An Integrated Kinetic Modeling Framework for Copyrolysis of Biomass and Plastic Waste

Developing robust kinetic models for pyrolytic processes is challenging due to the complex properties of solid feedstock materials and their intricate reaction pathways. This study introduced a three-module modeling framework designed to provide a systematic approach for addressing these challenges. A kinetic model was developed to simulate the copyrolysis of red oak wood and polyethylene terephthalate (PET) at a 1:1 mass mixing ratio. In the first module, a parallel reaction mechanism was developed, and the corresponding kinetic parameters were initially estimated using the Friedman method with thermogravimetric (TGA) data and subsequently refined through a least-squares optimization method. A kinetic model using the parallel reaction mechanism was used to predict the conversion of the solid mixture. Due to the limitations of TGA data, the kinetic model was incapable of predicting the product yields from copyrolysis. In the second module, the kinetic model was retrained with experimental data of copyrolysis in a vertical-tube reactor, and the parallel reaction mechanism was also updated with the mass distribution coefficients of bioproducts calculated using the SLSQP (Sequential Least Squares Programming) method. However, this model exhibited inaccuracies at a higher temperature, 700 °C, indicating the absence of crucial secondary reactions. To address this issue, the kinetic model was restructured by combining tar decomposition reactions with the parallel-reaction mechanism. The kinetic parameters of tar decomposition reactions were calculated using the PSO (Particle Swarm Optimization) method. Finally, the kinetic model using the combined reaction mechanism successfully simulated copyrolysis and was validated with experimental data at 500, 600, and 700 °C, providing accurate predictions for both solid conversion and product generation. The findings of this work demonstrate that the methodology for identifying reaction mechanisms and determining kinetic parameters can also be valuable to the modeling of other complex pyrolytic processes.