6- to 10-inch ore. High recirculating loads require a steady influx of large rock fragments. Blast designs, therefore, are presently characterized by wide patterns and low powder factors. Current efforts are aimed at improving mill throughput while reducing overall energy costs. Based on research and published case studies and the advent of optical measuring devices, Hibbing Taconite Company has embarked on a blast optimization program to identify optimum mill feed and to design blast fragmentation goals for each geological unit and each mining area. This paper outlines the preliminary findings of an ongoing, broad-based team effort which requires close cooperation of geologists, mine engineers, crushing and milling personnel. Three specific areas of investigation are reviewed: I) Historical relationships between powder factor and mill performance, 2) Blast fragmentation modeling using the Kuz-Ram model, and 3) Drill
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