Optimization of long-term quarry production planning to supply raw materials for cement plants

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 _11