Faculty of Mining and Minerals Technology
http://localhost:8080/xmlui/handle/123456789/216
FMMT2024-03-28T09:51:27ZRisk analysis for discount rate determination in mineral development project: a case study
http://localhost:8080/xmlui/handle/123456789/368
Risk analysis for discount rate determination in mineral development project: a case study
Dzimah, Emmanuel
In Ghana alone, Newmont Ghana owns two subsidiaries, Newmont Ghana Gold Limited which operate the Ahafo North and South project and Newmont Golden Ridge Limited which operate the Akyem mine. The Ahafo South mine which has been in operation since 2006 was given permit to start developing another concession, the Awonsu project. As a result, there was the need for the company to evaluate the economic viability of mining this concession using NPV evaluation method which has discount rate as one of its parameters. The development and exploitation of a mine comes with risks and heavy capital investment with longer payback periods. It is therefore important to consider risk when evaluating a mining projects by calculating a risk premium that will compensate investors for taking additional risks in a mineral project.Without a risk premium, Strategic Mine Planners face a challenge of setting an appropriate discount rate for use in NPV project evaluation while investors will also appreciate risk premium which when added to a risk-free rate of return (Interest rate) to get an adjusted-discount rate will compensate these investors for extra risk taken.
Some major inherent risks such as technical (grade estimation), economic (varying metal prices) and political risks (tax, environmental regulations) and their accompanying probability and impact scores were identified through questionnaires and these two values multiplied to get a risk value for each risk identified. The risk values for each of the risk were summed up to get an overall risk value which was then converted into a risk premium of 9% for the project. An adjusted-discount rate of 28.8% was finally calculated by adding a risk-free rate of return (bond rate) to the risk premium of 9%.
The adjusted- discount rate of 28.8% determined represent the returns that compensate investors for extra risk taken when they decide to ignore other investment instrument and choose this project. With this, investors are now better placed to make sound investment decisions on projects. The researcher therefore recommends that mining companies in Ghana should use a discount rate that is between 25.8% and 39.8% for their mineral projects.
v, 99p. ill.
2020-06-01T00:00:00ZReview of the Ahafo South Mine blasting procedures to achieve zero boulders
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Review of the Ahafo South Mine blasting procedures to achieve zero boulders
Ackom-Ghansah, Michael
Obtaining a good fragmentation for every blast remains an ever-important discussion in the mining parlance as it is the first step towards mineral recovery. Achieving the required fragment sizes with the maximum size (p100 value) being less than 1000 mm after blasting is a major challenge in Newmont Ahafo South Mine. Blasting usually results in excessive proportion of boulders which negatively affect productivity by increasing the time taken for loading, hauling, and crushing. It also increases the cost of operation because of secondary blasting and the fines also result in loss of gold.
Helping the Mine achieve its initiative of zero boulders will positively impact productivity and reduce the cost of operation. The objective of the study is to review the current blast design procedures adopted by Ahafo South Mines in order to identify the causes of the boulders and to optimize the design parameters if necessary, in order to obtain the required fragmentation for the Mine. For this reason, a quality assurance and quality control were done on the existing drilling and blasting procedures to identify the mistakes and optimized (modified) drilling and blasting parameters was obtained through a simulation using the Kuz-Ram Model.
Digital images from the blast shots were taken and analyzed using the Orica Shotplus software and the average result of the two blocks were compared to results from the Kuz-Ram Model. Because of the Kuz-Ram model’s strong predictability, it was used to predict the blast design parameters that would yield optimal fragmentation without any boulder. The image analysis showed an average variation of 16.4% of the expected fragmentation which aided in obtaining an expected maximum size as 603.4 mm other than the 517 mm from the prediction model.
Thus, the Kuz-Ram model was used to predict spacing and burden of 4.2 x 3.5 m (with a powder factor of 1 kg/m3) as the optimal blast design parameter to achieve the Mine’s Initiative
viii, 112p. ill.
2020-07-01T00:00:00ZDevelopment of a Robust Stope Stability Prediction (RSSP) model using machine learning techniques
http://localhost:8080/xmlui/handle/123456789/310
Development of a Robust Stope Stability Prediction (RSSP) model using machine learning techniques
Saadaari, Festus
Assessing stopes' stability is essential in open stope mine design, as unstable
hangingwalls, footwalls, and crowns lead to dilution and safety concerns, thereby
compromising the mine's profitability. In the past, several empirical methods,
predominantly the stability graph method, were used to assess stopes' stability.
