Prediction and Governance of Mine Gas Emission

2013 ◽  
Vol 419 ◽  
pp. 500-504
Author(s):  
Yi Wen Liu ◽  
Yi Cao ◽  
Lin Zhang ◽  
Ming Chuan Meng
Keyword(s):  
Neural Network ◽  
Coal Mining ◽  
Bp Neural Network ◽  
Emission Control ◽  
Control Measures ◽  
Gas Emission ◽  
Emission Data ◽  
Main Factors ◽  
Mine Gas ◽  
Emission Control Measures

Coal mining gas emission constrained by many factors, considering the eight main factors of gas emission. The first gas emission data are normalized, avoid data overflow to improve the training speed of neural network. Then use BP neural network to predict the amount of mine gas emission, finally proposed gas emission control measures.

Application of Gray Elman Neural Network to Predict the Gas Emission Amount

2013 ◽  
Vol 706-708 ◽  
pp. 1750-1754 ◽  
Author(s):  
Jing Gang Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Control Measures ◽  
Grey Theory ◽  
Grey Prediction ◽  
Gas Emission ◽  
Elman Neural Network ◽  
Artificial Neural ◽  
Mine Gas

The prediction of mine Gas Emission Amount is an important part of helping to make rational gas control measures. In order to improve the accuracy of mine gas emission prediction, this paper introduced the grey theory into the Elman artificial neural network theory, and combined the gray prediction model GM (1,1) with the Elman neural network model,established a gray Elman artificial neural network prediction model of gas emission, and carried on the simulation through software Matlab. Practice and experiment showed that this method compared well, and is superior to the traditional Grey prediction model, moreover this method also applied to the situation of original data was few or the historical data had transition. The forecasting results from this method can be more reliable and accurate, so it can instruct the practice accurately

scholarly journals Attributions of meteorological and emission factors to the 2015 winter severe haze pollution episodes in China's Jing-Jin-Ji area

2017 ◽  
Vol 17 (4) ◽  
pp. 2971-2980 ◽  
Author(s):  
Tingting Liu ◽  
Sunling Gong ◽  
Jianjun He ◽  
Meng Yu ◽  
Qifeng Wang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Emission Control ◽  
Northern China ◽  
Air Pollutant ◽  
Control Measures ◽  
Air Pollution Control ◽  
Emission Data ◽  
Haze Pollution ◽  
Stable Conditions ◽  
Emission Control Measures

Abstract. In the 2015 winter month of December, northern China witnessed the most severe air pollution phenomena since the 2013 winter haze events occurred. This triggered the first-ever red alert in the air pollution control history of Beijing, with an instantaneous fine particulate matter (PM2. 5) concentration over 1 mg m−3. Air quality observations reveal large temporal–spatial variations in PM2. 5 concentrations over the Beijing–Tianjin–Hebei (Jing-Jin-Ji) area between 2014 and 2015. Compared to 2014, the PM2. 5 concentrations over the area decreased significantly in all months except November and December of 2015, with an increase of 36 % in December. Analysis shows that the PM2. 5 concentrations are significantly correlated with the local meteorological parameters in the Jing-Jin-Ji area such as the stable conditions, relative humidity (RH), and wind field. A comparison of two month simulations (December 2014 and 2015) with the same emission data was performed to explore and quantify the meteorological impacts on the PM2. 5 over the Jing-Jin-Ji area. Observation and modeling results show that the worsening meteorological conditions are the main reasons behind this unusual increase of air pollutant concentrations and that the emission control measures taken during this period of time have contributed to mitigate the air pollution ( ∼  9 %) in the region. This work provides a scientific insight into the emission control measures vs. the meteorology impacts for the period.

Gas Emission Prediction Based on Coal Mine Operating Data

2014 ◽  
Vol 484-485 ◽  
pp. 604-607
Author(s):  
Da Chao Yuan ◽  
Xiao Guang Yue ◽  
Chen Wang ◽  
Jian Feng Zhang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Rbf Neural Network ◽  
Actual Data ◽  
Data Detection ◽  
Gas Emission ◽  
Emission Data

Currently, mine accidents are recurring disasters. Among them, the coal mine gas emission data detection is important information that needed to be focus on. At first, the actual data from coal mines are gotten as a part of training and testing data. Then, BP neural network model and RBF neural network model was constructed by using Matlab. Finally, the actual data is ready for the simulation; select 15 sets of data to predict the three sets of data. BP neural network is an effective mean for gas emission, and the performance of BP neural network prediction method is better than RBF neural network method.

