Abstract

The use of waste tires for producing valuable chemicals via fast pyrolysis necessarily involves the understanding and synthesis of catalysts. Therefore, here, a statistical-based screening of SiO2-supported metal catalysts (Ni, Pd, Co, and Fe) to produce limonene from waste tire pyrolysis (WTP) is presented. The response surface method (RSM) was integrated into a principal component analysis (PCA) to identify the catalyst and reaction conditions that maximize the limonene yields for the experiments performed in an analytical pyrolyzer. The experiments were performed in an analytical pyrolysis unit coupled to a mass spectrometer (Py-GC/MS) using the temperature, the tire-to-catalyst ratio, and the type of catalyst as independent variables. The samples were grouped using PCA into 4 clusters according to the studied experimental conditions. The RSM model demonstrates that Co/SiO2 generates the most positive influence on the selectivity towards limonene under the following operating conditions: 370 degrees C and a tire-to-catalyst ratio of 1:5. Furthermore, it is possible to maintain a high selectivity to limonene and reduce the optimal catalyst load by slightly increasing the reaction temperature.