Now showing items 1-6 of 6

    • TEST Master's Projects 9/25/17

      CHISUM (2017-09)
      TEST Master's Projects 9/25/17
    • TEST College of Liberal Arts 9/25/17

      CHISUM (2017-09)
      TEST College of Liberal Arts 9/25/17
    • Data mining for mine-mill ore grade reconciliation at Erdenet Mining Corporation

      Sarantsatsral, Narmandakh (2016-11)
      This project investigates the relationship between the mined ore and the produced copper at the Erdenet Mining Corporation (EMC) surface copper mine in Mongolia. Four and half years of data (from 2011-2015) was obtained from the open pit mine and mineral processing plant of EMC. The mine-mill data was collected on a shift basis. The data was examined carefully using process knowledge and exploratory data analysis techniques to detect and eliminate errors. Ultimately, two years of data (2013-2014) was selected for further analysis. As is common in all mines, the material flow between the mine and mill is complicated by numerous stockpiles. The copper grade going into a stockpile may not be directly related to the copper grade exiting a stockpile. Therefore, data mining techniques applied to detect the relationship between mined ore and milled copper had to overcome the complications introduced by the presence of stockpiles. Multiple data sets were created by aggregating the original dataset by different periods. For example, in one case, data was aggregated by three shifts, to convert the data from shift-basis to daily-basis. Aggregation is an ideal way to absorb variations in material flow (tonnage and grade) between mine and mill. Data was aggregated by 3, 4, 5, 6, 7, 8, 9, 10, 11 and 12 shifts ("aggregated widths" or AW). Correlation analysis was then conducted on each version of the data to determine if a relationship existed between mine data and mill data. Correlation was computed for various period lengths, but not exceeding 28 days. Therefore, in a given year, several correlation plots were produced at each aggregated width. The number of times the correlation coefficient exceeded 0.8 in a year was measured. Results showed that correlation improved with aggregation width. The highest correlations occurred at AW of 7 or 8. This suggests that the stockpiles aggregate material for 2-3 days. Correlation analysis also included examining a time shift ("lag") between mine data and mill data. This is useful to detect whether material takes a certain amount of time before it is processed and produced as copper. However, results indicated that once the data was aggregated, a time lag greater than 0 only worsened correlation.
    • Calibration of an on-line analyzer using neural network modeling

      Yu, Shaohai (2003-08)
      The goal of the project was to predict the ash content of raw coal in real time using the Americium-137 and Cesium-241 scintillation counts from an on-line analyzer. Rather than regression methods (that are current industrial practice), neural networks were used to map the scintillation counts to percentage ash. Quick stop training was used to prevent overfitting The noise and sparseness of the data required that the training, calibration and prediction subsets are statistically similar to each other. Therefore, Kohonen networks were first used to detect the features present in the data set. Three subsets were then built such that they had representative members from each feature. Neural network models were developed for the screened coal, the unscreened coal and the combined data respectively. The results show that the performance of the combined model was comparable to the performance with two different models for the screened and unscreened data. Due to the variance in the sample data, the neural networks (screened, unscreened and combined) did not predict individual samples well. The network predictions were, however, accurate on the average. Compared to the common regression approach, neural network modeling demonstrated much better performance in ash prediction based on certain criteria.
    • Investigation and development of a mathematical model for the oxidation of cyanide in the INCO SO₂/O₂ process

      Oleson, James L. (2003-12)
      The purpose of this study was to develop a mathematical model to describe the oxidation of cyanide with SO₂ as proposed in the INCO process. This research employed a direct method for measuring the change in cyanide concentration with respect to time as affected by varying concentrations of SO₂ and copper and pH. This model may be applicable in determining optimum conditions in a process well known and used in the mining industry.
    • An experimental investigation of natural freezing and biopolymers for permeability modification to reduce the volume of dense non-aqueous phase liquids in groundwater

      D'Cunha, Neil John (2004-12)
      Dense Non-Aqueous Phase Liquid (DNAPL) contamination is one of the major environmental concerns today. DNAPL can remain in significant quantities as residual contaminants in the low permeability zones even after the bulk phase has been removed. As the drive fluid sweeps through the aquifer it follows the path of least resistance, which is the high permeability zone. Thus the contaminants trapped in the low permeability zones remain as residuals and serve as a source for prolonged contamination. Conventional remediation techniques are ill-equipped to deal with the heterogeneities of the aquifers. Various techniques to enhance the efficiency of the conventional methods are tried without significant success. Reducing the temperature of soil formations can modify aquifer flow paths. The natural freezing of soils in winter may be used effectively to modify the flow paths. In summer, permeability modification can be accomplished by emplacement of microbial polymer gels. In this thesis, we have investigated using a laboratory scale one dimensional column experiment, a novel technique to reduce the volume of residual DNAPL using a combination of natural freezing in winter and biopolymer in summer.