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A dynamic model is proposed for the photopolymerization of 1,6‐hexanediol diacrylate (HDDA) with the bifunctional initiator bis‐acylphosphine oxide (BAPO) in the presence of oxygen. The model tracks time‐varying concentrations of monomer, oxygen, and different radical end groups using ordinary differential equations. An analytical expression is derived for the mass‐transfer of oxygen. Oxygen‐related parameters are estimated using real‐time Fourier‐transform infrared reflection (FTIR) vinyl conversion data, which were collected during polymerization of HDDA films with different thicknesses, initiator concentrations, and light intensities. The resulting model and parameter estimates provide good predictions for experiments involving thin films up to 12 μm, with BAPO levels ranging from 1 to 4 wt.% and relatively low light intensities (200–1000 W/m2). [ABSTRACT FROM AUTHOR]

Sequential model‐based design of experiments (MDBOE) accounts for information from previous experiments when selecting conditions for new experiments. In the current study, sequential MBDOE is used to select operating conditions for experiments in a batch‐reactor that produces bio‐based polytrimethylene ether glycol (PO3G). These Bayesian A‐optimal experiments are designed to obtain improved estimates of 70 fundamental‐model parameters, while accounting for industrial data from eight previous runs. Settings are selected for three decision variables: reactor temperature, initial catalyst level, and initial water concentration. If only one new experiment is conducted, it should be run at high temperature, with relatively high concentrations of catalyst and initial water. When two new runs are conducted, one should use an intermediate catalyst concentration. The effectiveness of the proposed MBDOE approach is tested using Monte‐Carlo simulations, revealing that the selected experiments are superior compared to experiments selected randomly from corners of the permissible design space. [ABSTRACT FROM AUTHOR]

A comprehensive dataset containing 1779 measured values, collected from seven experimental runs, is used for parameter estimation in a model for batch polycondensation of biobased 1,3‐propanediol. The data are from dynamic experiments conducted between 160 and 180 °C using a super‐acid catalyst with concentrations between 0.1 and 0.25 wt%. The model accounts for generation, consumption, and evaporation of linear and cyclic oligomers, the inhibitory influence of water on the rate of polycondensation, and the dynamic behavior of an overhead condenser. In total, the model contains 70 kinetic, transport, and thermodynamic parameters. A mean‐squared‐error criterion reveals that the influence of temperature on mass‐transfer coefficients is significant. The model provides a good fit to the data and also gives reliable predictions of validation data that are not used in parameter estimation. Typical deviations between model predictions and data are within 1.75% of average measured values for linear and cyclic oligomers. [ABSTRACT FROM AUTHOR]

A comprehensive model is developed for polycondensation of 1,3‐propanediol in a batch reactor. The model accounts for formation, consumption, and evaporation of cyclic oligomers. Measurements of species and end‐group concentrations in the reactor and collected condensate are used to estimate 36 kinetic and mass‐transfer parameters. The model and parameter estimates provide good fits to cyclic‐oligomer data obtained using four different levels of super‐acid catalyst. Predictions from the model reveal that end‐biting reactions are important for formation of cyclic oligomers during early stages of each batch‐reactor run, whereas back‐biting reactions become important at longer reaction times. Simulation results reveal that ring‐opening polyaddition reactions contribute significantly to growth of polymer chains at long reaction times. [ABSTRACT FROM AUTHOR]