Improved ANN-Based Approach Using Relative Impact for the Prediction of Thermal Coal Elemental Composition Using Proximate Analysis-Reference-Cited by-同舟云学术

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Improved ANN-Based Approach Using Relative Impact for the Prediction of Thermal Coal Elemental Composition Using Proximate Analysis

Published:2022-08-16 Issue:34 Volume:7 Page:29734-29746
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Jo Jangho¹,Lee Dae-Gyun¹,Kim Jongho²,Lee Byoung-Hwa³,Jeon Chung-Hwan¹³^ORCID

Affiliation:

1. School of Mechanical Engineering, Pusan National University, Busan 46241, Republic of Korea

2. Chemical Engineering, University of Newcastle, Callaghan, New South Wales 2308, Australia

3. Pusan Clean Energy Research Institute, Pusan National University, Busan 46241, Republic of Korea

Funder

National Research Foundation of Korea

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.2c02324

Reference43 articles.

1. A review of the correlations of coal properties with elemental composition

2. Rapid Determination of the Gross Calorific Value of Coal Using Laser-Induced Breakdown Spectroscopy Coupled with Artificial Neural Networks and Genetic Algorithm

3. A New Global Mechanism for MILD Combustion Using Artificial-Neural-Network-Based Optimization

4. Prediction and Sensitivity Analysis of the Cetane Number of Different Biodiesel Fuels Using an Artificial Neural Network

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Prediction of unburned carbon and NOx emissions of solid fuel using petrographic UBC index and artificial neural network;Fuel;2024-10

2. MSA-Net: A Precise and Robust Model for Predicting the Carbon Content on an As-Received Basis of Coal;Sensors;2024-07-16

3. Development of Empirical and Artificial Neural Network Model for the Prediction of Sorption Time to Assess the Potential of CO₂ Sequestration in Coal;ACS Omega;2023-08-16

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