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    Wyświetlanie 1-3 z 3
    Tytuł:
    Developing an Advanced Soft Computational Model for Estimating Blast-Induced Ground Vibration in Nui Beo Open-pit Coal Mine (Vietnam) Using Artificial Neural Network
    Opracowanie zaawansowanego modelu obliczeniowego do szacowania wibracji gruntu wywołanych wybuchem w odkrywkowej kopalni węgla Nui Beo (Wietnam) przy użyciu sztucznej sieci neuronowej
    Autorzy:
    Nguyen, Hoang
    Bui, Xuan‑Nam
    Tran, Quang Hieu
    Nguyen, Quoc Long
    Vu, Dinh Hieu
    Pham, Van Hoa
    Le, Qui Thao
    Nguyen, Phu Vu
    Powiązania:
    https://bibliotekanauki.pl/articles/317864.pdf
    Data publikacji:
    2019
    Wydawca:
    Polskie Towarzystwo Przeróbki Kopalin
    Tematy:
    Wietnam
    górnictwo odkrywkowe
    sieci neuronowe
    Vietnam
    open pit mining
    artificial neural network
    Opis:
    The principal object of this study is blast-induced ground vibration (PPV), which is one of the dangerous side effects of blasting operations in an open-pit mine. In this study, nine artificial neural networks (ANN) models were developed to predict blast-induced PPV in Nui Beo open-pit coal mine, Vietnam. Multiple linear regression and the United States Bureau of Mines (USBM) empirical techniques are also conducted to compare with nine developed ANN models. 136 blasting operations were recorded in many years used for this study with 85% of the whole datasets (116 blasting events) was used for training and the rest 15% of the datasets (20 blasting events) for testing. Root Mean Square Error (RMSE), Determination Coefficient (R2), and Mean Absolute Error (MAE) are used to compare and evaluate the performance of the models. The results revealed that ANN technique is more superior to other techniques for estimating blast-induced PPV. Of the nine developed ANN models, the ANN 7-10-8-5-1 model with three hidden layers (ten neurons in the first hidden layer, eight neurons in the second layers, and five neurons in the third hidden layer) provides the most outstanding performance with an RMSE of 1.061, R2 of 0.980, and MAE of 0.717 on testing datasets. Based on the obtained results, ANN technique should be applied in preliminary engineering for estimating blast-induced PPV in open-pit mine.
    Źródło:
    Inżynieria Mineralna; 2019, 21, 2/2; 58-73
    1640-4920
    Pojawia się w:
    Inżynieria Mineralna
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Lasso and Elastic-Net Regularized Generalized Linear Model for Predicting Blast-Induced Air Over-pressure in Open-Pit Mines
    Model Lasso i uogólniony model liniowy elastycznej siatki do prognozowania nadciśnienia wywołanego wybuchem w kopalniach odkrywkowych
    Autorzy:
    Bui, Xuan‑Nam
    Nguyen, Hoang
    Tran, Quang Hieu
    Bui, Hoang‑Bac
    Nguyen, Quoc Long
    Nguyen, Dinh An
    Le, Thi Thu Hoa
    Pham, Van Viet
    Powiązania:
    https://bibliotekanauki.pl/articles/318532.pdf
    Data publikacji:
    2019
    Wydawca:
    Polskie Towarzystwo Przeróbki Kopalin
    Tematy:
    Lasso model
    kopalnia odkrywkowa
    wybuchy
    open pit mines
    explosives
    Opis:
    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.
    Źródło:
    Inżynieria Mineralna; 2019, 21, 2/2; 8-20
    1640-4920
    Pojawia się w:
    Inżynieria Mineralna
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Use of Unmanned Aerial Vehicles for 3D topographic Mapping and Monitoring the Air Quality of Open-pit Mines
    Wykorzystanie bezzałogowych statków powietrznych (dronów) do monitorowania jakości powietrza w odkrywkowych kopalniach węgla kamiennego
    Autorzy:
    Bui, Xuan‑Nam
    Lee, Changwoo
    Nguyen, Quoc Long
    Adeel, Ahmad
    Cao, Xuan Cuong
    Nguyen, Viet Nghia
    Le, Van Canh
    Nguyen, Hoang
    Le, Qui Thao
    Duong, Thuy Huong
    Nguyen, Van Duc
    Powiązania:
    https://bibliotekanauki.pl/articles/317913.pdf
    Data publikacji:
    2019
    Wydawca:
    Polskie Towarzystwo Przeróbki Kopalin
    Tematy:
    drony
    jakość powietrza
    kopalnie odkrywkowe
    unmanned aerial vehicles
    air quality
    open pit mines
    Opis:
    Recently remarkable advancement development of unmanned aerial vehicles (UAVs) has been observed and their applications have been shown in many fields such as agriculture, industry, and environmental management. However, in the mining industry, the application of UAV technology remains potential. This paper presents a low-cost unmanned aerial vehicle technology-based system for 3D mapping and air quality monitoring at open-pit mine sites in Vietnam. The system includes several dust sensors that are mounted on a low-cost rotary-wing type UAV. The system collects a variety of data, mainly images and airborne pollutant concentrations. To evaluate the performance of the proposed system, field tests were carried out at the Coc Sau coal mine. Based on the images transmitted to the ground monitoring station, large scale 3D topographic maps were successfully modeled. In addition, sensors mounted on the UAV system were able to monitor the levels of environmental variables associated with the air quality within the pit such as temperature, dust, CO, CO2, and NOx. The field test results in this study illustrate the applicability of the low-cost UAV for the 3D mapping and the air quality monitoring at large and deep coal pits with relatively high accuracy.
    Źródło:
    Inżynieria Mineralna; 2019, 21, 2/2; 223-239
    1640-4920
    Pojawia się w:
    Inżynieria Mineralna
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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