The ultimate pit may affect other aspects in the life of a mine such as economical, technical, environmental, and social aspects. What makes it even more complex is that most often there are many pits which are econom...The ultimate pit may affect other aspects in the life of a mine such as economical, technical, environmental, and social aspects. What makes it even more complex is that most often there are many pits which are economically minable. This calls for a heuristic approach to determine which of these pits is the ultimate pit. This study presents a means of selecting an ultimate pit during design operations of the Hebei Limestone mine. During optimization processes of the mine, many pit shells were created using Whittle Software. Normally, Whittle Software optimizes these processes and assigns a revenue factor of 1 for the ultimate pit. Unfortunately, the pit shells created did not satisfy the criteria with a revenue factor of 1 based on the parameters. As a result of this, statistical analysis was implemented to further understand the relationship, variability, and correlation of the pit shells created (data). Correlation Analysis, K-means++ Analysis, Principal Component Analysis, and Generalized Linear models were used in the analysis of the pit shells created. The results portray a salient relationship of the optimization variables. In addition, the proposed method was tested on Whittle Sample projects which satisfy the selection of ultimate pit selection with a revenue factor of 1. The results show that the proposed model produced almost the same results as the Whittle model with a revenue factor of 1 and was also able to determine the ultimate pit in cases which did not satisfy the Whittle selection criteria.展开更多
文摘The ultimate pit may affect other aspects in the life of a mine such as economical, technical, environmental, and social aspects. What makes it even more complex is that most often there are many pits which are economically minable. This calls for a heuristic approach to determine which of these pits is the ultimate pit. This study presents a means of selecting an ultimate pit during design operations of the Hebei Limestone mine. During optimization processes of the mine, many pit shells were created using Whittle Software. Normally, Whittle Software optimizes these processes and assigns a revenue factor of 1 for the ultimate pit. Unfortunately, the pit shells created did not satisfy the criteria with a revenue factor of 1 based on the parameters. As a result of this, statistical analysis was implemented to further understand the relationship, variability, and correlation of the pit shells created (data). Correlation Analysis, K-means++ Analysis, Principal Component Analysis, and Generalized Linear models were used in the analysis of the pit shells created. The results portray a salient relationship of the optimization variables. In addition, the proposed method was tested on Whittle Sample projects which satisfy the selection of ultimate pit selection with a revenue factor of 1. The results show that the proposed model produced almost the same results as the Whittle model with a revenue factor of 1 and was also able to determine the ultimate pit in cases which did not satisfy the Whittle selection criteria.
