Student Number : 9506626A - MSc (Eng) research report - School of Mining - Faculty of Engineering and the Built Environment / Significant research has been carried out over the years into what effect blast designs and techniques have on the final product in the mining process. There are numerous parameters that can be altered to deliver downstream benefits – the key is to determine which changes are appropriate for the rock body in question. A project is currently underway at Thabazimbi Iron Ore Mine (Northern Province, South Africa) to improve the operational efficiency through attention to the blasting operation. Previous research suggests changing fragmentation will have an effect on mining efficiency, but no definitive model has been developed directly linking the two. Using data collected during the project, the author developed a sensitivity analysis tool, which defines the effect of changing fragmentation on overall mine efficiency. This prediction model was based partly on theory and partly on empirical information gathered from mine databases and personnel. Over the course of this project, this model was validated through the practical implementation of the theory behind its development. This involved decreasing powder factors through increasing the drilling pattern, thus changing the resulting fragmentation of the muckpile. Subsequently, downstream effects on mining efficiency were monitored and these results were recorded in the model. The proven model was then used to identify areas of opportunity for improvement. In this report two areas are discussed, firstly, the implementation of a doped emulsion replacing ANFO across the mine, and secondly the introduction of electronic detonators. The second option would require further test work to develop confidence in the assumptions made in the model, concerning the effect of timing accuracy on fragmentation. This research report covers the background to the project, an explanation of the model and the final results obtained.

Chen, Chang, Wen and Lin (2013)提出馬可夫調控跳躍過程模型(MMJDM)描述股價指數報酬率，布朗運動項、跳躍項之頻率與市場狀態有關。然而，利率並非常數，本論文以狀態轉換模型配適零息債劵之動態過程，提出狀態轉換下的利率與具跳躍風險的股票報酬之二維模型(MMJDMSI)，並以1999年至2013年的道瓊工業指數與S&P 500指數和同期間之一年期美國國庫劵價格為實證資料，採用EM演算法取得參數估計值。經由概似比檢定結果顯示無論道瓊工業指數還是S&P 500指數，狀態轉換下利率與跳躍風險之股票報酬二維模型更適合描述報酬率。接著，利用Esscher轉換法推導出各模型下的股價指數之歐式買權定價公式，再對MMJDMSI模型進行敏感度分析以評估模型參數發生變動時對於定價公式的影響。最後，以實證資料對各模型進行模型校準及計算隱含波動度，結果顯示MMJDMSI在價內及價外時定價誤差為最小或次小，且此模型亦能呈現出波動度微笑曲線之現象。 / To model asset return, Chen, Chang, Wen and Lin (2013) proposed Markov-Modulated Jump Diffusion Model (MMJDM) assuming that the Brownian motion term and jump frequency are all related to market states. In fact, the interest rate is not constant, Regime-Switching Model is taken to fit the process of the zero-coupon bond price, and a bivariate model for interest rate and stock index return with regime-switching and dependent jump risks (MMJDMSI) is proposed. The empirical data are Dow Jones Industrial Average and S&P 500 Index from 1999 to 2013, together with US 1-Year Treasury Bond over the same period. Model parameters are estimated by the Expectation-Maximization (EM) algorithm. The likelihood ratio test (LRT) is performed to compare nested models, and MMJDMSI is better than the others. Then, European call option pricing formula under each model is derived via Esscher transformation, and sensitivity analysis is conducted to evaluate changes resulted from different parameter values under the MMJDMSI pricing formula. Finally, model calibrations are performed and implied volatilities are computed under each model empirically. In cases of in-the-money and out-the-money, MMJDMSI has either the smallest or the second smallest pricing error. Also, the implied volatilities from MMJDMSI display a volatility smile curve.