Traditional coal washability analysis is determined by conducting sink-float test, which is destructive and uses environmentally hazardous chemicals. ... The machine can process about 800-1200 ...
PDF | On Jan 1, 2011, C. Igathinathane and others published Particle size distribution analysis of ground coal by machine vision σVolume approach | Find, read and cite all the research you need ...
For this analysis, a coal sample is combusted in an ultimate analyzer, which measures the weight percent of carbon, hydrogen, nitrogen, sulfur, and ash from a coal sample. ... Both machines are calibrated using standard reference samples of known carbon, hydrogen, nitrogen, sulfur and ash values. In the carbon-sulfur analyzer, 0.25 …
PGNAA or PFTNA coal analyser and software that measure sulfur, ash, moisture, calorific value to control coal quality and blend consistency in coal mining and production. Contact a coal analysis sales representative to discuss your …
These coal parameters are termed proximate coal analysis, according to ISO 17246 and ASTM D 3172. Though they refer to different standards like ISO 562, ASTM D 3175 or ASTM D 7582, which describe the determination of one analysis parameter (e. g. volatiles) in more detail, both standards (ASTM and ISO) define proximate coal and …
Study relationship between inorganic and organic coal analysis with gross calorific value by multiple regression and ANFIS. International Journal of Coal Preparation and Utilization, 31 (1 ... Failure mode and effects analysis of RC members based on machine-learning-based shapley additive explanations (SHAP) approach. Engineering …
Coal and gas outburst is an extremely complex mine dynamic disaster in coal mine production [1], which poses a great threat to the stable development of energy economy in China.In the past decades, coal and gas outburst accidents have occurred frequently, causing serious casualties and property losses (Fig. 1).The research and …
The results of their finding are very useful to predict and manage gas and coal outbursts . Although several studies have been conducted on the prediction of safety risk, very few researches have been conducted to apply machine learning to predict risk and risk classification involving the hazards occurring in underground coal mining.
Traditional methods of obtaining prediction data suffer from a range of problems such as too many input variables, low prediction accuracy of a single analysis method and lack of sensitivity analysis of input variables. In this paper, a novel hybrid analysis was presented to predict the CV of coal.
Hardgrove Grindability Tester measures the relative ease of pulverization of coal compared to standard coal samples in accordance with ASTM D409 Hardgrove Machine Method. The resulting Hardgrove Grindability Index (HGI) provides a measure of the energy required in a grinding process or a measure of yield for given energy input.
ABSTRACT Knowing the properties of coal, which is still the most widely used among primary energy sources, is critical for determining the application area and the technology to be applied. The ultimate analysis results contain important information for the estimation of the gas product composition to be released to the environment as a result …
ECA-3 Online Elemental Coal Analyzer Provides real-time quality analysis of critical process streams to facilitate sorting, blending and out-of-seam dilution control. …
PGNAA or PFTNA coal analyser and software that measure sulfur, ash, moisture, calorific value to control coal quality and blend consistency in coal mining and production. …
A coal proximate analysis method based on a combination of visible-infrared spectroscopy and deep neural networks.. This method can fate examines the moisture, ash, volatile matter, fixed carbon, sulphur and low heating value in coal.. Compared with traditional coal analysis, this method has unparalleled advantages and …
The resulting TBF and TTR can then be used to conduct component failure analysis. The essence of the component failure analysis is to identify the critical components of the system (Paraszczak ...
technique for coal analysis7–9 and demonstrates advantages such as effortless sample preparation, real-time detection, and capability to detect all elements. Gaft et al.10 first applied LIBS to online analysis of ash content and the results were in good agreement with those from an existing online PGNAA machine.
Download Citation | Coal Production Analysis using Machine Learning | Coal will keep on giving a significant segment of energy prerequisites in the US for at any rate the following quite a few years.
The support vector machine (SVM) algorithm was proved as the optimal machine learning model for predicting CWP, with the ROC curves obtained from three feature selection methods using SVM ...
An online machine vision system to predict the ash content of a fine cleaned-coal product on a moving conveyor belt was established and tested. Observing the changes of different coal images and their corresponding ash content, six statistical features from a gray histogram and one feature reflecting the light-spot ratio were …
Coal is one of the chief energy sources having significant applications in the iron and steel industry. This research investigates the screening efficiency of coal of different size range. The experiments on the screening of coal with different size range in the screening machine were carried out using different mesh sizes.
