Model-Based Control and Optimization of Grade Transitions in Polyethylene Solution Polymerization Processes
This thesis study focuses on the development of model-based control and optimization for optimal grade transitions in polyethylene solution polymerization processes. To meet the operational need of this particular process and to reduce transition time and off-grade production for economic benefit, four major topics are taken into account: 1) model development, 2) optimization formulations and solution strategies, 3) handling uncertainties, and 4) online implementation. These four parts cover two layers, the real time optimization layer and the advanced control layer, in the decision-making hierarchy of chemical processes. Both of them require detailed mathematical models that are representative of the process and efficient dynamic optimization strategies. First, a detailed mathematical model is developed to capture the dynamic behavior of the 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. Next, 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 reduction in grade transition time and off-spec production. However, the performance can deteriorate in the presence of uncertainties, disturbances and model mismatch, which calls for robust optimization strategies. In our work, a flowchart is proposed and back-off constraints calculated from Monte Carlo simulations are incorporated in the original optimization problemto generate optimal control policies that can be applied at different uncertainty levels. As an extension of this work, nonlinear model predictive control and state estimation are then considered. An online implementation framework is built up for grade transitions in such processes and can be further extended to other similar processes. For dynamic optimization, simultaneous collocation method is applied to discretize the differential-algebraic equations, and the resulting nonlinear programming (NLP) problems are solved using NLP solvers. Because of the characteristics of the problem, singular control problems are considered and the influence of regularization is discussed for both offline dynamic optimization and optimization under uncertainty.