Geology, coal resources, and coal quality of the Prairie Dog Creek EMRIA Study Area, Rosebud County, Montana

1981 ◽  
Author(s):  
Laura N. Robinson ◽  
W.C. Culbertson ◽  
Ronald H. Affolter
Keyword(s):  
Prairie Dog ◽  
Coal Quality ◽  
Coal Resources

scholarly journals The Analysis of Influence of the Gangue in the Coal as Fired on the Coal Conveying System and the Boiler Operation of the Circulating Fluidized Bed Unit

2019 ◽  
Vol 118 ◽  
pp. 02011
Author(s):  
Su Pan ◽  
Yu Pengfeng ◽  
Linbo Liu ◽  
Han Jing ◽  
Xiao Shen
Keyword(s):  
Fluidized Bed ◽  
Circulating Fluidized Bed ◽  
Storage Conditions ◽  
Coal Gangue ◽  
Quality Data ◽  
Stable Operation ◽  
Coal Quality ◽  
Boiler Operation ◽  
Conveying System

The coal as fired, with unidentified characteristics of the coal gangue, was burned on a 300MW circulating fluidized bed unit. The equipment of the coal conveying system was damaged and the boiler operation was unstable. In response to the problems, the coal quality data and storage conditions of the coal were examined and the site was spot-checked to evaluate the coal quality characteristics. At the same time, the typical representative parameters of the coal handling system and boiler operation were selected. According to the analysis of coal quality and coal storage, the coal quality fluctuates greatly and the uniformity of particle size distribution is poor. There is actually the coal gangue with hard texture and hard to grind in the coal pile. The coal gangue will have adverse effects on the fine screening machine, fine crusher and other equipment. After burned this type of coal, the fluidized quality of the boiler bed is degraded to make an impact on the safe and stable operation of the boiler. It is recommended that the coal should be screened and then burned into the furnace to ensure safe and stable operation of the boiler.

scholarly journals DETERMINATION OF ASH CONTENT IN COAL BY X-RAY FLUORESCENCE ANALYSIS Authors D Baimolda

2021 ◽  
Vol 73 (1) ◽  
pp. 82-84
Author(s):  
D. Baimolda ◽  
◽  
T. Cechak ◽  
Sh. Shyngysova ◽  
◽  
...  
Keyword(s):  
Nuclear Physics ◽  
Coal Ash ◽  
Fluorescence Analysis ◽  
Ash Content ◽  
Environmental Damage ◽  
Coal Quality ◽  
X Ray ◽  
Burning Coal

This article discusses the advantages of X-ray fluorescence analysis (XRF) techniques for the determination of ash in coal. The quality of coal depends on the amount of ash contained in it. On the other hand, ash causes irreversible environmental damage when using coal as a source of energy. Since coal is considered as the most important source of energy, coal quality is directly related to ash, which correlates with its non-combustible minerals and elements. Some elements such as S (sulfur), Ti (titanium), Ca (calcium), Fe (iron) after burning coal can have an adverse impact on the environment. Thus, we have demonstrated in this study how we can determine the ash content consisting of noncombustible minerals in the composition of coal and, thus, assess the quality of coal using X-ray fluorescence research. It also describes how we can determine coal ash samples using the XRF analyzer 123-1 in online, which is one of the most optimal methods in nuclear physics.

scholarly journals Coal quality prediction based on multi-feature fusion of flotation foam images

2020 ◽  
Author(s):  
yateng bai ◽  
xiaoping ma
Keyword(s):  
Real Time ◽  
Feature Fusion ◽  
Image Features ◽  
Image Feature ◽  
Coal Preparation ◽  
Coal Quality ◽  
Coal Flotation ◽  
Edge Operator ◽  
Occurrence Matrix

Abstract Coal flotation monitoring cannot provide real-time feedback on the yield and ash of coal preparation products because it is influenced by the subjective nature of artificial judgment of coal preparation status and the lag of product quality testing of coal preparation. This paper aims to extract the texture, colour and shape features of floating foam images using various image processing methods, such as colour space, wavelet transform, greyscale co-occurrence matrix and edge operator, and to quantify the characterisation of various characteristic parameters on the basis of the indicative effect of floating foam characteristics on the quality of coal preparation products. The correlation between image features and the yield and ash of flotation products is studied, and a regression prediction model of coal preparation yield and ash was established by combining various image feature parameters using machine learning methods. Experimental results show that the proposed method can realise the real-time monitoring of coal mine flotation and effectively predict coal quality.

scholarly journals Black-Footed Ferret (Mustela nigripes) Restoration in Badlands National Park 1994-1995

1994 ◽  
Vol 18 ◽  
pp. 29-30
Author(s):  
Glenn Plumb
Keyword(s):  
National Park ◽  
Prairie Dog ◽  
Mustela Nigripes ◽  
Wilderness Area ◽  
Badlands National Park

During spring 1994, three black-tailed prairie dog colonies totaling 403 ha within Badlands National Park were selected as locales to release ferrets during fall. This area was selected based on quality of habitat, remoteness from visitors and accessibility for field crews and is located within the Badlands Wilderness Area where mechanical transport is prohibited and approximately 550 bison range freely. Forty proposed release cages were stratified across the 3 colonies based on topography and degree of prairie dog activity. A helicopter was used to airlift over 8,000 lbs of supplies used for construction of 28 release cages/bison exclosures in the Badlands Wilderness Area several months prior to black-footed ferret arrival.

Coal Quality of Assessed Areas of Texas and Louisiana, Gulf Coastal Plain

2011 ◽  
Author(s):  
Brenda S. Pierce ◽  
Jason C. Willett ◽  
Sharon M. Swanson ◽  
Kristin O. Dennen
Keyword(s):  
Coastal Plain ◽  
Gulf Coastal Plain ◽  
Coal Quality

Coal geology and coal quality of the Miocene Mugla basin, southwestern Anatolia, Turkey

1999 ◽  
Vol 41 (4) ◽  
pp. 311-332 ◽  
Author(s):  
X Querol ◽  
A Alastuey ◽  
F Plana ◽  
A Lopez-Soler ◽  
E Tuncali ◽  
...  
Keyword(s):  
Coal Quality
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