Emerging trends in kinetic modelling of plastic waste pyrolysis for energy applications

The global rise in plastic waste has increased the demand for sustainable recycling methods, with pyrolysis showing promise in converting plastic waste into energy products, such as liquid oil, char and syngas. Accurate determination of kinetic parameters, especially activation energy (Ea), is essential for optimising pyrolysis processes. This paper critically reviews mechanisms, different models and challenges of chemical kinetic modelling of plastic waste pyrolysis. The challenges in methodological inconsistencies, reactor scalability, and catalytic stability are analysed in this review. It was found that activation energies range from 22.56 to 418.80 kJmol −1 , are influenced by plastic types, heating rate, experimental conditions, and kinetic methods. Reaction orders range between 0.5 and 2.5, with first-order kinetics often provide the best mechanistic fit for both single plastic and mixed plastic waste fractions. The exponential factor (A) varies widely, typically falling between 10 3 and 10 29 S −1 , indicating strong kinetic compensation effects and the complex, multi-step nature of plastic degradation. It can be noted that the challenges remain in methodological consistency and kinetic triplets, scalability of data, catalyst stability, and control over oil quality. The novelty and future directions coming out of this review include integrating kinetics with transport phenomena, exploring co-pyrolysis synergies, and developing advanced catalytic materials. Reactor configuration and scale-up pose bottlenecks due to discrepancies between laboratory and industrial conditions. Catalytic pyrolysis faces obstacles like catalyst deactivation and coke formation. Product distribution and selectivity, especially oil quality and yield control, are poorly optimised in the literature. These limitations highlight the complexity of plastic waste pyrolysis kinetics, emphasizing the need for improved models that integrate transport phenomena, feedstock variability, and catalyst behaviour to enhance process reliability and scalability.