Machine learning approach for categorical biomass higher heating value prediction based on proximate analysis

ABSTRACT

The present study attempts a new approach for optimizing higher heating value (HHV) of agricultural biomass from only proximate analysis data, thereby requiring nominal specificity compared to elemental composition-based models. This objective was achieved by classifying the largest extracted 1140 data into five categories and by adopting five machine learning (ML) models namely: Multi Layer Perceptron, Random Forest, Support Vector Machine, Linear Regression and Artificial Neural Network. Further, relative correlation and contribution of different input parameters on target variable was performed to forecast the impact level and interaction. It showed higher relevancy factor for FC and VM as 0.82 and 0.91 respectively. Compared to other models, Artificial neural network and Random Forest model provided better accuracy with higher correlation coefficient values (0.991 and 0.989 respectively) and lower root mean square error 0.215 and 0.283 respectively. The validity and applicability of the database and ML modeling were verified using Box plots, in which more than 96% of the data for each category of biomass were located in the valid region. The values of statistical indicators proved better prediction accuracy of our proposed models in comparison to other reported literature models for all predefined categories of biomass.

KEYWORDS:

• Artificial neural network
• biomass
• higher heating value
• machine learning
• proximate analysis

Abbreviations/Nomenclatures

Abbreviation	=	Full form
HHV	=	Higher Heating Value (MJ/kg)
FC	=	Fixed carbon (wt%)
VM	=	Volatile matter (wt%)
ASH	=	Ash content (wt%)
ML	=	Machine Learning
ANN	=	Artificial Neural Network
RF	=	Random Forest
MLP	=	Multi Layer Perceptron
SVM	=	Support Vector Machine
LR	=	Linear Regression
WEKA	=	Waikato Environment for Knowledge Analysis
AW	=	Agri waste
FBP	=	Fruits by product
GL	=	Grass leaves and tree species
OWM	=	Other waste materials
WC	=	Wood chips
wt%	=	Mass fraction or percentage by weight
PCC	=	Pearson Correlation Coefficient
CC or R2	=	Coefficient of correlation
RMSE	=	Root mean square error

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

The datasets of 1140 data generated in this study are available from the corresponding author on reasonable request.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.