U.S. flag

An official website of the United States government

Dot gov

Official websites use .gov
A .gov website belongs to an official government organization in the United States.


Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.


Main content area

Mathematical Model of Scrap Tire Rubber Pyrolysis in a Non-isothermal Fixed Bed Reactor: Definition of a Chemical Mechanism and Determination of Kinetic Parameters

Paola Gauthier-Maradei, Yeniffer Cely Valderrama, Debora Nabarlatz
Waste and biomass valorization 2019 v.10 no.3 pp. 561-573
analysis of variance, depolymerization, differential scanning calorimetry, feedstocks, mathematical models, polymers, prediction, pyrolysis, reaction mechanisms, rubber, temperature, tires
A chemical reaction mechanism is proposed to describe the pyrolysis of scrap tire rubber based on the decomposition of their three main polymer compounds (natural, butadiene and styrene-butadiene rubbers). Samples of each polymer were tested separately using differential scanning calorimetry (DSC) at the same operating conditions (heating rate, temperature and atmosphere). The thermograms clearly show that the polymer decomposition takes place in two or three thermal steps. The comparison with the literature allowed associating these steps with depolymerization reactions. The DSC results also allowed determining the kinetic parameters for each reaction considered in the chemical mechanism proposed in this study. Consequently, these were included in a mathematical model developed for a fixed bed reactor (at laboratory-scale) considering mass and energy balances. The experimental conversion obtained in TGA at operating conditions of pyrolysis using scrap tire rubber as feedstock, were successful confronted with those simulated by the mathematical model obtaining a determination coefficient (R²) of 0.97. On the other hand, the mathematical model predicts correctly the influence of the temperature in the product yields, being this variable the most statistically significant in the process, being in agreement with ANOVA results ((p value < 0.001 at confidence level of 95%) allowing a good prediction of the product yields.