Forecast of the higher heating value in biomass torrefaction by means of machine learning techniques

Abstract

Torrefaction of biomass can be explained as a mild type of pyrolysis at temperatures usually ranging between 200 and 300 °C in lack of oxygen. Torrefaction of biomass enhances properties as the moisture content and calorific value. The objective of this study was to acquire a predictive model of the higher heating value (HHV) in a biomass torrefaction process. This study introduces a new hybrid algorithm, relied on support vector machines (SVMs) combined with the simulated annealing (SA) optimization technique, for predicting the calorific value (HHV) of biomass from operation input parameters determined experimentally during the torrefaction process. Additionally, a multivariate adaptive regression splines (MARS) approach and random forest (RF) technique were fitted to the experimental data for comparison purposes. The results of the present study are two-fold. In the first place, the significance of each physical–chemical variables on the higher heating value (HHV) is presented through the model. Secondly, several models for forecasting the calorific value of torrefied biomass are obtained. Indeed, when this hybrid SVM–SA-based model with RBF kernel function was applied to the experimental dataset with the optimal hyperparameters, a coefficient of determination equal to 0.90 was achieved for the higher heating value estimation of torrefied biomass. Moreover, the results accomplished with the MARS–SA-based approach and RF–SA-based technique are worse than those achieved with the RBF–SVM–SA-based model. The agreement between experimental data and the model demonstrated the good performance of the latter. • A RBF–SVM–SA-based predictive model is built for the torrefied biomass’ HHV. • The characterization of the biomass’ HHV is important in its quality assessment. • The relative importance of input variables in torrefaction process is determined. • The results of this new hybrid model are compared with MARS–SA and RF–SA models.