The direct gas content determination methods subdivide the total gas content of a coal sample into three components [12].These components are defined as lost (Q 1), desorbed (Q 2) and residual (Q 3) gas.The Q 1 is gas lost from the samples subsequent to its removal from its in-situ position and prior to its containment in the canister. Q 2 is the …
DOI: 10.1016/J.POWTEC.2018.11.056 Corpus ID: 105755238; Machine vision based monitoring and analysis of a coal column flotation circuit @article{Massinaei2019MachineVB, title={Machine vision based monitoring and analysis of a coal column flotation circuit}, author={M. Massinaei and A. Jahedsaravani and …
A nation's ability to use its energy resources efficiently and the expansion of its industrial sector are key factors in its prosperity. Most of the industrially developed nations in the world use coal as their main energy source. These resources have been utilized mainly for industrial growth, such as thermal power plants, steel industries, and other …
The thickness of tectonically deformed coal (TDC) has positive correlation associations with gas outbursts. In order to predict the TDC thickness of coal beds, we propose a new quantitative predicting method using an extreme learning machine (ELM) algorithm, a principal component analysis (PCA) algorithm, and seismic attributes.
Cutting teeth are the parts that are tended to damage on mining machine.Through analyzing and researching for cutting teeth in a long period,at some aspect,such as the appliance of new type cutting teeth and arrangement and structure improving of cutting teeth, this article simply analyzes cutting teeth's reliability,in order to increase the …
This study uses combined thermogravimetric analysis (TGA)/differential thermal calorimetry (DSC) instrumentation as a tool to evaluate the reactivity patterns of the aliphatic versus aromatic content …
ABSTRACT Coal is one of the chief energy sources having significant applications in the iron and steel industry. This research investigates the screening efficiency of coal of different size range. The experiments on the screening of coal with different size range in the screening machine were carried out using different mesh …
This book deals with the various aspects of coal analysis and provides a detailed explanation of the necessary standard tests and procedures that are applicable to coal …
Results. Through applying three feature selection approaches based on machine learning algorithms, it was found that AaDO 2 and some pulmonary function indicators played an important role in prediction for identifying CWP of early stage. The support vector machine (SVM) algorithm was proved as the optimal machine learning …
Statistical analysis was performed on the compiled database to extract valuable insights from experimental data. Four machine learning models (SVR, ANN, RFR, and GBR) were employed to predict product distribution and composition based on biomass-coal properties and operating parameters.
In current work, 14 different kinds of standard coal samples used in the experiment are from Jinan Quandong Institute of reference materials. The major elements of these samples are shown in Table 1. 4.6-g powdered coal particles were loaded into the mold of full-automatic hydraulic press, in order to obtain multiple sets of data, a larger …
Machine learning prediction of calorific value of coal based on the hybrid analysis Int J Coal Prep Util, 43 ( 3 ) ( 2023 ), pp. 577 - 598, 10.1080/19392699.2022.2064454 Google Scholar
1. Introduction. Over the years, there has been an increasing demand for sustainable and renewable sources of energy. Energy production and energy utilization symbolize the economic progress of a country [1].Regarding energy production through renewable sources, India ranks third among the countries in the world [2].Global …
Several studies have used machine learning technology to predict the biomass-coal co-pyrolysis process, as evidenced by the findings summarized in Table 1. These studies have showcased the effectiveness of machine learning models in accurately estimating the decomposition kinetics during biomass-coal co-pyrolysis.
In this work, we develop a LIBS system for at-line coal analysis, which can pre-treat coal blocks into pressed pellets, acquire sample spectra and quantify coal …
Request PDF | Regression modeling and residual analysis of screening coal in screening machine | Coal is one of the chief energy sources having significant applications in the iron and steel industry.
In our previous work, an approach based on image analysis and particle swarm optimization-support vector machine was presented (Wang et al. 2021) to detect the coal-carrying rate in gangue ...
The turbulent airflow generated by the shearer's rotation has a significant impact on the spread of cutting dust. In this study, the actual shearer drum was used as a physical model, and the MRF method was applied to simulate coal cutting operations with consideration of rotating fluid, accurately reproducing the turbulent airflow generated …
Purpose: The purpose of this study is to propose an efficient coal workers' pneumoconiosis (CWP) clinical prediction system and put it into clinical use for clinical diagnosis of pneumoconiosis. Methods: Patients with CWP and dust-exposed workers who were enrolled from August 2021 to December 2021 were included in this study. Firstly, we …
