SHAP-based interpretation of an XGBoost model in the prediction of grindability of coals and their blends-Reference-Cited by-同舟云学术

SHAP-based interpretation of an XGBoost model in the prediction of grindability of coals and their blends

Published:2021-08-08 Issue: Volume: Page:1-21
ISSN:1939-2699
Container-title:International Journal of Coal Preparation and Utilization
language:en
Short-container-title:International Journal of Coal Preparation and Utilization

Author:

Rzychoń Maciej¹,Żogała Alina¹,Róg Leokadia²

Affiliation:

1. Department of Acoustics, Electronics and IT Solutions, Central Mining Institute, Katowice, Poland

2. Department of Solid Fuel Quality Assessment, Central Mining Institute, Katowice, Poland

Publisher

Informa UK Limited

Subject

Mechanical Engineering,Energy Engineering and Power Technology,Geotechnical Engineering and Engineering Geology,Fuel Technology,General Chemical Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/19392699.2021.1959324

Reference44 articles.

1. Grindability Studies on Blended Coals of High-Ash Indian Coals with Low-Ash Imported Coals

2. Studies of the relationship between petrography and grindability for Kentucky coals using artificial neural network

3. Carpenter, A. M. 1995. Coal blending for power stations. IEACR-81. IEA Coal Research, London, United Kingdom.

4. Prediction of coal grindability based on petrography, proximate and ultimate analysis using multiple regression and artificial neural network models

5. XGBoost

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