Influence of rock properties on emission rate of Particulates Matter (Pm) during drilling operation in surface mines
محورهای موضوعی :
Mineralogy
Koneti Nagesha
1
,
Harinandan Kumar
2
,
Muralidhar Munisingh
3
1 - Madanapalle Institute of Technology and Science, Madanapalle, India
2 - Department of Petroleum and Earth Sciences, University of Petroleum and Energy Studies, Bidholi, Via Prem Nagar,
Dehradun, Uttarakhand 248007, India
3 - Madanapalle Institute of Technology and Science, Madanapalle, India
تاریخ دریافت : 1398/09/10
تاریخ پذیرش : 1399/05/17
تاریخ انتشار : 1399/10/12
کلید واژه:
dust,
Multiple Regression Analysis,
Rock Properties,
ANN,
emission,
چکیده مقاله :
The mining process generates significant amount of dust in the form of particulate matters into the atmosphere. Out of different mining process, opencast mining produces more dust than that of underground mining because of exposure in the ambiance. The mining operations are directly or indirectly involved in the production of dust particles. The activities like drilling operation, Blasting and haul road operations produce fugitive dust and causes significant deterioration of mine atmosphere. This fugitive dust consists of particulate matters (PM), which are more harmful to the human respiratory system. The prevention measures is only possible when the actual prediction of emission of those fugitive dust particles are possible. There is several model that predict the emission of the dust particles, but there is very less model to predict fugitive dust produced from a drilling operation in surface mines. In this paper, study was carried out to develop dust prediction model and to assess the influence of rock properties on dust emission. Based on the results obtained the developed model exhibit close proximity of predicted as well as field measured values with a regression coefficient of 0.75. Thus, the development of the model with effective prediction capability is the novelty of this paper. Decrease in dust emission rate was observed with increased moisture content present in drill cuttings, higher compressive strength, and density.
منابع و مأخذ:
Abdul-Wahab SA, Worthing MA, Al-Maamari S (2005) Mineralogy of atmospheric suspended dust in three indoor and one outdoor location in Oman. Environmental Monitoring and Assessment 107(1):313-27.
Chakraborty MK, Ahmad M, Singh RS, Pal D, Bandopadhyay C, Chaulya SK (2001) Determination of the emission rate from various opencast mining operations, Environmental Modelling and Software 17:467–480.
Chaloulakou A, Grivas G, Spyrellis N (2003a) Neural network and multiple regression models for PM10 prediction in Athens: A comparative assessment, Journal of Air and Waste Management 53: 1183-1190.
Cole CF, Kerch RL (1990) Air quality management. In: Surface mining Kennedy Society for Mining, Metallurgy and Exploration Inc., Littleton, Colorado 841-859.
Cole CF, Zapert JG (1995) Air quality dispersion model validation at three stone quarries, National Stone Association 14884.
Collett RS, Oduyemi K (1997) Air quality modelling: a technical review of mathematical approaches. Meteorological Applications: A journal of forecasting, practical applications, training techniques and modelling 4(3):235-246.
Dust control (1998) Best practice environmental management in mining, Environment Australia. BPEMD.
Jose I, Huertas JI (2102) Air quality impact assessment of multiple open pit coal minesin northern Colombia, Journal of Environmental Management 93: 121-129.
Lal B, Tripathy SS (2012) Prediction of dust concentration in open cast coal mine using artificial neural network, Atmospheric Pollution Research 3:211-218.
Nagesha VR, Chandar KR (2015) Prediction of dust dispersion during drilling operation in open cast coal mines: A multi regression model, International journal of Environmental Sciences 6: 591-606.
Pandey B, Agrawal M, Singh S (2014) Assessment of air pollution around coal mining area: Emphasizing on spatial distributions, seasonal variations and heavy metals, using cluster and principal component analysis, Atmospheric Pollution Research 5:79-86.
Papanastasiou MD, Kioutsioukis (2007) Development and Assessment of Neural Network and Multiple Regression Models in Order to Predict PM10 Levels in a Medium-sized Mediterranean City, Water Air Soil Pollut 182:325–334.
Roy AR, Jha AK (2011) Development of multiple regression and Neural Network Models for assessment of blasting dust at a large surface coal mine, Journal of Environmental Science and Technology 4: 284-301.
Sousa SI, Martins FG, Pereira MC, Alvim-Ferraz MC, Ribeiro H, Oliveira M, Abreu I (2008) Influence of atmospheric ozone, PM10 and meteorological factors on the concentration of airborne pollen and fungal spores. Atmospheric Environment 42(32):7452-7464.
Thompson and Visser (2007) Selection, performance and economic evaluation of dust palliatives on surface mine haul roads, Journal of the Southern African Institute of Mining and Metallurgy 107:435-449.
Trivedi R, Chakraborty MK, Tewary BK (2009) Dust dispersion modeling using fugitive dust model at an opencast coal project of Western Coalfields Limited, India. Journal of Scientific and Industrial Research 68: 71-78.