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Title: Total Ore Processing Integration and Management

Abstract

This report outlines the technical progress achieved for project DE-FC26-03NT41785 (Total Ore Processing Integration and Management) during the period 01 October through 31 December of 2005. Graphical analysis of blast patterns according to drill monitor data is continuing. Multiple linear regression analysis of 16 mine and mill variables (powder factor, two modeled size fractions, liberation index, predicted grind, total crude Fe, Satmagan Fe, sat ratio, DSC, geologic blend, ambient temperature, cobbing hours, feeder plugs, and percent feeder run time-of-mill time) indicates that December variations in plant performance are generally predictable (Figure 1). The outlier on December 28th coincides with low cobbing availability and equipment downtime. Mill productivity appeared to be most influenced, as usual, by ore quality as indicated by the liberation index--the higher the liberation index, the lower the throughput. The upcoming quarter will be concerned with wrapping up the work in progress, such as the detailed statistical analyses, and writing a final report. Hibtac Mine engineers are evaluating neural network software to determine its utility for modeling, and eventually predicting, mill throughput.

Authors:
Publication Date:
Research Org.:
University of Missouri
Sponsoring Org.:
USDOE
OSTI Identifier:
876104
DOE Contract Number:
FC26-03NT41785
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; AMBIENT TEMPERATURE; AVAILABILITY; ENGINEERS; MANAGEMENT; MONITORS; NEURAL NETWORKS; ORE PROCESSING; PERFORMANCE; PRODUCTIVITY; REGRESSION ANALYSIS; SIMULATION

Citation Formats

Leslie Gertsch. Total Ore Processing Integration and Management. United States: N. p., 2006. Web. doi:10.2172/876104.
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Leslie Gertsch. Total Ore Processing Integration and Management. United States. doi:10.2172/876104.
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Leslie Gertsch. Mon . "Total Ore Processing Integration and Management". United States. doi:10.2172/876104. https://www.osti.gov/servlets/purl/876104.
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@article{osti_876104,
title = {Total Ore Processing Integration and Management},
author = {Leslie Gertsch},
abstractNote = {This report outlines the technical progress achieved for project DE-FC26-03NT41785 (Total Ore Processing Integration and Management) during the period 01 October through 31 December of 2005. Graphical analysis of blast patterns according to drill monitor data is continuing. Multiple linear regression analysis of 16 mine and mill variables (powder factor, two modeled size fractions, liberation index, predicted grind, total crude Fe, Satmagan Fe, sat ratio, DSC, geologic blend, ambient temperature, cobbing hours, feeder plugs, and percent feeder run time-of-mill time) indicates that December variations in plant performance are generally predictable (Figure 1). The outlier on December 28th coincides with low cobbing availability and equipment downtime. Mill productivity appeared to be most influenced, as usual, by ore quality as indicated by the liberation index--the higher the liberation index, the lower the throughput. The upcoming quarter will be concerned with wrapping up the work in progress, such as the detailed statistical analyses, and writing a final report. Hibtac Mine engineers are evaluating neural network software to determine its utility for modeling, and eventually predicting, mill throughput.},
doi = {10.2172/876104},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jan 30 00:00:00 EST 2006},
month = {Mon Jan 30 00:00:00 EST 2006}
}
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Technical Report:
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DOI:  10.2172/876104
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  • ;
    A new dataset to illustrate ordinary, non-segregated operation of the mine and mill has been collected. Beginning in mid-November, it ended on 31 December, 2004. Drill monitoring data for several blast patterns is being analyzed. Figures 1 through 6 represent one of the patterns. Sample preparation for laboratory rock strength tests is underway, for comparison with the density and point-load test results measured last summer. The relationships among data mined from the databases and the ore segregation tests of both mines are being examined, mainly through use of multiple regression analysis. The study is ongoing.

  • ;
    The lessons learned from ore segregation test No.3 were presented to Minntac Mine personnel during the reporting period. Ore was segregated by A-Factor, with low values going to Step 1/2 and high values going to Step 3. During the test, the mine maintained the best split possible for the given production and location constraints. During the test, Step 1&2 A-Factor was lowered more than Step 3 was raised. All other ore quality changes were not manipulated, but the segregation by A-Factor affected most of the other qualities. Magnetic iron, coarse tails, fine tails, silica, and grind changed in response tomore » the split. Segregation was achieved by adding ore from HIS to the Step 3 blend and lowering the amount of LC 1&2 and somewhat lowering the amount of LC 3&4. Conversely, Step 1&2 received less HIS with a corresponding increase in LC 1&2. The amount of IBC was increased to both Steps about one-third of the way into the test. For about the center half of the test, LC 3&4 was reduced to both Steps. The most noticeable layer changes were, then: an increase in the HIS split; a decrease in the LC 1&2 split; adding IBC to both Steps; and lowering LC 3&4 to both Steps. Statistical analysis of the dataset collected during ordinary, non-segregated operation of the mine and mill is continuing. Graphical analysis of blast patterns according to drill monitor data was slowed by student classwork. It is expected to resume after the semester ends in May.« less

  • ;
    This report outlines the technical progress achieved for project DE-FC26-03NT41785 (Total Ore Processing Integration and Management) during the period 01 July through 30 September of 2005. This ninth quarterly report discusses the activities of the project team during the period 1 July through 30 September 2005. Richard Gertsch's unexpected death due to natural causes while in Minnesota to work on this project has temporarily slowed progress. Statistical analysis of the Minntac Mine data set for late 2004 is continuing. Preliminary results raised several questions that could be amenable to further study. Detailed geotechnical characterization is being applied to improve themore » predictability of mill and agglomerator performance at Hibtac Mine.« less

  • ;

  • This report outlines the technical progress achieved for project DE-FC26-03NT41785 (Total Ore Processing Integration and Management) during the period 01 January through 31 March of 2006. (1) Work in Progress: Minntac Mine--Graphical analysis of drill monitor data moved from two-dimensional horizontal patterns to vertical variations in measured and calculated parameters. The rock quality index and the two dimensionless ({pi}) indices developed by Kewen Yin of the University of Minnesota are used by Minntac Mine to design their blasts, but the drill monitor data from any given pattern is obviously not available for the design of that shot. Therefore, the blastmore » results--which are difficult to quantify in a short time--must be back-analyzed for comparison with the drill monitor data to be useful for subsequent blast designs. {pi}{sub 1} indicates the performance of the drill, while {pi}{sub 2} is a measure of the rock resistance to drilling. As would be expected, since a drill tends to perform better in rock that offers little resistance, {pi}{sub 1} and {pi}{sub 2} are strongly inversely correlated; the relationship is a power function rather than simply linear. Low values of each Pi index tend to be quantized, indicating that these two parameters may be most useful above certain minimum magnitudes. (2) Work in Progress: Hibtac Mine--Statistical examination of a data set from Hibtac Mine (Table 1) shows that incorporating information on the size distribution of material feeding from the crusher to the autogenous mills improves the predictive capability of the model somewhat (43% vs. 44% correlation coefficient), but a more important component is production data from preceding days (26% vs. 44% correlation coefficient), determined using exponentially weighted moving average predictive variables. This lag effect likely reflects the long and varied residence times of the different size fragments in the grinding mills. The rock sizes are also correlated with the geologic layers from which they originate. Additional predictive parameters include electric power drawn by the crusher and the inverse of the average grind index of the ore being milled.« less