Inioluwa Christianah Afolabi
Data-Driven Machine Learning Approach for Predicting the Higher Heating Value of Different Biomass Classes
Afolabi, Inioluwa Christianah; Epelle, Emmanuel I.; Gunes, Burcu; Güleç, Fatih; Okolie, Jude A.
Authors
Emmanuel I. Epelle
Burcu Gunes
DR FATIH GULEC FATIH.GULEC1@NOTTINGHAM.AC.UK
Assistant Professor in Chemical and Environmental Engineering
Jude A. Okolie
Contributors
Yongliang Xie
Editor
Shimao Wang
Editor
Abstract
Higher heating values (HHV) is a very useful parameter for assessing the design and large-scale operation of biomass-driven energy systems. HHV is conventionally measured experimentally with an adiabatic oxygen bomb calorimeter. This procedure is often time-consuming and expensive. Furthermore, limited access to the required facilities is the main bottleneck for researchers. Empirical linear and nonlinear models have initially been proposed to address these concerns. However, most of the models showed discrepancies with experimental results. Data-driven machine learning (ML) methods have also been adopted for HHV predictions due to their suitability for nonlinear problems. However, most ML correlations are based on proximate or ultimate analysis. In addition, the models are only applicable to either the originator biomass or one specific type. To address these shortcomings, a total of 227 biomass datasets based on four classes of biomass, including agricultural residue, industrial waste, energy crop, and woody biomass, were employed to develop and verify three different ML models, namely artificial neural network (ANN), decision tree (DT) and random forest (RF). The model incorporates proximate and ultimate analysis data and biomass as input features. RF model is identified as the most reliable because of its lowest mean absolute error (MAE) of 1.01 and mean squared error (MSE) of 1.87. The study findings can be used to predict HHV accurately without performing experiments.
Citation
Afolabi, I. C., Epelle, E. I., Gunes, B., Güleç, F., & Okolie, J. A. (2022). Data-Driven Machine Learning Approach for Predicting the Higher Heating Value of Different Biomass Classes. Clean Technologies, 4(4), 1227-1241. https://doi.org/10.3390/cleantechnol4040075
Journal Article Type | Article |
---|---|
Acceptance Date | Nov 18, 2022 |
Online Publication Date | Nov 22, 2022 |
Publication Date | 2022-12 |
Deposit Date | May 7, 2024 |
Publicly Available Date | May 8, 2024 |
Electronic ISSN | 2571-8797 |
Publisher | MDPI |
Peer Reviewed | Peer Reviewed |
Volume | 4 |
Issue | 4 |
Pages | 1227-1241 |
DOI | https://doi.org/10.3390/cleantechnol4040075 |
Keywords | Article, machine learning, biomass, higher heating value, biofuel, artificial neural network |
Public URL | https://nottingham-repository.worktribe.com/output/14880020 |
Publisher URL | https://www.mdpi.com/2571-8797/4/4/75 |
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Data-Driven Machine Learning Approach for Predicting the Higher Heating Value of Different Biomass Classes
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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
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