Predictions of elemental composition of coal and biomass from their proximate analyses using ANFIS, ANN and MLR |
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Author: | Lawal, Abiodun Ismail1; Aladejare, Adeyemi Emman2; Onifade, Moshood3,4; |
Organizations: |
1Department of Mining Engineering, Federal University of Technology, Akure, Nigeria 2Oulu Mining School, University of Oulu, Oulu, Finland 3Informetrics Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
4Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam
5Clean Coal and Sustainable Energy Research Group, Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, South Africa 6Division of Mining and Geotechnical Engineering, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Luleå, Sweden |
Format: | article |
Version: | published version |
Access: | open |
Online Access: | PDF Full Text (PDF, 2.7 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe2021061036468 |
Language: | English |
Published: |
Springer Nature,
2021
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Publish Date: | 2021-06-10 |
Description: |
AbstractThe elemental composition of coal and biomass provides significant parameters used in the design of almost all energy conversion systems and projects. The laboratory tests to determine the elemental composition of coal and biomass is time-consuming and costly. However, limited research has suggested that there is a correlation between parameters obtained from elemental and proximate analyses of these materials. In this study, some predictive models of the elemental composition of coal and biomass using soft computing and regression analyses have been developed. Thirty-one samples including parameters of elemental and proximate analyses were used during the analyses to develop multiple prediction models. Dependent variables for multiple prediction models were selected as carbon, hydrogen, and oxygen. Using volatile matter, fixed carbon, moisture and ash contents as independent variables, three different prediction models were developed for each dependent parameter using ANFIS, ANN, and MLR. In addition, a routine for selecting the best predictive model was suggested in the study. The reliability of the established models was tested by using various prediction performance indices and the models were found to be satisfactory. Therefore, the developed models can be used to determine the elemental composition of coal and biomass for practical purposes. see all
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Series: |
International journal of coal science & technology |
ISSN: | 2095-8293 |
ISSN-E: | 2198-7823 |
ISSN-L: | 2095-8293 |
Volume: | 8 |
Issue: | 1 |
Pages: | 124 - 140 |
DOI: | 10.1007/s40789-020-00346-9 |
OADOI: | https://oadoi.org/10.1007/s40789-020-00346-9 |
Type of Publication: |
A1 Journal article – refereed |
Field of Science: |
1171 Geosciences |
Subjects: | |
Copyright information: |
© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
https://creativecommons.org/licenses/by/4.0/ |