(432h) A Coupled Stochastic-Deterministic Model for the Simulation of a Tubular Pilot-Scale Copolymerization Reactor | AIChE

(432h) A Coupled Stochastic-Deterministic Model for the Simulation of a Tubular Pilot-Scale Copolymerization Reactor

Authors 

Meimaroglou, D. - Presenter, University of Lorraine - CNRS
Falk, L., University of Lorraine - CNRS
Hoppe, S., University of Lorraine - CNRS
Durand, A., University of Lorraine - CNRS
Chevrel, M. C., University of Lorraine - CNRS
Wilson, J., SOLVAY



In the present work, a modular integrated modeling approach is presented for the simulation of the free-radical co-polymerization of Acrylic Acid (AA) with a co-monomer (M2)1, in a flexible tubular pilot-plant reactor equipped with static mixer elements, for the production of water soluble copolymer products. In this respect, a combination of the well-established method of double moments (DMoM) with a stochastic Monte Carlo (MC) kinetic simulation algorithm is implemented in order to take full advantage of the different capabilities of the two methods. More precisely, the developed reactor simulator combines the benefits of speed and accuracy, provided by the DMoM, with the possibility to obtain detailed distributed polymer property characteristics by the kinetic MC module.

A direct comparison of the obtained simulation results, in terms of the average copolymer molecular weights (Mn and Mw), molecular weight distributions (MWD), monomer conversion and reactor temperature profiles along the reactor length, with respective experimental measurements validate the accuracy and applicability of the proposed integrated simulation approach. The simulator is further utilized for the prediction of additional copolymer properties of interest (i.e., copolymer composition distribution, sequence length distribution, etc.) as well as for the exploitation of the domain of process operating conditions (i.e., reactor temperature, solid content, initiator concentration) with respect to their impact on the final copolymer characteristics, in terms of a process intensification study.

The research leading to these results has received funding from the European Community‘s 7th Framework Programme under grant agreement n° 228867

 1Undisclosed information for reasons of confidentiality.

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