A operação e gerência de empreendimentos mineiros são tarefas normalmente difíceis e complexas. Para otimizar toda a operação, os engenheiros precisam lidar com muitos aspectos técnicos e restrições, como a modelagem geológica, estimativa de reservas, determinação da necessidade de blendagem, projeto das cavas ótimas e operacionais, custos operacionais, questões ambientais, entre outros. Nesse sentido, o posicionamento de infraestruturas de superfície é um dos pontos críticos dentro do planejamento de mina. Aproximar as estruturas da cava, com o intuito de reduzir custos operacionais, pode interferir em eventuais expansões da cava em novos e favoráveis cenários. Nesses casos, impactos no valor presente líquido (VPL) do projeto são inevitáveis e precisam ser tratados tecnicamente, avaliando um grande número de cenários alternativos para delinear uma estratégia que incremente a lucratividade do projeto. O objetivo deste estudo é avaliar, por meio de comparações de VPL entre diferentes cenários de múltiplas restrições de superfície, sob incerteza geológica, a possibilidade de mover pilhas de estéril e outras infraestruturas de suas atuais posições e/ou definir prioridades e mensurar o impacto que cada restrição representa na lucratividade do projeto. A metodologia foi aplicada a uma mina de fosfato para ilustrar como determinar a melhor alternativa em uma perspectiva de planejamento de mina de longo prazo. Utilizando o método de cavas híbridas, aplicado ao modelo de teores simulados, foi possível identificar zonas de probabilidade de ocorrência dentro da cava matemática, o que forneceu informações cruciais para auxiliar na tomada de decisão a respeito da necessidade de relocação de estruturas. / The operation and management of mining enterprises are usually difficult and complex tasks. To optimize the entire operation the engineers must deal with several technical aspects and constraints, such as orebody modelling, reserves estimation, determination of blending necessity, optimum and operational pit designs, operational costs, environmental issues, among others. In this sense, locating surface infrastructures is one of the most critical mine planning concerns. Approximating these structures to the pit, in order to reduce the operational costs, might interfere with future pit expansions in new favorable scenarios. In such cases, impacts on project’s net present value (NPV) are inevitable and must be deal technically, evaluating several alternative scenarios to delineate a strategy to maximize profitability. The aim of this study is to evaluate, through NPV comparisons considering different scenarios with multiple constraints, under geological uncertainty, the possibility of moving waste piles and infrastructure buildings from their current position and/or defining priorities to after measuring the impact that each constraint represents on the project´s profitability. The methodology will be applied to a phosphate mine, to determine the best alternative from a long-term mine planning perspective. Using the hybrid pits method, applied to a simulated grades model, allowed the identification os occurrence probability zones within mathematical pit, providing critical data to support decision making regarding infrastructure relocation.

A operação e gerência de empreendimentos mineiros são tarefas normalmente difíceis e complexas. Para otimizar toda a operação, os engenheiros precisam lidar com muitos aspectos técnicos e restrições, como a modelagem geológica, estimativa de reservas, determinação da necessidade de blendagem, projeto das cavas ótimas e operacionais, custos operacionais, questões ambientais, entre outros. Nesse sentido, o posicionamento de infraestruturas de superfície é um dos pontos críticos dentro do planejamento de mina. Aproximar as estruturas da cava, com o intuito de reduzir custos operacionais, pode interferir em eventuais expansões da cava em novos e favoráveis cenários. Nesses casos, impactos no valor presente líquido (VPL) do projeto são inevitáveis e precisam ser tratados tecnicamente, avaliando um grande número de cenários alternativos para delinear uma estratégia que incremente a lucratividade do projeto. O objetivo deste estudo é avaliar, por meio de comparações de VPL entre diferentes cenários de múltiplas restrições de superfície, sob incerteza geológica, a possibilidade de mover pilhas de estéril e outras infraestruturas de suas atuais posições e/ou definir prioridades e mensurar o impacto que cada restrição representa na lucratividade do projeto. A metodologia foi aplicada a uma mina de fosfato para ilustrar como determinar a melhor alternativa em uma perspectiva de planejamento de mina de longo prazo. Utilizando o método de cavas híbridas, aplicado ao modelo de teores simulados, foi possível identificar zonas de probabilidade de ocorrência dentro da cava matemática, o que forneceu informações cruciais para auxiliar na tomada de decisão a respeito da necessidade de relocação de estruturas. / The operation and management of mining enterprises are usually difficult and complex tasks. To optimize the entire operation the engineers must deal with several technical aspects and constraints, such as orebody modelling, reserves estimation, determination of blending necessity, optimum and operational pit designs, operational costs, environmental issues, among others. In this sense, locating surface infrastructures is one of the most critical mine planning concerns. Approximating these structures to the pit, in order to reduce the operational costs, might interfere with future pit expansions in new favorable scenarios. In such cases, impacts on project’s net present value (NPV) are inevitable and must be deal technically, evaluating several alternative scenarios to delineate a strategy to maximize profitability. The aim of this study is to evaluate, through NPV comparisons considering different scenarios with multiple constraints, under geological uncertainty, the possibility of moving waste piles and infrastructure buildings from their current position and/or defining priorities to after measuring the impact that each constraint represents on the project´s profitability. The methodology will be applied to a phosphate mine, to determine the best alternative from a long-term mine planning perspective. Using the hybrid pits method, applied to a simulated grades model, allowed the identification os occurrence probability zones within mathematical pit, providing critical data to support decision making regarding infrastructure relocation.

