The success of a cement production project depends on the supply of raw materials. Long-term quarry production scheduling (LTQPS) based on resource models is essential to maintain a consistent supply to cement plants. Geological uncertainty is inherent due to sparse exploration data in resource models and significant risk factors for not achieving production targets. This research proposes a stochastic framework for LTQPS that considers the impact of geological uncertainty on raw material supply. A clustering algorithm uses multiple simulated deposit models to aggregate blocks into mining cuts. A new stochastic mixedinteger programming model is formulated with two objectives: to minimise the cost for developing the raw mix and the risk of not meeting production targets. The proposed framework is implemented successfully in a limestone deposit in Southern Vietnam, resulting in an increase of 5 million tons (Mt) and a 30% reduction in unit cost over the deterministic mixed-integer programming model.
Blast fragmentation size distribution is one of the most critical factors in evaluating the blasting results and affecting the downstream mining and processing operations in open-pit mines. Image-based methods are widely applied to address the problem but require heavy user interaction and experience. This study deployed a deep learning model Mask R-CNN to develop an automatic measurement method of blast fragmentation. The model was trained using images captured from real blasting sites in Nui Phao open-pit mine in Vietnam. The trained model reported high average precision scores (Intersection over Union, IoU = 0.5) 92% and 83% for bounding box and segmentation masks, respectively. The results lay a solid technical basis for the automated measurement of blast fragmentation in open-pit mines.
Long-term limestone quarry production planning is essential to maintain the supply to the cement plant. In which, quarry planners usually attempt to fulfil the complicated calculations, which ensure a consistent supply of raw materials to the cement plant while guaranteeing technical and operational parameters in mining. Traditionally, the calculations are done on a spreadsheet or by trial and error procedure resulting in high additive cost and an increase in product variability. Modern quarry management relies on block models and mathematical algorithms integrated into the software to optimize the long-term limestone quarry production planning. However, this method is potentially sensitive to geological uncertainty in block modelling, resulting in the deviation of the supply production of raw materials. The need for mining intelligently raw material is, therefore, crucial and an increasing issue in the cement industry. In this research, a new simulation and optimization software application called Quarrier is introduced, allowing quarry planners to address the conflicting requirements of long-term limestone quarry production planning while forecasting and mitigating the effects of geological uncertainty on the supply of raw materials for the cement plant. The benefits of this software are demonstrated through a limestone quarry in Vietnam.
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