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Optimisation of Emulsion Copolymerization of Styrene and MMA in Batch and Semi-batch Reactors
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In this work, emulsion copolymerization of Styrene and Methyl Methacrylate (MMA) is studied in batch and semi-batch process. A number of different dynamic optimization problems are formulated and solved for batch process; semi-batch process with and without pre-batch time. In the batch process, the overall monomer conversion (Xn) is maximized for different desired number average molecular weights (Mn). In semi-batch process without pre-batch time Mn is maximized. In semi-batch process with fixed pre-batch time at the beginning of the reaction process, Mn is maximized for different initial initiator concentration (CI0). A process model involving the equations for mass and energy balance, population balance describing the particle evolution in the reactor and diffusion controlled kinetics is incorporated into the optimization framework. The dynamic optimization problems are converted into nonlinear programming problem using the CVP techniques which are solved using efficient SQP (Successive Quadratic Programming) method available within the gPROMS (general PROcess Modelling System) software.
Walter de Gruyter GmbH
Title: Optimisation of Emulsion Copolymerization of Styrene and MMA in Batch and Semi-batch Reactors
Description:
In this work, emulsion copolymerization of Styrene and Methyl Methacrylate (MMA) is studied in batch and semi-batch process.
A number of different dynamic optimization problems are formulated and solved for batch process; semi-batch process with and without pre-batch time.
In the batch process, the overall monomer conversion (Xn) is maximized for different desired number average molecular weights (Mn).
In semi-batch process without pre-batch time Mn is maximized.
In semi-batch process with fixed pre-batch time at the beginning of the reaction process, Mn is maximized for different initial initiator concentration (CI0).
A process model involving the equations for mass and energy balance, population balance describing the particle evolution in the reactor and diffusion controlled kinetics is incorporated into the optimization framework.
The dynamic optimization problems are converted into nonlinear programming problem using the CVP techniques which are solved using efficient SQP (Successive Quadratic Programming) method available within the gPROMS (general PROcess Modelling System) software.
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