A operação e gerência de empreendimentos mineiros são tarefas normalmente difíceis e complexas. Para otimizar toda a operação, os engenheiros precisam lidar com muitos aspectos técnicos e restrições, como a modelagem geológica, estimativa de reservas, determinação da necessidade de blendagem, projeto das cavas ótimas e operacionais, custos operacionais, questões ambientais, entre outros. Nesse sentido, o posicionamento de infraestruturas de superfície é um dos pontos críticos dentro do planejamento de mina. Aproximar as estruturas da cava, com o intuito de reduzir custos operacionais, pode interferir em eventuais expansões da cava em novos e favoráveis cenários. Nesses casos, impactos no valor presente líquido (VPL) do projeto são inevitáveis e precisam ser tratados tecnicamente, avaliando um grande número de cenários alternativos para delinear uma estratégia que incremente a lucratividade do projeto. O objetivo deste estudo é avaliar, por meio de comparações de VPL entre diferentes cenários de múltiplas restrições de superfície, sob incerteza geológica, a possibilidade de mover pilhas de estéril e outras infraestruturas de suas atuais posições e/ou definir prioridades e mensurar o impacto que cada restrição representa na lucratividade do projeto. A metodologia foi aplicada a uma mina de fosfato para ilustrar como determinar a melhor alternativa em uma perspectiva de planejamento de mina de longo prazo. Utilizando o método de cavas híbridas, aplicado ao modelo de teores simulados, foi possível identificar zonas de probabilidade de ocorrência dentro da cava matemática, o que forneceu informações cruciais para auxiliar na tomada de decisão a respeito da necessidade de relocação de estruturas. / The operation and management of mining enterprises are usually difficult and complex tasks. To optimize the entire operation the engineers must deal with several technical aspects and constraints, such as orebody modelling, reserves estimation, determination of blending necessity, optimum and operational pit designs, operational costs, environmental issues, among others. In this sense, locating surface infrastructures is one of the most critical mine planning concerns. Approximating these structures to the pit, in order to reduce the operational costs, might interfere with future pit expansions in new favorable scenarios. In such cases, impacts on project’s net present value (NPV) are inevitable and must be deal technically, evaluating several alternative scenarios to delineate a strategy to maximize profitability. The aim of this study is to evaluate, through NPV comparisons considering different scenarios with multiple constraints, under geological uncertainty, the possibility of moving waste piles and infrastructure buildings from their current position and/or defining priorities to after measuring the impact that each constraint represents on the project´s profitability. The methodology will be applied to a phosphate mine, to determine the best alternative from a long-term mine planning perspective. Using the hybrid pits method, applied to a simulated grades model, allowed the identification os occurrence probability zones within mathematical pit, providing critical data to support decision making regarding infrastructure relocation.

Waterflooding is a common type of oil recovery techniques where water is pumped into the reservoir for increased productivity. Reservoir states change with time, as such, different injection and production settings will be required to lead the process to optimal operation which is actually a dynamic optimization problem. This could be solved through optimal control techniques which traditionally can only provide an open-loop solution. However, this solution is not appropriate for reservoir production due to numerous uncertain properties involved. Models that are updated through the current industrial practice of ‘history matching’ may fail to predict reality correctly and therefore, solutions based on history-matched models may be suboptimal or non-optimal at all. Due to its ability in counteracting the effects uncertainties, direct feedback control has been proposed recently for optimal waterflooding operations. In this work, two feedback approaches were developed for waterflooding process optimization. The first approach is based on the principle of receding horizon control (RHC) while the second is a new dynamic optimization method developed from the technique of self-optimizing control (SOC). For the SOC methodology, appropriate controlled variables (CVs) as combinations of measurement histories and manipulated variables are first derived through regression based on simulation data obtained from a nominal model. Then the optimal feedback control law was represented as a linear function of measurement histories from the CVs obtained. Based on simulation studies, the RHC approach was found to be very sensitive to uncertainties when the nominal model differed significantly from the conceived real reservoir. The SOC methodology on the other hand, was shown to achieve an operational profit with only 2% worse than the true optimal control, but 30% better than the open-loop optimal control under the same uncertainties. The simplicity of the developed SOC approach coupled with its robustness to handle uncertainties proved its potentials to real industrial applications.

Waterflooding is a common type of oil recovery techniques where water is pumped into the reservoir for increased productivity. Reservoir states change with time, as such, different injection and production settings will be required to lead the process to optimal operation which is actually a dynamic optimization problem. This could be solved through optimal control techniques which traditionally can only provide an open-loop solution. However, this solution is not appropriate for reservoir production due to numerous uncertain properties involved. Models that are updated through the current industrial practice of ‘history matching’ may fail to predict reality correctly and therefore, solutions based on history-matched models may be suboptimal or non-optimal at all. Due to its ability in counteracting the effects uncertainties, direct feedback control has been proposed recently for optimal waterflooding operations. In this work, two feedback approaches were developed for waterflooding process optimization. The first approach is based on the principle of receding horizon control (RHC) while the second is a new dynamic optimization method developed from the technique of self-optimizing control (SOC). For the SOC methodology, appropriate controlled variables (CVs) as combinations of measurement histories and manipulated variables are first derived through regression based on simulation data obtained from a nominal model. Then the optimal feedback control law was represented as a linear function of measurement histories from the CVs obtained. Based on simulation studies, the RHC approach was found to be very sensitive to uncertainties when the nominal model differed significantly from the conceived real reservoir. The SOC methodology on the other hand, was shown to achieve an operational profit with only 2% worse than the true optimal control, but 30% better than the open-loop optimal control under the same uncertainties. The simplicity of the developed SOC approach coupled with its robustness to handle uncertainties proved its potentials to real industrial applications.

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