New mass-transfer model for simulating industrial nylon-6 production trains

Kevin C. Seavey, Y. A. Liu, Bruce Lucas, Neeraj P. Khare, Tom Lee, Jason Pettrey, Thomas N. Williams, John Mattson, Earl Schoenborn, Charles Larkin, Harry Hu, Chau Chyun Chen

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

We present a new mass-transfer model for simulating industrial nylon-6 polymerization trains. In this model, both diffusion and boiling (bubble nucleation) contribute to mass transfer. With this model, we are able to simulate widely differing production technologies using identical mass-transfer parameters, along with identical models for fundamentals such as phase equilibrium, physical properties, and polymerization kinetics. To illustrate, we simulate the direct-melt process and the bubble-gas kettle process. The direct-melt process builds up the polymer molecular weight and removes nearly all residual caprolactam monomer by employing, under vacuum, a wiped-wall evaporator and a rotating-disk finisher. The bubble-gas kettle process, on the other hand, injects inert gas bubbles through the melt at nearly atmospheric pressure to build up the polymer molecular weight but does not significantly reduce the caprolactam level because the diffusion coefficient is so low. We validate our process models using commercial train performance data at different production rates, including the first known validation of a dynamic rate-change simulation for industrial polycondensation trains. Model predictions quantitatively agree with product quality data such as formic acid viscosity (FAV), polymer end-group concentration, and water extractables. The prediction errors for the direct-melt process are 2.81%, -3.13%, and -3.06% for FAV, water extractables, and amine end groups, respectively. For the bubble-gas kettle process, the prediction errors are -17.2%, -17.0%, and -7.49% for extrusion FAV, washed-and-dried FAV, and water extractables, respectively. These errors are much lower than the ca. -50% errors obtained by existing advanced models for devolatilization.

Original languageEnglish
Pages (from-to)5063-5076
Number of pages14
JournalIndustrial and Engineering Chemistry Research
Volume43
Issue number17
DOIs
StatePublished - Aug 16 2004

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