In Situ Raman Spectroscopy Real-Time Monitoring of a Polyester Polymerization Process for Subsequent Process Optimization and Control

Here, in situ Raman spectroscopy is used to develop a method for determining in real time the percentage of esterification denoted as Ester%, a key quality index of polymerization processes in polyester industries. Specifically, Raman spectra of the polymerization (esterification and polyesterification) of terephthalic acid (PTA) and 1,4-butanediol (BDO) to obtain poly(butylene terephthalate) (PBT) are monitored as a function of reaction time. They are processed through a background subtraction algorithm to yield Raman spectra, which allows for the identification and quantification of Raman bands corresponding to the ester and carboxylic groups. The Ester% is calculated by the ratio between the ester and carboxylic groups in terms of the characteristic peak intensities or areas. The ratio based on the Raman peak areas yields more satisfactory results, namely, the calculated values of the Ester% are less noisy and agree better with those obtained by titration. The established in situ Raman spectroscopy method allows for real-time monitoring and quantification of the Ester% during the polymerization process. It will be adopted for process optimization and control at a pilot scale and ultimately at an industrial production scale

Kinetic Parameter Estimation for Linear Low-Density Polyethylene Gas-Phase Process from Molecular Weight Distribution and Short-Chain Branching Distribution Measurements

Kinetic parameter estimation for a complex copolymerization process has always been a challenge in the modeling procedure. This study aims at the kinetic parameter estimation for a linear low-density polyethylene (LLDPE) gas-phase process from molecular weight distribution (MWD) and short-chain branching distribution (SCBD) measurements. First, experimental MWD and SCBD are simultaneously deconvoluted to obtain intermediate model parameters as output variables. Then, appropriate nominal values of the kinetic parameters are provided by solving an optimization problem. This procedure plays a significant role in narrowing down the range of the nominal values. The determined output variables and nominal parameter values are used to form a sensitivity matrix for parameter estimability analysis. After that, a new parameter ranking strategy is proposed using hierarchical clustering. Based on the determined nominal values, the ranking results obtained using the proposed strategy in a robustness test are more robust than those obtained under random nominal values. Lastly, the hierarchical clustering is combined with Wu’s mean squared error-based method to determine an estimable parameter subset, during which the selected kinetic parameters are estimated by matching the intermediate model parameters. The gas-phase copolymerization process model based on the estimated parameter values is further validated with different MWD and SCBD measurements. Model predictions show good agreement with experimental data