Prediction of Coal and Gas Outburst by BP Neural Network

2014 ◽  
Vol 539 ◽  
pp. 664-668 ◽  
Author(s):  
Guang Zhang ◽  
Zhi Jun Liu ◽  
Yi Wen Yang ◽  
Chang Chuan Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Middle Layer ◽  
Coal And Gas Outburst ◽  
Initial Speed ◽  
Gas Emission ◽  
Gas Outburst ◽  
Mining Depth ◽  
Input Layer ◽  
Main Factors

Accidents of coal mine have happened frequently in china and coal and gas outburst is a common and serious disaster. Outbursts are often accompanied by the release of gas and lead to gas suffocation and explosions. In this paper, we use BP neural network to predict outbursts. We selected mining depth, gas pressure, initial speed of gas emission, firmness coefficient and geological extent as the main factors of outbursts. By analyzing and comparing we established a prediction model which has 5 neurons in input layer, 14 in middle layer, 1 in output layer and the model is established based on Matlab7.8.0. Experimental results show that simulated curves have the same trend with actual curve, so the method is feasible.

scholarly journals Air quality improvement in a megacity: implications from 2015 Beijing Parade Blue pollution control actions

2017 ◽  
Vol 17 (1) ◽  
pp. 31-46 ◽  
Author(s):  
Wen Xu ◽  
Wei Song ◽  
Yangyang Zhang ◽  
Xuejun Liu ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Air Quality ◽  
Control Period ◽  
Emission Control ◽  
Monitoring Network ◽  
Control Measures ◽  
Integrated Analysis ◽  
Air Quality Improvement ◽  
Emission Control Measures ◽  
Control Regions

Abstract. The implementation of strict emission control measures in Beijing and surrounding regions during the 2015 China Victory Day Parade provided a valuable opportunity to investigate related air quality improvements in a megacity. We measured NH3, NO2 and PM2.5 at multiple sites in and outside Beijing and summarized concentrations of PM2.5, PM10, NO2, SO2 and CO in 291 cities across China from a national urban air quality monitoring network between August and September 2015. Consistently significant reductions of 12–35 % for NH3 and 33–59 % for NO2 in different areas of Beijing during the emission control period (referred to as the Parade Blue period) were observed compared with measurements in the pre- and post-Parade Blue periods without emission controls. Average NH3 and NO2 concentrations at sites near traffic were strongly correlated and showed positive and significant responses to traffic reduction measures, suggesting that traffic is an important source of both NH3 and NOx in urban Beijing. Daily concentrations of PM2.5 and secondary inorganic aerosol (sulfate, ammonium and nitrate) at the urban and rural sites both decreased during the Parade Blue period. During (after) the emission control period, concentrations of PM2.5, PM10, NO2, SO2 and CO from the national city-monitoring network showed the largest decrease (increase) of 34–72 % (50–214 %) in Beijing, a smaller decrease (a moderate increase) of 1–32 % (16–44 %) in emission control regions outside Beijing and an increase (decrease) of 6–16 % (−2–7 %) in non-emission-control regions of China. Integrated analysis of modelling and monitoring results demonstrated that emission control measures made a major contribution to air quality improvement in Beijing compared with a minor contribution from favourable meteorological conditions during the Parade Blue period. These results show that controls of secondary aerosol precursors (NH3, SO2 and NOx) locally and regionally are key to curbing air pollution in Beijing and probably in other mega cities worldwide.

scholarly journals Enhanced hydrophobicity and volatility of submicron aerosols under severe emission control conditions in Beijing

2017 ◽  
Author(s):  
Yuying Wang ◽  
Fang Zhang ◽  
Zhanqing Li ◽  
Haobo Tan ◽  
Hanbing Xu ◽  
...  
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Control Period ◽  
Emission Control ◽  
Control Measures ◽  
Particle Sizes ◽  
Industrial Emissions ◽  
Aerosol Hygroscopicity ◽  
Emission Control Measures

