Pyrolysis of macadamia nut peel using multicomponent Gaussian kinetic modeling and ANN analysis

Abstract

The physicochemical properties of macadamia nut peel (MNP) were explored based on pyrolysis characteristics, kinetics, thermodynamic parameters, reaction mechanism, and artificial neural network (ANN) application to evaluate its suitability and potential for pyrolytic conversion into bioenergy products. Pyrolysis experiments of MNP were performed using a thermogravimetric analyzer in a nitrogen atmosphere. The thermal degradation process of MNP can be successfully modeled into pseudo hemicellulose (PS–H1), cellulose (PS–C2), and lignin (PS-L3) using a multicomponent Gaussian kinetic model with high correlation coefficient (R2 > 0.99). The average apparent activation energy was 179.28 kJ/mol for PS-H1, 192.31 kJ/mol for PS-C2, and 129.42 kJ/mol for PS-L3 using Ozawa-Flynn-Wall, Kissinger-Akahira-Sunose, Starink, and Tang four model-free methods. Master plots method was used to determine the reaction mechanism models of three pseudo components, and the pyrolysis progress can be described well using order-based and nucleation mechanism models. The thermal degradation behaviors of MNP were predicted using artificial neural network, and the best artificial neural network prediction model was ANN (3*15*1). Based on the physicochemical properties, pyrolysis characteristics, kinetics, thermodynamic parameters, reaction mechanism and ANN prediction model, MNP is suitable for pyrolytic conversion into bioenergy products.