Fuel Processing Technology, volume 211, pages 106584
Prediction of Gray-King coke type from radical concentration and basic properties of coal blends
3
Beijing Advanced Innovation Center for Soft Matter Science and Engineering
|
5
Beijing 100029 China
|
Publication type: Journal Article
Publication date: 2021-01-01
Journal:
Fuel Processing Technology
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 7.5
ISSN: 03783820
General Chemical Engineering
Energy Engineering and Power Technology
Fuel Technology
Abstract
Metallurgical coke is mainly produced from coal blends. The coke qualities have been related with or predicted by numerous polynomials with one or a few coal parameters from the proximate and ultimate analyses, maximum vitrinite reflectance (Rmax) and quantity of plastic matters. More fundamental and intrinsic prediction of coke quality, such as that required by artificial intelligence in the future, calls for relations between coke quality and its intermediate state, such as the coke type (CT) determined by the well-known Gray King (GK) assay, and consequently the relations between GKCT with the basic properties of coal blends. This work studies the GKCT of 68 coal blends and predicts the G type coke (G-coke), the best coke form defined by GK, with the parameters from the ultimate and proximate analyses, and the radical concentration (Cr) of coals because Cr is found correlating well with Rmax. The prediction methods include the traditional single- and multi-parameter range (MPR) methods and 3 machine learning models, namely K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM). It is found that the readily measurable Cr of coals is an important parameter in GKCT prediction. MPR, KNN, LDA and SVM are capable to predict G-coke with no more than 5 parameters, and SVM is more effective than other models.
Citations by journals
|
1
2
|
|
|
Fuel Processing Technology
|
Fuel Processing Technology
2 publications, 33.33%
|
|
ACS Omega
|
ACS Omega
1 publication, 16.67%
|
|
International Journal of Coal Preparation and Utilization
|
International Journal of Coal Preparation and Utilization
1 publication, 16.67%
|
|
Energy Sources, Part A: Recovery, Utilization and Environmental Effects
|
Energy Sources, Part A: Recovery, Utilization and Environmental Effects
1 publication, 16.67%
|
|
International Journal of Photoenergy
|
International Journal of Photoenergy
1 publication, 16.67%
|
|
1
2
|
Citations by publishers
|
1
2
|
|
|
Taylor & Francis
|
Taylor & Francis
2 publications, 33.33%
|
|
Elsevier
|
Elsevier
2 publications, 33.33%
|
|
American Chemical Society (ACS)
|
American Chemical Society (ACS)
1 publication, 16.67%
|
|
Hindawi Limited
|
Hindawi Limited
1 publication, 16.67%
|
|
1
2
|
- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
{"yearsCitations":{"type":"bar","data":{"show":true,"labels":[2021,2022,2023],"ids":[0,0,0],"codes":[0,0,0],"imageUrls":["","",""],"datasets":[{"label":"Citations number","data":[5,0,1],"backgroundColor":["#3B82F6","#3B82F6","#3B82F6"],"percentage":["83.33",0,"16.67"],"barThickness":null}]},"options":{"indexAxis":"x","maintainAspectRatio":true,"scales":{"y":{"ticks":{"precision":0,"autoSkip":false,"font":{"family":"Montserrat"},"color":"#000000"}},"x":{"ticks":{"stepSize":1,"precision":0,"font":{"family":"Montserrat"},"color":"#000000"}}},"plugins":{"legend":{"position":"top","labels":{"font":{"family":"Montserrat"},"color":"#000000"}},"title":{"display":true,"text":"Citations per year","font":{"size":24,"family":"Montserrat","weight":600},"color":"#000000"}}}},"journals":{"type":"bar","data":{"show":true,"labels":["Fuel Processing Technology","ACS Omega","International Journal of Coal Preparation and Utilization","Energy