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Wyświetlanie 1-2 z 2
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
    Wyświetlanie 1-2 z 2

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