University of Oulu

Lawal, A.I., Aladejare, A.E., Onifade, M. et al. Predictions of elemental composition of coal and biomass from their proximate analyses using ANFIS, ANN and MLR. Int J Coal Sci Technol 8, 124–140 (2021). https://doi.org/10.1007/s40789-020-00346-9

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
Publish Date: 2021-06-10
Description:

Abstract

The 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.

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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:
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