A comprehensive model has been developed to handle the reactions in polymers undergoing polycondensation reactions in the solid state. The polymer crystalline fraction is modeled as containing only repeat units, thus concentrating end groups and condensate in the amorphous fraction. In addition, by using a general framework for the equations, many previously neglected effects are included; for example, variable crystallinity and gas phase mass transfer effects. This model is compared to PET and nylon reaction data with good results.

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ABSTRACT:A model for handling the effect of water on nylon equilibria and kinetics has been developed and tested. Observed variations in the equilibria and kinetics are attributed to microscopic phenomena, not an empirical expression. The equilibrium variation is modeled based on two states of water in the nylon melt, and the kinetic variation is modeled as due to a changing dielectric constant. All reactions are assumed to be acid catalyzed. Results are good at water to polymer ratios ranging from 0.01 to 30 and from temperatures from 200 to 280ЊC. The model framework also allowed the calculation of interchange rates and ring oligomer concentrations. The model is compared with published data and other proposed models.

This work reports on using microwave energy to increase the rate of solidstate polymerization for PET and nylon 66. Theoretical analysis and experimental evidence show that the increase in polymerization rate is not due to an increase in the bulk temperature. Instead, the effect is consistent with directed heating of the condensate leading to enhanced diffusion rates.

ABSTRACT:The relative merits of the moving packed bed, fluidized bed, and stirred bed reactors for solid-state polycondensation are discussed along with methods for improving these designs. A general model to describe continuous solid-state polymerization reactors is then developed and illustrated by a case study of a moving packed bed reactor showing the relative importance of operating variables. The model also predicts the dynamic behavior in response to several process inputs.