Data-Driven Machine Learning Approach for Predicting the Higher Heating Value of Different Biomass Classes
Abstract
:1. Introduction
Relevant Literature and Study Novelty
- -
- This study proposes a comprehensive ML model comprising proximate and ultimate analysis and different biomass classification input features. Specifically, the biomass classification is selected to capture a wide range of materials, including agricultural residues, energy crops, woody biomass, and industrial waste.
- -
- This study applies a robust data set of 227 different biomass materials and computationally compares the performance of three different ML algorithms, including RF, DT, and ANN.
2. Methodology
2.1. Dataset Collection and Pre-Processing
2.2. Overview of the Machine Learning Algorithm
2.2.1. Artificial Neural Networks (ANN)
2.2.2. Decision Tree Regression (DT)
2.2.3. Random Forest (RF)
2.3. Empirical Correlations
3. Results and Discussion
3.1. Statistical Analysis of the Dataset
3.2. Model Performance Evaluation
3.3. Feature Analysis of the Best Model
3.4. Comparison of Other Models from Literature and Empirical Models
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AC | Ash content |
ANN | Artificial neural network |
ASTM | American Society for Testing and Materials |
C | Carbon content |
DT | Decision tree |
FC | Fixed carbon content |
H | Hydrogen content |
HHV | Higher heating value |
IEA | International Energy Agency |
LHV | Lower heating value |
MAE | Mean absolute error |
ML | Machine learning |
MSE | Mean Squared Error |
N | Nitrogen content |
O | Oxygen content |
RF | Random Forest regression |
RMSE | Root mean square error |
S | Sulphur content |
VM | Volatile matter |
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Empirical Correlation | Equation for HHV in MJ/kg | Biomass Used | References |
---|---|---|---|
Demirbas correlation | 0.01 (33.5C + 142.3H − 15.4O − 14.5 N) | Agricultural residues | Demirbaş [7] |
Sheng correlation | −1.3675 + 0.3137C + 0.7009H + 0.0318O | Agricultural residues | Sheng and Azevedo [8] |
Friedl correlation | 20600 + 3.55 C2 − 232C − 2230H + (51.2C × H) +131N | Wood, grass, rye, rape, reed, brewery waste, and poultry litter | Friedl et al. [6] |
Yu correlation (Ultimate analysis) | 0.2949C + 0.8250H | Agricultural residues | Yu et al. [10] |
Yu correlation (Proximate analysis) | 0.1905VM + 0.2521FC | Agricultural residues | Yu et al. [10] |
Qian correlation (Ultimate analysis) | 32.9C + 162.7H − 16.2O − 954.4S + 1.408 | Biochars | Qian et al. [11] |
Qian correlation (proximate analysis) | −30.3FC2 + 65.2Ash2 + 55.4FC − 48.5Ash + 9.591 | Biochars | Qian et al. [11] |
ML Models | MAE | MSE | RMSE |
---|---|---|---|
DT | 1.48 | 4.36 | 2.09 |
RF | 1.01 | 1.87 | 1.37 |
ANN | 1.21 | 2.43 | 1.56 |
Machine Learning Model | Input Feature | RMSE | References |
---|---|---|---|
RF | Proximate and ultimate analysis, biomass classes | 1.37 | This study |
DT | Proximate and ultimate analysis, biomass classes | 2.09 | This study |
ANN | Proximate and ultimate analysis, biomass classes | 1.56 | This study |
ANN | Proximate analysis of biochars | 0.65 | Çakman et al., 2021 [34] |
Extreme learning machine | Ultimate analysis | 1.93 | Dai et al. [35] |
ANN | Ultimate analysis | 3.87 | Xing et al. [18] |
RF | Ultimate analysis | 2.39 | Xing et al. [18] |
SVM | Ultimate analysis | 2.53 | Xing et al. [18] |
Genetic programming (GP) | Ultimate analysis | 0.95 | Ghugare et al. [32] |
Multilayer perceptron neural network (MLP) | Ultimate analysis | 0.99 | Ghugare et al. [32] |
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Afolabi, I.C.; Epelle, E.I.; Gunes, B.; Güleç, F.; Okolie, J.A. Data-Driven Machine Learning Approach for Predicting the Higher Heating Value of Different Biomass Classes. Clean Technol. 2022, 4, 1227-1241. https://doi.org/10.3390/cleantechnol4040075
Afolabi IC, Epelle EI, Gunes B, Güleç F, Okolie JA. Data-Driven Machine Learning Approach for Predicting the Higher Heating Value of Different Biomass Classes. Clean Technologies. 2022; 4(4):1227-1241. https://doi.org/10.3390/cleantechnol4040075
Chicago/Turabian StyleAfolabi, Inioluwa Christianah, Emmanuel I. Epelle, Burcu Gunes, Fatih Güleç, and Jude A. Okolie. 2022. "Data-Driven Machine Learning Approach for Predicting the Higher Heating Value of Different Biomass Classes" Clean Technologies 4, no. 4: 1227-1241. https://doi.org/10.3390/cleantechnol4040075