Integration of Scheduling and Dynamic Optimization: Computational Strategies and Industrial Applications
2014-07-01T00:00:00Z (GMT) by
This thesis study focuses on the development of model-based optimization strategies for the integration of process scheduling and dynamic optimization, and applications of the integrated approaches to industrial polymerization processes. The integrated decision making approaches seek to explore the synergy between production schedule design and process unit control to improve process performance. The integration problem has received much attention from both the academia and industry since the past decade. For scheduling, we adopt two formulation approaches based on the state equipment network and resource task network, respectively. For dynamic optimization, we rely on the simultaneous collocation strategy to discretize the differential-algebraic equations. Two integrated formulations are proposed that result in mixed discrete/dynamic models, and solution methods based on decomposition approaches are addressed. A class of ring-opening polymerization processes are used for our industrial case studies. We develop rigorous dynamic reactor models for both semi-batch homopolymerization and copolymerization operations. The reactor models are based on first-principles such as mass and heat balances, reaction kinetics and vapor-liquid equilibria. We derive reactor models with both the population balance method and method of moments. The obtained reactor models are validated using historical plant data. Polymerization recipes are optimized with dynamic optimization algorithms to reduce polymerization times by modifying operating conditions such as the reactor temperature and monomer feed rates over time. Next, we study scheduling methods that involve multiple process units and products. The resource task network scheduling model is reformulated to the state space form that offers a good platform for incorporating dynamic models. Lastly for the integration study, we investigate a process with two parallel polymerization reactors and downstream storage and purification units. The dynamic behaviors of the two reactors are coupled through shared cooling resources. We formulate the integration problem by combining the state space resource task network model with the moment reactor model. The case study results indicate promising improvements of process performances by applying dynamic optimization and scheduling optimization separately, and more importantly, the integration of the two.