(433e) A Molecular Model for Reactive Fouling in Steam-Cracker Separation Trains

Polymeric fouling is a ubiquitous problem in the separation train downstream of steam crackers used to pyrolyze ethane and other saturated feeds into alkenes (olefins). Macromolecules form and deposit in downstream equipment, forming films on those surfaces over time, resulting in sub-optimal operation and eventually forcing plant shut down. A variety of empirical approaches have been developed and used, with mixed success, to ameliorate this problem. However, there is limited fundamental understanding on the origin of fouling. Because the root cause of fouling is often not known, it is difficult to determine the best mitigation strategy.

In this work, we lay the foundations for understanding polymer fouling with a multiscale multiphase model for the deposit growth process. First the composition at different locations in the separation train is computed using ASPEN. Then a reaction network suitable for modeling the kinetics occurring in that chemical mixture is constructed using the Reaction Mechanism Generator (RMG), a software package that constructs detailed chemical reaction mechanisms by a rate-based model enlarging algorithm. The rates of key reactions and the thermochemistry of critical reaction intermediates are computed using high level quantum chemistry methods; in the present case these reactions include Diels-Alder, ene, and free radical reactions. This extensive, detailed list of elementary reactions is embedded within a multiphase and multiscale deposition model, represented as a set of equations consisting of foundational balances and constitutive relations to describe mesoscale-continuum dynamics. Simulations are conducted with Julia, a high-performance scientific computing language. Computed sensitivities of the simulated deposit growth rates to the model parameters are used to direct iterative refinement of the model.

The model presented here focuses on diene-driven deposit growth at specific reaction conditions. But the general modeling framework presented here, which combines classical analyses of phase equilibria, transport, and material properties with automated reaction mechanism generation and quantum chemistry calculations of reaction kinetics, is expected to be useful in understanding deposit growth in a variety of other systems as well.