Wyniki wyszukiwania - BazTech

1

Strategy in Dispatching Trucks and Shovels with Different Capacity to Increase the Operating Efficiency in Cao Son Surface Coal Mine, Vietnam

Pham Van Hoa, Tran Trung Chuyen, Le Hong Anh, Le Thi Thu Hoa, Pham Van Viet, Nguyen Anh Tuan, Le Thi Huong Giang

Inżynieria Mineralna

|

2021

|

no. 2

487--494

EN

In surface mining operations, the operating costs of truck-shovel system constitutes 50-60% of the total. Only a little save in the operation costs in this system will bring large profit for the mines. Due to many investment periods, the capacity of both trucks and shovels in Cao Son surface coal mine is different. This leads to the low efficiency and the difficulty in dispatching strategy for the mine. This paper presents the current situation and selection of advanced dispatching strategy for increasing the efficiency trucks and shovels at this surface coal mine. The results show the detailed match factor reflects the state of each team of loader and trucks and should be use as the indicator for dispatching decision for the heterogeneous truck and shovel fleet at Cao Son surface coal mine.

2

A comparative study of empirical and ensemble machine learning algorithms in predicting air over pressure in open pit coal mine

Nguyen Hoang, Bui Xuan-Nam, Tran Quang-Hieu, Pham Van Hoa, Nguyen Dinh-An, Le Thi Thu Hoa, Le Qui-Thao, Do Ngoc-Hoan, Bao Tran Dinh, Bui Hoang-Bac, Moayedi Hossein

Acta Geophysica

|

2020

|

Vol. 68, no. 2

325--336

EN

This study aims to take into account the feasibility of three ensemble machine learning algorithms for predicting blast-induced air over-pressure (AOp) in open-pit mine, including gradient boosting machine (GBM), random forest (RF), and Cubist. An empirical technique was also applied to predict AOp and compared with those of the ensemble models. To employ this study, 146 events of blast were investigated with 80% of the total database (approximately 118 blasting events) being used for developing the models, whereas the rest (20%~28 blasts) were used to validate the models’ accuracy. RMSE, MAE, and R2 were used as performance indices for evaluating the reliability of the models. The fndings revealed that the ensemble models yielded more precise accuracy than those of the empirical model. Of the ensemble models, the Cubist model provided better performance than those of RF and GBM models with RMSE, MAE, and R2 of 2.483, 0.976, and 0.956, respectively, whereas the RF and GBM models provided poorer accuracy with an RMSE of 2.579, 2.721; R2 of 0.953, 0.950, and MAE of 1.103, 1.498, respectively. In contrast, the empirical model was interpreted as the poorest model with an RMSE of 4.448, R2 of 0.872, and MAE of 3.719. In addition, other fndings indicated that explosive charge capacity, spacing, stemming, monitoring distance, and air humidity were the most important inputs for the AOp predictive models using artifcial intelligence.

3

Estimation of truck-shovel dispatching in Cao Son Open-Pit coal mine and the ability in applying information technology for increasing its efficiency

Pham Van Hoa, Bui Xuan‑Nam, Le Van Quyen, Le Thi Thu Hoa, Pham Van Viet

Inżynieria Mineralna

|

2019

|

R. 21, nr 2/2

21--27

EN

With many types of trucks and shovels for hauling large volume of waste rocks to the dump sites and coal to the storages, the truck – shovel dispatching in Cao Son open pit coal mine is the operation which needs to be improved. At present, the combination between trucks and shovel is usually assigned at the beginning of shift and adjusted during the operation at the mine. The GPS tracking system are integrated into each truck to monitor the position in real time, but applying this information to find the best destination to send the truck to satisfy the production requirements and to minimize truck operating costs is still not used. This paper presents the estimation of the information system, data, the remaining problems of truck – shovel dispatching system, from that proposes the application of available information technology for increasing the efficiency of this activities at the mine.

4

A Lasso and Elastic-Net Regularized Generalized Linear Model for Predicting Blast-Induced Air Over-pressure in Open-Pit Mines

Bui Xuan‑Nam, Nguyen Hoang, Tran Quang Hieu, Bui Hoang‑Bac, Nguyen Quoc Long, Nguyen Dinh An, Le Thi Thu Hoa, Pham Van Viet

Inżynieria Mineralna

|

2019

|

R. 21, nr 2/2

8--20

EN

Air overpressure (AOp) is one of the products of blasting operations in open-pit mines which have a great impact on the environment and public health. It can be dangerous for the lungs, brain, hearing and the other human senses. In addition, the impact on the surrounding environment such as the vibration of buildings, break the glass door systems are also dangerous agents caused by AOp. Therefore, it should be properly controlled and forecasted to minimize the impacts on the environment and public health. In this paper, a Lasso and Elastic-Net Regularized Generalized Linear Model (GLMNET) was developed for predicting blast-induced AOp. The United States Bureau of Mines (USBM) empirical technique was also applied to estimate blast-induced AOp and compare with the developed GLMNET model. Nui Beo open-pit coal mine, Vietnam was selected as a case study. The performance indices are used to evaluate the performance of the models, including Root Mean Square Error (RMSE), Determination Coefficient (R2), and Mean Absolute Error (MAE). For this aim, 108 blasting events were investigated with the Maximum of explosive charge capacity, monitoring distance, powder factor, burden, and the length of stemming were considered as input variables for predicting AOp. As a result, a robust GLMNET model was found for predicting blast-induced AOp with an RMSE of 1.663, R2 of 0.975, and MAE of 1.413 on testing datasets. Whereas, the USBM empirical method only reached an RMSE of 2.982, R2 of 0.838, and MAE of 2.162 on testing datasets.