Results 51 to 60 of about 111 (90)

Optimizing the use of Indigenous high ash bituminous coal for sustainable pulverized coal injection and efficient energy transfer. [PDF]

Sci Rep
Karmakar A, Gopinathan P, Subramani T, Naik AS, Sethi MK, Santosh M, Sarkar P, Banerjee PK.   +7 more
europepmc   +1 more source

Experiments on torrefied wood pellet: study by gasification and characterization for waste biomass to energy applications. [PDF]

R Soc Open Sci, 2016
Rollinson AN, Williams O.
europepmc   +1 more source

Determination of the cooling gas composition effect on the coke quality parameters during a coke dry quenching. [PDF]

Sci Rep
Kravchenko S, Кogtin A, Miroshnichenko D.   +2 more
europepmc   +1 more source

Mapping of compositional properties of coal using isometric log-ratio transformation and sequential Gaussian simulation - A comparative study for spatial ultimate analyses data. [PDF]

J Geochem Explor, 2018
Karacan CÖ, Olea RA.
europepmc   +1 more source

Characterization and Prediction of Coal Particle Size Distribution before and after Cleaning: An Integrated Experimental and Machine Learning Approach. [PDF]

ACS Omega
Ding H, Wen G, Long Q, Lin Y.
europepmc   +1 more source

Cleat and Fracture Systems in Coal with Implications for Strength, Hydrofracking, and Gas-Water Flow Mechanisms: A Critical Review. [PDF]

ACS Omega
Abrar SW, Mendhe VA, Kamble AD, Shukla P, Sharma P, Buragohain J, Kumari S, Kumari S, Anurag, Das AK, Kumari M, Subbaraidu S, Bordoloi A.   +12 more
europepmc   +1 more source

Prediction of Bond Work Index and Hardgrove Grindability Index Values from mechanical and index tests using deep neural networks method

Acta Geodynamica et Geomaterialia
Halil Boluk, Çağrı Aldı, Olgay Yaralı   +2 more
openaire   +1 more source

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Estimation of Hardgrove grindability index of Turkish coals by neural networks

International Journal of Mineral Processing, 2008
In this research, different techniques for the estimation of coal HGI values are studied. Data from 163 sub-bituminous coals from Turkey are used by featuring I I coal parameters, which include proximate analysis, group maceral analysis and rank. Nonlinear regression and neural network techniques are used for predicting the HGI values for the specified
A Murat Ozbayoglu, Gulhan Ozbayoglu
exaly   +3 more sources

Soft modelling of the Hardgrove grindability index of bituminous coals: An overview

International Journal of Coal Geology, 2021
Abstract Predictions of the Hardgrove grindability index, a predictor of the crushing and pulverization propensity of coal, have been made using both regression and neural network techniques. All techniques suffer from shortcomings. In general, input parameters must be selected based on a sound knowledge of coal chemistry and petrology, with ...
Saeed Chehreh Chelgani, , Saeid R Dindarloo   +2 more
exaly   +2 more sources