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This study considers the development of optimization models for grade transition of polyethylene solution polymerization processes. A detailed mathematical model is developed to capture the dynamics of the solution polymerization process. This includes time delay models for vapor and liquid recycle streams as well as a reduced, yet accurate, vapor-liquid equilibrium (VLE) model derived from rigorous VLE calculations. Simultaneous dynamic optimization approach is applied to solve the optimization problem to reduce off-spec production time and transition time. Two optimization formulations, single stage and multistage, are developed to deal with single-value target and specification bands of product properties, respectively. The results show significant reductions in grade transition time and off-spec production time. In addition, the multistage formulation designed for problems with specification bands outperforms its single stage counterpart. It minimizes transition time and off-spec production directly, and leads to higher performance control profiles.

Introduction This study focuses on the development and implementation of an Exceptional Events Management (EEM) framework that detects, diagnoses, and mitigates exceptional events inherent to particulate processes that are prevalent in the pharmaceutical industry. Methods The EEM framework combines several methods, including signed directed graph (SDG), qualitative trend analysis (QTA), and fast Fourier transform analysis. Results We discuss the efficacy of the EEM framework in detecting and diagnosing abnormal events, and demonstrate its application on roller compaction. We demonstrate that various commonly occurring exceptional events such as "no powder entering the roll region" (which includes "jamming in the nip region"), "caking of powder on rolls" and "varying moisture in powder" can be detected and diagnosed using the EEM framework. In addition, we show that the SDG-QTA approach is useful in determining the type and location of sensors that would enable better monitoring thereby improving the detection and diagnostic capabilities of our EEM framework.Conclusion The developed EEM framework shows potential for facilitating the pharmaceutical transition from batch to continuous manufacturing.