Data-driven computational approaches to estimate gross calorific value of coal using proximate and ultimate analyses-Reference-Cited by-同舟云学术

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Data-driven computational approaches to estimate gross calorific value of coal using proximate and ultimate analyses

Published:2024-01-02 Issue: Volume: Page:1-26
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:

Munshi Tanveer Alam¹,Jahan Labiba Nusrat¹,Howladar M. Farhad¹,Hashan Mahamudul¹

Affiliation:

1. Department of Petroleum and Mining Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh

Funder

Ministry of Science and Technology, Government of the People’s Republic of Bangladesh

Shahjalal University of Science and Technology (SUST) Research Center

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/19392699.2023.2298372

Reference64 articles.

1. Prediction of gross calorific value of coal based on proximate analysis using multiple linear regression and artificial neural networks

2. Dynamic failure analysis of process systems using neural networks

3. Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study

4. Artificial neural networks workflow and its application in the petroleum industry

5. Hydraulic unit prediction using support vector machine

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