However, one of the stability graph method's main challenges is the subjectivity
inherent in determining the stability zones. Since stability graphs are empirical, their
accuracy is dependent on the database used, and no two geological shields may be the
same. This research aimed to address the stability graph method's challenges by
exploring the feasibility of using machine learning techniques to predict open stopes'
stability status in some selected Ghanaian mines. A comprehensive stability database
was established for the research based on available data on block and stope notes,
design and planning, surveying, geological and other geotechnical reports. In all, a total
of 1 067 data was obtained from Newmont GoldCorp Ahafo Mine (NGAM), Kinross
Chirano Gold Mine Limited (CGML) and AngloGold Ashanti Obuasi Mine (AGAG),
with each company contributing 292, 303 and 472 case histories, respectively. 283 of
the database were caved cases; 336 cases were observed as stable, while the remaining
448 were unstable. In this research, thirteen machine learning algorithms were usedDecision Trees Classifier (DTC), Random Forest Classifier (RFC), K Nearest Neighbours
Classifier (KNN), Gaussian Process Classifier (GPC), Extra Trees Classifier (ETC),
Bootstrap Aggregation Classifier (BAC), Adaptive Boosting Classifier (ABC), Gradient
Boosting Classifier (GBC), Quadratic Discriminant Analysis (QDA), Stochastic Gradient
Boosting (SGB), Support Vector Classifier (SVC), Neural Network Classifier (NNC) and
Multi-Layer Perceptron Classifier (MLP). Using the Python programme's train-test split
technique, 853 representing 80% of the database were used for training the models,
while the remaining 214 (20%) were used in evaluating the performance of each of the
models. Eleven performance metrics were used to evaluate the reliability and
generalisability of all thirteen models. This assessment revealed that seven models
(RFC, GBC, KNN, GPC, MLP, NNC and BAC) performed remarkably well and can be
used as alternative stope design tools. However, based on a 95% confidence interval,
the RFC model was the selected model for the study areas considered. The findings of this study could significantly assist mining engineers in designing more reliable open
stopes.
xvi,352p; Ill.
2021-08-01T00:00:00ZBlast-induced ground vibration and air overpressure prediction using artificial intelligence techniques
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Blast-induced ground vibration and air overpressure prediction using artificial intelligence techniques
Arthur, Clement Kweku
Blast-induced ground vibration and air overpressure are considered as the most important
environmental hazards of mining and can damage structures such as buildings, dams and pit slopes. Review of previous studies has shown that some empirical and Artificial Intelligence (AI) models have been proposed to estimate blast-induced ground vibrations and air overpressure. Notable AI techniques applied in prediction of blast-induced ground vibration include Backpropagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Generalised Regression Neural Network (GRNN), Group Method of Data Handling (GMDH), Support Vector Machine (SVM) and Extreme Learning Machine
(ELM). In this study, five techniques, namely Wavelet Neural Network (WNN),
Multivariate Adaptive Regression Splines (MARS), Least Square Support Vector Machine
(LS-SVM), Relevance Vector Machines (RVM) and Gaussian Process Regression (GPR)
are proposed to predict blast-induced ground vibration using 210 blast data sets from Ghana Manganese Company Limited (GMC). Out of the data sets, 130 blast data sets were used to train the models and the remaining 80 to test the developed models. For comparison purpose and ascertaining the suitability of the proposed methods, four empirical techniques (United State Bureau of Mines, Langefors and Kilhstrom, Ambrasey-Hendron and Indian Standard) were also employed. With regards to the air overpressure, BPNN, GMDH, GPR and SVM are the only AI methods applied and captured in the literature. This study therefore tested the capability and applicability of some AI methods, namely RBFNN, GRNN, LS-SVM, RVM, ELM, WNN and MARS, that are yet to be explored in the prediction of air overpressure. To accomplish this task, air overpressure data sets from Newmont Golden Ridge Limited, Akyem Mine was used. In all, 98 data sets were used for the model construction and 73 data sets were used to independently assess the performance of the models formulated. Two empirical predictors, the general predictor model and ‘Newmont Model’, were utilised for the purpose of comparison. In the blast-induced ground vibration interpretations, the statistical results revealed that, four out of the five newly tested AI techniques (LS-SVM, WNN, MARS and GPR) could produce good ground vibration predictions comparing to the AI benchmark methods of BPNN, RNFNN and GRNN. Hence, LS-SVM, WNN, MARS and GPR have been proposed to be used as suitable alternative tools to predict blast-induced ground vibration. In comparing all methods applied, the proposed LS-SVM was the most accurate on the basis of the statistical analyses carried out in this study and thus the selected model for predicting blast-induced ground vibration at Ghana Manganese Company Limited. In the air overpressure prediction interpretations, it was found that four out of the seven methods (GRNN, RBFNN, RVM, and MARS) tested produced comparable and satisfactory results as the widely used BPNN, GPR and SVM and thus could serve as suitable alternatives to the prediction of air overpressure. However, it was found on the basis of the statistical analyses carried out that, the BPNN was the selected model for the prediction of air overpressure for Newmont Golden Ridge Limited, Akyem Mine. The overall analyses of the study showed that the AI techniques are superior in predicting both blast-induced ground vibration and air overpressure to the empirical predictors usually employed in most mining and civil engineering industries. To this end, a user-friendly AI-based software package was developed on the MATLAB platform and can be used in the industry for prior prediction of the blast-induced ground vibration and air overpressure based on the blast design in the mining industry.
xv, 260p, ill.
2019-05-01T00:00:00Z