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The success of a cement production project depends on the raw material supply. Longterm quarry production planning (LTQPP) is essential to maintain the supply to the cement plant. The quarry manager usually attempts to fulfil the complicated calculations, ensuring a consistent supply of raw materials to the cement plant while guaranteeing technical and operational parameters in mining. Modern quarry management relies on block models and mathematical algorithms integrated into the software to optimize the LTQPP. However, this method is potentially sensitive to geological uncertainty in resource estimation, resulting in the deviation of the supply production of raw materials. More importantly, quarry managers lack the means to deal with these requirements of LTQPP. This research develops a stochastic optimization framework based on the combination of geostatistical simulation, clustering, and optimization techniques to optimize the LTQPP. In this framework, geostatistical simulation techniques aim to model the quarry deposit while capturing the geological uncertainty in resource estimation. The clustering techniques are to aggregate blocks into selective mining cuts that reduce the optimization problem size and generate solutions in a practical timeframe. Optimization techniques were deployed to develop a new mathematical model to minimize the cost of producing the raw mix for the cement plant and mitigate the impact of geological uncertainty on the raw material supply. Matlab programming platform was chosen for implementing the clustering and optimization techniques and creating the software application. A case study of a limestone deposit in Southern Vietnam was carried out to verify the proposed framework and optimization models. Geostatistical simulation is applied to capture and transfer geological uncertainty into the optimization process. The optimization model size decreases significantly using the block clustering techniques and allowing generate solutions in a reasonable timeframe on ordinary computers. By considering mining and blending simultaneously, the optimization model minimizes the additive purchases to meet blending requirements and the amount of material sent to the waste dump. The experiments are also compared with the traditional optimization framework currently used for the deposit. The comparisons show a higher chance of ensuring a consistent supply of raw materials to the cement plant with a lower cost in the proposed framework. These results proved that the proposed framework provides a powerful tool for planners to optimize the LTQPP while securing the raw material supply in cement operations under geological uncertainty.:Title page _ i Declaration_ ii Acknowledgements _ i Publications during candidature_ii Abstract _iii Table of contents _v List of figures_viii List of tables _ xi List of abbreviations _xii Chapter 1 . Introduction _1 1.1 Background _1 1.2 Statement of the problem _2 1.3 Research aims and objectives_ 3 1.4 Scope of research _4 1.5 Research methodology _ 4 1.6 Significance of theresearch_5 1.7 Organization of thesis _6 Chapter 2 . Literature review _ 8 2.1 Introduction _ 8 2.2 Cement raw materials _8 2.3 Cement production process _ 8 2.3.1 Raw material recovery _9 2.3.2 Raw material processing_10 2.4 Impact of raw materials on the cement production process _12 2.5 Quarry planning and optimization _13 2.6 Long-term production planning (LTPP) problem _14 2.6.1 Deterministic approaches to solve the LTPP problem_15 2.6.2 Stochastic approaches for solving the LTPP problem_21 2.7 Conclusion_26 Chapter 3 . A stochastic optimization framework for LTQPP problem_28 3.1 Introduction_28 3.2 Deposit simulation_29 3.2.1 Simulating the rock type domains using SIS_30 3.2.2 Simulating the chemical grades within each domain conditionally to rock type domains, using SGS_30 3.3 Block clustering _31 3.4 The mathematical formulation for the LTQPP problem_32 3.4.1 Notation_34 3.4.2 Mathematical formulation_36 3.5 Numerical modelling_39 3.5.1 Clustering _39 3.5.2 SMIP formulation_41 3.6 Conclusion _47 Chapter 4 . Hierarchical simulation of cement raw material deposit_ 49 4.1 Introduction _49 4.2 Research area _ 50 4.2.1 General description_50 4.2.2. Data set_50 4.3. Application of hierarchical simulation _53 4.3.1 Rock-type simulation _ 53 4.3.2 Grade simulation _60 4.4. Discussion_73 4.5. Conclusion_76 Chapter 5 . Application of the stochastic optimization framework_77 5.1 Introduction_77 5.2 Implementation of KHRA _77 5.3 Implementation of the SMIP model _78 5.3.1 Sensitivity of the penalty cost _80 5.3.2 The effectiveness of the SMIP model _82 5.4 Risk mitigation _85 5.5 Conclusion _87 Chapter 6 . Conclusions and future works _ 89 6.1 Conclusions _89 6.2 Future works _91 References_ 93 Appendix I. Software Application _100 A.I.1 Introduction _100 A.I.2 Input preparation _101 A.I.2.1 Format of block model input _101 A.I.2.2 Import block model input _102 A.I.2.2 Cost assignment _104 A.I.2.3 Size reduction _ 107 A.I.3 Optimization _110 A.I.3.1 Destination _110 A.I.3.2 Production capacity _ 111 A.I.3.3 Additive purchase _ 111 A.I.3.4 Pit slopes _ 111 A.I.3.5 Optimization _ 112 A.I.4 Visualization of optimization results _112

