The European Union created a European Green Deal Program (EGDP). This program aims at a sustainable economy through the transformation of the challenges related to climate and the environment. The main goal of EGDP is climate neutrality by 2050. The increase of alternative biomass residues utilization from various food processing industries and cooperation in the energy and waste management sector is required to meet these expectations. Nut shells are one of the lesser-known, yet promising, materials that can be used as an alternative fuel or a pre-treated product to further applications. However, from a thermal conversion point of view, it is important to know the energy properties and kinetic parameters of the considered biowaste. In this study, the energy and kinetic parameters of walnut, hazelnut, peanut, and pistachio shells were investigated. The results showed that raw nut shells are characterized by useful properties such as higher heating value (HHV) at 17.8–19.7 MJ∙kg−1 and moisture content of 4.32–9.56%. After the thermal treatment of nut shells (torrefaction, pyrolysis), the HHV significantly increased up to ca. 30 MJ∙kg−1. The thermogravimetric analysis (TGA) applying three different heating rates (β; 5, 10, and 20 °C∙min−1) was performed. The kinetic parameters were determined using the isothermal model-fitting method developed by Coats–Redfern. The activation energy (Ea) estimated for β = 5 °C∙min−1, was, e.g., 60.3 kJ∙mol−1∙K−1 for walnut, 59.3 kJ∙mol−1∙K−1 for hazelnut, 53.4 kJ∙mol−1∙K−1 for peanut, and 103.8 kJ∙mol−1∙K−1 for pistachio, respectively. Moreover, the increase in the Ea of nut shells was observed with increasing the β. In addition, significant differences in the kinetic parameters of the biomass residues from the same waste group were observed. Thus, characterization of specific nut shell residues is recommended for improved modeling of thermal processes and designing of bioreactors for thermal waste treatment.
Evaluation of Non-Isothermal Kinetic Parameters for Pyrolysis of Teak Wood using Model-Fitting Techniques
