Abstract

Abstract Pyrolysis is one of the most important processes in the chemical industry not only for producing many of today's chemical building blocks via steam cracking. Pyrolysis also plays a key role in biomass conversion processes, chemical recycling of plastic waste, refinery processes, such as visbreaking and even in combustion. Essential to further improving, this process is understanding the reaction kinetics of these complex feeds, representative model compounds, and certain building blocks (e.g., lignin, cellulose, and hemicellulose). For solid feeds numerous TGA studies have provided valuable information on the overall kinetics. The presence of heat and mass transfer limitations implies that it is not obvious to scale the results. Nevertheless, mass loss-based apparent kinetic approaches are still being used to compare pyrolysis of different feedstocks due to its simplicity and ease in calculations. More multiplexed experimental studies on model compounds are needed to improve our current understanding. When aiming to construct an intrinsic chemical kinetic model, able to describe a system under diverse conditions, researchers have been relying on the automatic generation of these models for several reasons. One of the main challenges that still needs to be overcome is how to deal with data scarcity. The calculation power of supercomputers is certainly a strong driver for automatic kinetic model generation, but calculating all the rate coefficients using high level computational chemistry methods is still too time consuming at present. Several model assumptions for constructing the kinetic models can be used for reducing the stiffness of the whole set of reactor model equations or just simplify the complexity of the kinetic model. This has made that detailed kinetic models are widely applied both in industry and in the academic World.

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