Evaluation of models to predict the influence of chemical pretreatment on the peels of Nephelium lappaceum L. based on pyrolysis kinetic parameters obtained using a combined Fraser-Suzuki function and Friedman’s isoconversional method

In the present work, two chemometric strategies were evaluated to assess the influence of the chemical pretreatment: a Kohonen self-organizing map neural network and a Support Vector Machine Learning algorithm. Kohonen ANN was able to predict the influence of chemical treatment mainly on pseudo-component lignin, since cellulose and hemicellulose have similar structures; presenting a topological error of 0.044 and a quantification error of 0.193, while the Vector Supporting Machine (SVM) algorithm was able to predict the classes with accuracy of 0.956, area under the ROC curve (AUC) of 0.992 and F1 score of 0.956, proving to be a more effective strategy in predicting the influence of the chemical treatment on the rambutan shells based on the kinetic parameters. It was also observed an increase in the activation energy for the cellulose pretreated with sodium hydroxide and with phosphoric acid, mainly due to the partial conversion of type I cellulose into type II cellulose, ranging from 152 to 155 kJ/mol versus 124 kJ/mol for the untreated sample. The thermal decomposition mechanism did not change significantly, being estimated as pseudo-zero order, using the Master-Plot method. TG-FTIR analysis reveals that the main gases emitted during pyrolysis are CO2, H2O and CO, being the carbon dioxide-related band an indicator of the influence of the chemical treatment. This analysis also reveals the release of sulfur compounds from the sample treated with sulfuric acid, indicating that the environmental point of view of this sample shows no viability as use as pyrolysis feedstock.