(356a) Parameter Identification and Dynamic Optimization of Seeded Suspension Polymerization Process

Seeded suspension polymerization is one of the advanced technologies used to produce large size，typically in the range 5~1000um, composite polymer particles. The enhanced chemical and physical properties enable use of the resulting particles for many high-performance, lightweight applications. The semi-batch operation is essential for controlling the seed particle growth and polymer properties. Various composite polymer and particle morphologies can be obtained through this approach. Control of product quality and improvement of process productivity based on experimental adjustment is challenging, as the relationship between process operation and polymer properties has remained largely unexplored. Key challenges include developing a capable mathematical model and obtaining reliable model parameters for quality control and process optimization.

In this work, a generalized reaction diffusion single particle model is developed, with consideration of three key features of the seeded suspension polymerization -- particle growth mechanism, intra-particle heterogeneity and polymerization kinetics. The moving boundary partial differential equation is solved by dimensionless coordinate discretization. Particular emphasis is put on model parameter identification. Since in free radical polymerization, literature kinetic values often have very high variability, and available analytical measurements specifically for polymer composites are also limited, a systematic approach is proposed to obtain reliable estimated parameters. The approach applies parameter ranking with successive orthogonalization of the sensitivity matrix, and hybrids with simultaneous parameter estimation strategy. Parameter influence on the response and linear dependency between parameters could be taken into account with little additional effort.

The proposed model is carried out in conjunction with pilot plant experimental studies on polymerization rate, average molecular weight and particle size development. Consistent agreements were achieved for different grades of products. Finally, optimal process control based on this mathematical model is explored. An improved monomer feeding policy is obtained and validated by experiments. This comprehensive model framework shows a promising approach to study seeded suspension polymerization.