A viscometer-reactor assembly has been developed and bulk polymerization of methyl methacrylate carried out in it. The viscosity of the polymerizing mixture has been measured continuously at two different temperatures (isothermal) and initiator loadings. Samples of the reaction mass have also been taken out at several different times and analyzed for monomer conversion and the weight-average molecular weight. The data obtained have been curve-fitted using the Martin equation with K M treated as an empirical parameter. The use of experimental data on the viscosity of the reaction mass during polymerization as a state estimator (software sensor) has been demonstrated. The viscometer-reactor assembly can be used to obtain viscosity under nonisothermal conditions as well, and also to study on-line optimizing control of bulk polymerizations using appropriate on-line model-based state variable estimation and optimization software codes.

ABSTRACT:A model-predictive software sensor was developed for on-line estimation of monomer conversion and average molecular weight during bulk polymerization of systems exhibiting a gel effect. The viscosity and temperature of the reaction mass are the measured secondary variables, which when used with the model allow the state of the system to be estimated. The viscometer-reactor assembly was modified so as to measure the viscosity of the reaction mass during bulk polymerization of methyl methacrylate (MMA) at temperatures higher than those reported in our earlier work (Mankar, R. B.; Saraf, D. N.; Gupta, S. K. Ind Eng Chem Res 1998, 37, 2436).The viscosity data were curve-fitted using the modified Martin equation. Optimal temperature histories were then computed off-line, using a genetic algorithm, and implemented on the viscometer-reactor assembly in which the bulk MMA polymerization was carried out. The fact that the model tuned with the data obtained under the isothermal reactor operation can be used to predict the viscosity for nonisothermal (optimal or otherwise) reactor conditions without further tuning establishes the efficacy of the software sensor. This study can now be extended to investigate, experimentally, the on-line optimizing control of bulk MMA polymerizations.

An experimental unit has been assembled to carry out on-line optimizing control of the bulk polymerization of methyl methacrylate (MMA). A rheometer-reactor assembly is used. Temperature and viscosity measurements are used to describe the state of the system. The polymerization is carried out under an off-line computed optimal temperature history, T op (t). A planned disturbance (heating system failure) is introduced at time t 1 . This disturbance leads to a fall in the temperature of the reaction mass. A new optimal temperature history, T reop (t), is re-computed on-line and is implemented on the reaction mass at time t 2 , when the heating is resumed. This procedure helps 'save the batch'. A genetic algorithm is used to compute this reoptimized temperature history in a short period of ϳ2 min of real time. The feasibility of the on-line optimizing control scheme has been demonstrated experimentally. Replicable results for the viscosity history, (t), of the polymerizing mass under several nonisothermal conditions have been obtained. These experimental results are quite trustworthy, even though the model predictions are only in approximate agreement with them, perhaps because of the extreme sensitivity of results to the values of the model parameters.

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