Sources, Part A: Recovery, Utilization and Environmental Effects","International Journal of Photoenergy"],"ids":[6825,18901,20143,3075,2954],"codes":[0,0,0,0,0],"imageUrls":["\/storage\/images\/resized\/GDnYOu1UpMMfMMRV6Aqle4H0YLLsraeD9IP9qScG_medium.webp","\/storage\/images\/resized\/iLiQsFqFaSEx6chlGQ5fbAwF6VYU3WWa08hkss0g_medium.webp","\/storage\/images\/resized\/5YZtvLvkPZuc2JHOaZsjCvGSHFCuC3drUwN3YAc5_medium.webp","\/storage\/images\/resized\/5YZtvLvkPZuc2JHOaZsjCvGSHFCuC3drUwN3YAc5_medium.webp","\/storage\/images\/resized\/hqfzhQAjTGlNSRs6yzFNITgjSMm9Jr2QuotJHIvE_medium.webp"],"datasets":[{"label":"","data":[2,1,1,1,1],"backgroundColor":["#3B82F6","#3B82F6","#3B82F6","#3B82F6","#3B82F6"],"percentage":[33.33,16.67,16.67,16.67,16.67],"barThickness":13}]},"options":{"indexAxis":"y","maintainAspectRatio":false,"scales":{"y":{"ticks":{"precision":0,"autoSkip":false,"font":{"family":"Montserrat"},"color":"#000000"}},"x":{"ticks":{"stepSize":null,"precision":0,"font":{"family":"Montserrat"},"color":"#000000"}}},"plugins":{"legend":{"position":"top","labels":{"font":{"family":"Montserrat"},"color":"#000000"}},"title":{"display":true,"text":"Journals","font":{"size":24,"family":"Montserrat","weight":600},"color":"#000000"}}}},"publishers":{"type":"bar","data":{"show":true,"labels":["Taylor & Francis","Elsevier","American Chemical Society (ACS)","Hindawi Limited"],"ids":[18,17,40,6921],"codes":[0,0,0,0],"imageUrls":["\/storage\/images\/resized\/5YZtvLvkPZuc2JHOaZsjCvGSHFCuC3drUwN3YAc5_medium.webp","\/storage\/images\/resized\/GDnYOu1UpMMfMMRV6Aqle4H0YLLsraeD9IP9qScG_medium.webp","\/storage\/images\/resized\/iLiQsFqFaSEx6chlGQ5fbAwF6VYU3WWa08hkss0g_medium.webp","\/storage\/images\/resized\/hqfzhQAjTGlNSRs6yzFNITgjSMm9Jr2QuotJHIvE_medium.webp"],"datasets":[{"label":"","data":[2,2,1,1],"backgroundColor":["#3B82F6","#3B82F6","#3B82F6","#3B82F6"],"percentage":[33.33,33.33,16.67,16.67],"barThickness":13}]},"options":{"indexAxis":"y","maintainAspectRatio":false,"scales":{"y":{"ticks":{"precision":0,"autoSkip":false,"font":{"family":"Montserrat"},"color":"#000000"}},"x":{"ticks":{"stepSize":null,"precision":0,"font":{"family":"Montserrat"},"color":"#000000"}}},"plugins":{"legend":{"position":"top","labels":{"font":{"family":"Montserrat"},"color":"#000000"}},"title":{"display":true,"text":"Publishers","font":{"size":24,"family":"Montserrat","weight":600},"color":"#000000"}}}}}
Metrics
Cite this
GOST |
RIS |
BibTex
Cite this
GOST
Copy
Xiang C. et al. Prediction of Gray-King coke type from radical concentration and basic properties of coal blends // Fuel Processing Technology. 2021. Vol. 211. p. 106584.
GOST all authors (up to 50)
Copy
Xiang C., Liu Q., Shi L., Zhou B., Liu Z. Prediction of Gray-King coke type from radical concentration and basic properties of coal blends // Fuel Processing Technology. 2021. Vol. 211. p. 106584.
Cite this
RIS
Copy
TY - JOUR
DO - 10.1016/j.fuproc.2020.106584
UR - https://doi.org/10.1016%2Fj.fuproc.2020.106584
TI - Prediction of Gray-King coke type from radical concentration and basic properties of coal blends
T2 - Fuel Processing Technology
AU - Xiang, Chong
AU - Liu, Qingya
AU - Shi, Lei
AU - Zhou, Bin
AU - Liu, Zhenyu
PY - 2021
DA - 2021/01/01 00:00:00
PB - Elsevier
SP - 106584
VL - 211
SN - 0378-3820
ER -
Cite this
BibTex
Copy
@article{2021_Xiang,
author = {Chong Xiang and Qingya Liu and Lei Shi and Bin Zhou and Zhenyu Liu},
title = {Prediction of Gray-King coke type from radical concentration and basic properties of coal blends},
journal = {Fuel Processing Technology},
year = {2021},
volume = {211},
publisher = {Elsevier},
month = {jan},
url = {https://doi.org/10.1016%2Fj.fuproc.2020.106584},
pages = {106584},
doi = {10.1016/j.fuproc.2020.106584}
}