Abstract. A series of strict emission control measures were implemented in Beijing and the surrounding seven provinces to ensure good air quality during the 2015 China Victory Day parade, rendering a unique opportunity to investigate anthropogenic impact of aerosol properties. Submicron aerosol hygroscopicity and volatility were measured during and after the control period using a hygroscopic and volatile tandem differential mobility analyzer (H/V-TDMA) system. Three periods, namely, the control clean period (Clean1), the non-control clean period (Clean2), and the non-control pollution period (Pollution), were selected to study the effect of the emission control measures on aerosol hygroscopicity and volatility. Aerosol particles became more hydrophobic and volatile due to the emission control measures. The hygroscopicity parameter (κ) of 40–200 nm particles decreased by 32.0 %–8.5 % during the Clean1 period relative to the Clean2 period, while the volatile shrink factor (SF) of 40–300 nm particles decreased by 7.5 %–10.5 %. The emission controls also changed the diurnal variation patterns of both the probability density function of κ (κ-PDF) and the probability density function of SF (SF-PDF). During Clean1 the κ-PDF showed one nearly-hydrophobic (NH) mode for particles in the nucleation mode, which was likely due to the dramatic reduction in industrial emissions of inorganic trace gases. Compared to the Pollution period, particles observed during the Clean1 and Clean2 periods exhibited a more significant non-volatile (NV) mode throughout the day, suggesting a more externally-mixed state particularly for the 150 nm particles. Aerosol hygroscopicities increased as particle sizes increased, with the greatest increases seen during the Pollution period. Accordingly, the aerosol volatility became weaker (i.e., SF increased) as particle sizes increased during the Clean1 and Clean2 periods, but no apparent trend was observed during the Pollution period. Based on a correlation analysis of the number fractions of NH and NV particles, we found that a higher number fraction of hydrophobic and volatile particles during the emission control period.

scholarly journals Air Quality Improvement in a Megacity: Implications from 2015 Beijing Parade Blue Pollution-Control Actions

2016 ◽  
Author(s):  
Wen Xu ◽  
Wei Song ◽  
Yangyang Zhang ◽  
Xuejun Liu ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Air Quality ◽  
Control Period ◽  
Emission Control ◽  
Monitoring Network ◽  
Control Measures ◽  
Integrated Analysis ◽  
Beijing City ◽  
Air Quality Improvement ◽  
Emission Control Measures

Abstract. The implementation of strict emission control measures in Beijing and surrounding regions during the 2015 China Victory Day Parade provided a valuable opportunity to investigate related air quality improvements in a megacity. We measured NH3, NO2 and PM2.5 at multiple sites in and outside Beijing and summarized concentrations of PM2.5, PM10, NO2, SO2 and CO in 291 cities across China from a national urban air quality monitoring network between August and September 2015. Consistently significant reductions of 12–35 % for NH3 and 33–59 % for NO2 in different areas of Beijing city during the emission control period (referred to as the Parade Blue period) were observed compared with measurements in the pre- and post-Parade Blue periods without emission controls. Average NH3 and NO2 concentrations at sites near traffic were strongly correlated and showed positive and significant responses to traffic reduction measures, suggesting that traffic is an important source of both NH3 and NOx in urban Beijing. Daily concentrations of PM2.5 and secondary inorganic aerosol (sulfate, ammonium, and nitrate) at the urban and rural sites both decreased during the Parade Blue period. Concentrations of PM2.5, PM10, NO2, SO2 and CO from the national city-monitoring network showed the largest decrease (34–72 %) in Beijing, a smaller decrease (1–32 %) in North China (excluding Beijing), and an increase (6–16 %) in other regions of China during the emission control period. Integrated analysis of modeling and monitoring results demonstrated that emission control measures made a major contribution to air quality improvement in Beijing compared with a minor contribution from favorable meteorological conditions during the Parade Blue period. These results show that controls of secondary aerosol precursors (NH3, SO2 and NOx) locally and regionally are key to curbing air pollution in Beijing and probably in other mega cities worldwide.

scholarly journals China’s emission control strategies have suppressed unfavorable influences of climate on wintertime PM2.5 concentrations in Beijing since 2002

2020 ◽  
Author(s):  
Meng Gao ◽  
Kaili Lin ◽  
Shiqing Zhang ◽  
Ken kin lam Yung
Keyword(s):  
Control Strategies ◽  
Emission Control ◽  
Field Measurements ◽  
Meteorological Conditions ◽  
Control Measures ◽  
Anthropogenic Emissions ◽  
Recent Climate ◽  
Decadal Variations ◽  
Haze Days ◽  
Emission Control Measures

<p>Severe wintertime PM2.5 pollution in Beijing has been receiving increasing worldwide attention, yet the decadal variations remain relatively unexplored. Combining field measurements and model simulations, we quantified the relative influences of anthropogenic emissions and meteorological conditions on PM2.5 concentrations in Beijing overwinters of 2002-2016. Between the winters of 2011 and 2016, stringent emission control measures resulted in a 21% decrease in mean mass concentrations of PM2.5 in Beijing, with 7 fewer haze days per winter on average. Given the overestimation of PM2.5 by model, the effectiveness of stringent emission control measures might have been slightly overstated. With fixed emissions, meteorological conditions over the study period would have led to an increase of haze in Beijing, but the strict emission control measures have suppressed the unfavorable influences of recent climate. The unfavorable meteorological conditions are attributed to the weakening of the East Asia Winter Monsoon associated particularly with an increase in pressure associated with the Aleutian low.</p>

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