# Data-Driven Machine Learning Approach for Predicting the Higher Heating Value of Different Biomass Classes

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## Abstract

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## 1. Introduction

^{2}as a selection criterion for comparing the accuracy of the models. RF algorithm showed the best performance with R

^{2}> 0.94. However, biomass classification was not included in the ML model. This study presents a comprehensive predictive ML model based on DT, random forest regression (RF), and ANN for predicting HHV values of different biomass classes. Proximate and ultimate analysis data of four different biomass classes, including agricultural residue, industrial waste, energy crop, and woody biomass, were used in the development of the model. Results from the ML models were compared with empirical models and literature-reported ML models.

#### Relevant Literature and Study Novelty

^{2}value > 0.9.

- -
- 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}) was not considered as a ML model evaluation criterion in this study due to the nonlinear relationships between the target and features.

#### 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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**Figure 1.**Differences between experimental and model HHV values. Reprinted/adapted with permission from Vaezi et al. [9].

**Figure 4.**Box plot results of the statistical analysis of the input features and target (HHV) obtained from the entire dataset used in the machine learning models.

**Figure 7.**Graphical comparison of experimental and predicted HHV of different ML models using the test dataset.

**Figure 9.**Comparison of the predicted HHV from ML model with empirical models. Note: All the empirical correlations are outlined in Table 1.

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 C^{2} − 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) | −30.3FC^{2} + 65.2Ash^{2} + 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 |

**Table 3.**Comparison of statistical measurement criteria with ML-based HHV values reported in the literature.

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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**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Afolabi, 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