Estimation of the molecular weight distribution in batch polymerization

Abstract

A filtering technique is proposed for on-line estimation of the temperature, monomer conversion, initiator conversion, and the entire molecular weight distribution in a batch methyl methacrylate polymerization reactor. The technique uses a detailed polymerization model combined with on-line measurements of conversion, temperature, and the molecular weight distribution, taken at different discrete time intervals. The polymerization model includes a chain-length-dependent termination rate constant which allows the prediction of the molecular weight distribution for common free-radical polymerization conditions. Comparisons between modeling and experimental results show that the polymerization model gives good predictions of the monomer conversion and the molecular weight distribution in the polymerization system. The performance of the estimation scheme is tested for cases of strong gel effect conditions leading to a bimodal molecular weight distribution, and poor initial conditions. Finally, off-line experimental data are used to test the algorithm under actual reactor operating conditions.

A multi-thread parallel computation method for dynamic simulation of molecular weight distribution of multisite polymerization

“…One key property that directly influences the end-use quality of polymers is the molecular weight distribution (MWD), which characterizes the molecular features of the product. For example, MWD affects the processing characteristics of the polymer through the melting point and the flow properties of the melted polymer; MWD determines the mechanical properties of the processed product, such as strength and impact resistance (Ellis et al, 1988). The calculation for MWD, however, remains challenging due to its large-scale features.…”

Inferential Estimation of Polymer Melt Index Using Sequentially Trained Bootstrap Aggregated Neural Networks

“…A number of researchers have attempted to find this relationship by studying the polymerization mechanism and using some state estimation techniques, such as the extended Kalman filter, to obtain inferential estimates of polymer quality variables [1][2][3][4]. These approaches, however, require a deep understanding of the polymerization process, and the development of a mechanistic model generally demands considerable effort.…”

Itô–Volterra Optimal State Estimation With Continuous, Multirate, Randomly Sampled, and Delayed Measurements

“…For instance, Ellis et al [1] used a multirate EKF to estimate unobserved process states using frequently available measurements of temperature and density, and infrequent, delayed measurements of an average molecular weight. Shah et al [2] implemented a multirate formulation of the iterated EKF on a bioreactor.…”

“…This solution is only convenient for small and constant delays, since the state vector is expanded substantially when delays are long, and is not applicable when delays vary. Another standard method [1], [13] is to recalculate the state estimate from the time when a delayed measurement was acquired to the present time.…”