Neural Computing and Applications

, Volume 31, Issue 12, pp 8823–8836 | Cite as

Predictive modelling of the higher heating value in biomass torrefaction for the energy treatment process using machine-learning techniques

  • P. J. García NietoEmail author
  • E. García-Gonzalo
  • J. P. Paredes-Sánchez
  • A. Bernardo Sánchez
  • M. Menéndez Fernández
Original Article


Torrefaction of biomass can be described as a mild form of pyrolysis at temperatures typically ranging between 200 and 300 °C in the absence of oxygen. Common biomass reactions during torrefaction include devolatilization, depolymerization, and carbonization of hemicellulose, lignin, and cellulose. Torrefaction of biomass improves properties like moisture content as well as calorific value. The aim of this study was to obtain a predictive model able to perform an early detection of the higher heating value (HHV) in a biomass torrefaction process. This study presents a novel hybrid algorithm, based on support vector machines (SVMs) in combination with the particle swarm optimization (PSO) technique, for predicting the HHV of biomass from operation input parameters determined experimentally during the torrefaction process. Additionally, a multilayer perceptron network (MLP) and random forest (RF) were fitted to the experimental data for comparison purposes. To this end, the most important physical–chemical parameters of this industrial process are monitored and analysed. The results of the present study are two-fold. In the first place, the significance of each physical–chemical variables on the HHV is presented through the model. Secondly, several models for forecasting the calorific value of torrefied biomass are obtained. Indeed, when this hybrid PSO–SVM-based model with cubic kernel function was applied to the experimental dataset and regression with optimal hyperparameters was carried out, a coefficient of determination equal to 0.94 was obtained for the higher heating value estimation of torrefied biomass. Furthermore, the results obtained with the MLP approach and RF-based model are worse than the best obtained with the PSO–SVM-based model. The agreement between experimental data and the model confirmed the good performance of the latter. Finally, we expose the conclusions of this study.


Support vector machines (SVMs) Particle swarm optimization (PSO) Artificial neural networks (ANNs) Higher heating value (HHV) prediction 



Authors wish to acknowledge the computational support provided by the Department of Mathematics at University of Oviedo. Additionally, we would like to thank Anthony Ashworth for his revision of English grammar and spelling of the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mathematics, Faculty of SciencesUniversity of OviedoOviedoSpain
  2. 2.Department of Energy, College of Mining, Energy and Materials EngineeringUniversity of OviedoOviedoSpain
  3. 3.Department of Mining Technology, Topography and StructuresUniversity of LeónLeónSpain

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