The production of raw materials through mining projects is nowadays very challenging, mainly due to the rapid progress in the industrial and technological fields. On the one hand, they have to fulfill industries' requirements in their demand for materials while making a profit based on the current technologies. On the other hand, they should consider all other limitations, primarily environmental and social challenges that are confronting. The transportation system in any mining project is one of the most significant parts, especially in the technical and economic issues. It must transfer the planned volume of ore/waste that the whole stream of the mining process would not be interrupted and, it can cover the technical challenges and the costs imposed on the project. Additionally, it should be designed and selected to have the lowest environmental impact and the highest safety during the operation. Accordingly, a transportation system selection process that considers all these factors is one of the challenging issues in any mining project. Although the Truck-Shovel system is known as the conventional transportation in open-pit mines, which is preferable because of the low capital cost and high flexibility, it still imposes a high rate of operating costs, safety issues as well as environmental footprints. In-Pit Crushing and Conveying (IPCC) systems are the alternative transportation systems for the Truck-Shovel systems, in which the material is crushed inside the mine’s pit limit and transferred into the outside through conveyor belts. Although these systems are not new, they are mostly neglected as a transportation option basically due to the high capital cost and low flexibility. On the contrary, they can offer more environmentally friendly and safer working areas and a lower operating cost. According to these facts, each transportation system is preferable in a couple of technical, economic, environmental, safety, and social issues. Accordingly, in each circumstance, one or more of these systems can be used in the mining project. However, there is not yet a way or tool that investigates the transportation system selection along with the mine life that takes into account all of these factors. To fill this gap, this project aims to define a model to introduce all these elements while it is interactively connected throughout the mine life. For this and as the first step, the system dynamics modeling is defined and used to build the model for all the technical, economic, environmental, safety, and social factors. As an output of this step, software entitled “TEcESaS Indexes” is designed and produced through Venapp that makes working with the model comfortable. As the second step, a selection method based on the Analytical Hierarchy Process (AHP) is performed that the transportation system selection regarding all the mentioned factors can be made. As the output in this step, the “Sustainability Index” software programmed in the Java language is developed. Considering a hypothetical copper open-pit mine as the case study and implementing the designed software, the results show although the Truck-Shovel system should be used in the first two years of the project (2016 and 2017) in the single expert and deterministic mode, the Fully Mobile In-Pit Crushing and Conveying (FMIPCC) system shows the highest sustainability index among other transportation systems from 2018 until the end of the mine life. While in the group decision making and deterministic simulation, the Truck-Shovel system should be utilized from 2016 to 2020. Additionally, in the group decision making and stochastic mode, the FMIPCC is the selected transportation system with the highest sustainability index probability.

Verflüssigungserscheinungen von Böden wurden bisher meistens im Zusammenhang mit zyklischen, undrainierten Beanspruchungen infolge von Erdbeben untersucht. Allerdings sind in jüngerer Vergangenheit größere Verflüssigungsereignisse in Kippengeländen ehemaliger Braunkohlentagebaue aufgetreten, welche nicht durch Erdbeben ausgelöst wurden. Im Allgemeinen sind die Eigenschaften und der Zustand des anstehenden Bodens, die Größe und Art der zyklischen oder statischen Einwirkung sowie die hydraulischen Verhältnisse Einflussparameter, welche das undrainierte Scherverhalten, und somit die Verflüssigung, kontrollieren. Das steady state-Konzept stellt eine Möglichkeit zur Modellierung des Spannungsdehnungs-Verhaltens dar. Der Einfluss des Feinkorngehaltes auf die Lage und Funktion der steady state-Linie experimentell untersucht. Danach wird das steady state-Konzept angewendet, um das Verflüssigungspotenzial von Lausitzer Kippensanden zu untersuchen und quantitativ einzuschätzen.

As ore grades decline, waste rock to ore ratios increase and mines become progressively deeper mining operations face challenges in more complex scenarios. Today´s predominant means of material transport in hard-rock surface mines are conventional mining trucks however despite rationalisation efforts material transportation cost increased significantly over the last decades and currently reach up to 60% of overall mining. Thus, considerations and efforts to reduce overall mining costs, promise highest success when focusing on the development of more economic material transport methods. Semi-mobile in-pit crusher and conveyor (SMIPCC) systems represent a viable, safer and less fossil fuel dependent alternative however its viability is still highly argued as inadequate methods for the long term projection of system capacity leads to high uncertainty and consequently higher risk. Therefore, the objective of this thesis is to develop a structured method for the determination of In-pit crusher and conveyor SMIPCC system that incorporates the random behaviour of system elements and their interaction. The method is based on a structured time usage model specific to SMIPCC system supported by a stochastic simulation. The developed method is used in a case study based on a hypothetical mine environment to analyse the system behaviour with regards to time usage model component, system capacity, and cost as a function of truck quantity and stockpile capacity. Furthermore, a comparison between a conventional truck & shovel system and SMIPCC system is provided. Results show that the capacity of a SMIPCC system reaches an optimum in terms of cost per tonne, which is 24% (22 cents per tonne) lower than a truck and shovel system. In addition, the developed method is found to be effective in providing a significantly higher level of information, which can be used in the mining industry to accurately project the economic viability of implementing a SMIPCC system.