The purpose of this research was to develop a new approach in determination of overhaul and maintenance cost of loading equipment in surface mining. Two statistical models including univariate exponential regression (...The purpose of this research was to develop a new approach in determination of overhaul and maintenance cost of loading equipment in surface mining. Two statistical models including univariate exponential regression (UER) and multivariate linear regression (MLR) were used in this study. Loading equipment parameters such as bucket capacity, machine weight, engine power, boom length, digging depth, and dumping height were considered as variables. The results obtained by models and mean absolute error rate indicate that these models can be applied as the useful tool in determination of overhaul and maintenance cost of loading equipment. The results of this study can be used by the decision-makers for the specific surface mining operations.展开更多
Correlation analysis revealed that winter precipitation in six regions of eastern China is closely related not only to preceding climate signals but also to synchronous atmospheric general circulation fields. It is th...Correlation analysis revealed that winter precipitation in six regions of eastern China is closely related not only to preceding climate signals but also to synchronous atmospheric general circulation fields. It is therefore necessary to use a method that combines both dynamical and statistical predictions of winter precipitation over eastern China （hereinafter called the hybrid approach）, in this connection, seasonal real-time prediction models for winter precipitation were established for the six regions. The models use both the preceding observations and synchronous numerical predictions through a multivariate linear regression analysis. To improve the prediction accuracy, the systematic error between the original regression model result and the corresponding observation was corrected. Cross-validation analysis and real-time prediction experiments indicate that the prediction models using the hybrid approach can reliably predict the trend, sign, and interannual variation of regionally averaged winter precipitation in the six regions of concern. Averaged over the six target regions, the anomaly correlation coefficient and the rate with the same sign of anomaly between the cross-validation analysis and observation during 1982-2008 are 0.69 and 78%, respectively. This indicates that the hybrid prediction approach adopted in this study is applicable in operational practice.展开更多
文摘The purpose of this research was to develop a new approach in determination of overhaul and maintenance cost of loading equipment in surface mining. Two statistical models including univariate exponential regression (UER) and multivariate linear regression (MLR) were used in this study. Loading equipment parameters such as bucket capacity, machine weight, engine power, boom length, digging depth, and dumping height were considered as variables. The results obtained by models and mean absolute error rate indicate that these models can be applied as the useful tool in determination of overhaul and maintenance cost of loading equipment. The results of this study can be used by the decision-makers for the specific surface mining operations.
基金Supported by the Knowledge Innovation Project of the Chinese Academy of Sciences(KZCX2-YW-Q03-3)National Basic Research Program of China(2009CB421406)+1 种基金Special Public Welfare Research Fund for Meteorological Profession of China Mete-orological Administration(GYHY200906018)National Natural Science Foundation of China(40875048)
文摘Correlation analysis revealed that winter precipitation in six regions of eastern China is closely related not only to preceding climate signals but also to synchronous atmospheric general circulation fields. It is therefore necessary to use a method that combines both dynamical and statistical predictions of winter precipitation over eastern China （hereinafter called the hybrid approach）, in this connection, seasonal real-time prediction models for winter precipitation were established for the six regions. The models use both the preceding observations and synchronous numerical predictions through a multivariate linear regression analysis. To improve the prediction accuracy, the systematic error between the original regression model result and the corresponding observation was corrected. Cross-validation analysis and real-time prediction experiments indicate that the prediction models using the hybrid approach can reliably predict the trend, sign, and interannual variation of regionally averaged winter precipitation in the six regions of concern. Averaged over the six target regions, the anomaly correlation coefficient and the rate with the same sign of anomaly between the cross-validation analysis and observation during 1982-2008 are 0.69 and 78%, respectively. This indicates that the hybrid prediction approach adopted in this study is applicable in operational practice.
