Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challe...Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.展开更多
In recent years, with the increase of the depth of open-pit mining, the pollution level has been on the rise due to harmful gases and dust occurring in the process of mining. In order to accelerate the diffusion of th...In recent years, with the increase of the depth of open-pit mining, the pollution level has been on the rise due to harmful gases and dust occurring in the process of mining. In order to accelerate the diffusion of these air pollutants, the distributed regularity of the rock face temperature which is directly related to the air ventilation in deep open-pit mines should be studied. Here, we establish the key factors influencing the rock face temperature in a deep open-pit mine. We also present an empirical model of the rock face temperature variation in the deep open-pit mine, of which the performance is interestingly high compared with that of the field test. This study lays a foundation to study the ventilation thermodynamic theory in the deep open-pit mine, which is of great importance for theoretical studies and engineering applications of solving air pollution problem in deep open-pit mines.展开更多
基金This work was supported by the Pilot Seed Grant(Grant No.RES0049944)the Collaborative Research Project(Grant No.RES0043251)from the University of Alberta.
文摘Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.
基金Project(51274023) supported by the National Natural Science Foundation of ChinaProject(FRF-BD-17-007A) supported by Fundamental Research Funds for the Central Universities,China
文摘In recent years, with the increase of the depth of open-pit mining, the pollution level has been on the rise due to harmful gases and dust occurring in the process of mining. In order to accelerate the diffusion of these air pollutants, the distributed regularity of the rock face temperature which is directly related to the air ventilation in deep open-pit mines should be studied. Here, we establish the key factors influencing the rock face temperature in a deep open-pit mine. We also present an empirical model of the rock face temperature variation in the deep open-pit mine, of which the performance is interestingly high compared with that of the field test. This study lays a foundation to study the ventilation thermodynamic theory in the deep open-pit mine, which is of great importance for theoretical studies and engineering applications of solving air pollution problem in deep open-pit mines.
