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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.

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Authors

Inioluwa Christianah Afolabi

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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