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Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO

1
Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi 100000, Vietnam
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Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi 100000, Vietnam
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Department of Energy and Mineral Resources, College of Engineering, Dong-A University, Busan 49315, Korea
4
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang Ward, Bac Tu Liem dist., Hanoi 100000, Vietnam
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Center for Excellence in Analysis and Experiment, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang Ward, Bac Tu Liem dist., Hanoi 100000, Vietnam
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Department of Mine Surveying, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi 100000, Vietnam
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Faculty of Environmental and Occupational Health, Hanoi University of Public Health, 1A Duc Thang Road, Duc Thang Ward, North Tu Liem District, Hanoi 100000, Vietnam
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Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
10
Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(14), 2806; https://doi.org/10.3390/app9142806
Received: 7 June 2019 / Revised: 4 July 2019 / Accepted: 5 July 2019 / Published: 12 July 2019
(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)
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Abstract

Dust is one of the components causing heavy environmental pollution in open-pit mines, especially PM10. Some pathologies related to the lung, respiratory system, and occupational diseases have been identified due to the effects of PM10 in open-pit mines. Therefore, the prediction and control of PM10 concentration in the production process are necessary for environmental and health protection. In this study, PM10 concentration from drilling operations in the Coc Sau open-pit coal mine (Vietnam) was investigated and considered through a database including 245 datasets collected. A novel hybrid artificial intelligence model was developed based on support vector regression (SVR) and a swarm optimization algorithm (i.e., particle swarm optimization (PSO)), namely PSO-SVR, for estimating PM10 concentration from drilling operations at the mine. Polynomial (P), radial basis function (RBF), and linear (L) kernel functions were considered and applied to the development of the PSO-SVR models in the present study, abbreviated as PSO-SVR-P, PSO-SVR-RBF, and PSO-SVR-L. Also, three benchmark artificial intelligence techniques, such as k-nearest neighbors (KNN), random forest (RF), and classification and regression trees (CART), were applied and developed for estimating PM10 concentration and then compared with the PSO-SVR models. Root-mean-squared error (RMSE) and determination coefficient (R2) were used as the statistical criteria for evaluating the performance of the developed models. The results exhibited that the PSO algorithm had an essential role in the optimization of the hyper-parameters of the SVR models. The PSO-SVR models (i.e., PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF) had higher performance levels than the other models (i.e., RF, CART, and KNN) with an RMSE of 0.040, 0.042, and 0.043; and R2 of 0.954, 0.948, and 0.946; for the PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF models, respectively. Of these PSO-SVR models, the PSO-SVR-L model was the most dominant model with an RMSE of 0.040 and R2 of 0.954. The remaining three benchmark models (i.e., RF, CART, and KNN) yielded a more unsatisfactory performance with an RMSE of 0.060, 0.052, and 0.067; and R2 of 0.894, 0.924, and 0.867, for the RF, CART, and KNN models, respectively. Furthermore, the findings of this study demonstrated that the density of rock mass, moisture content, and the penetration rate of the drill were essential parameters on the PM10 concentration caused by drilling operations in open-pit mines. View Full-Text
Keywords: meta-heuristic algorithm; PM10 concentration; drilling operation; artificial intelligence; open-pit coal mine meta-heuristic algorithm; PM10 concentration; drilling operation; artificial intelligence; open-pit coal mine
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Bui, X.-N.; Lee, C.W.; Nguyen, H.; Bui, H.-B.; Long, N.Q.; Le, Q.-T.; Nguyen, V.-D.; Nguyen, N.-B.; Moayedi, H. Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO. Appl. Sci. 2019, 9, 2806.

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