(397a) Reactor modeling and recipe optimization of polyether polyol processes: Polypropylene glycol

Polyether polyols are polymers that find application in a wide range of industries for the production of flexible foams, high resiliency molded foams, and viscoelastic foams, as well as in CASE (Coatings, Adhesives, Sealants, and Elastomers) applications. They are also known as polyglycols, whose uses include surfactants, dispersants, and lubricants, to name a few. A significant percentage of the world capacity in both propylene and ethylene oxide goes into these polymers, who find uses in products as advanced as self-healing fuel tanks for battlefield fuel transportation. With such widespread use, there is an increasing demand for these products using a manufacturing technology that is already mature and well established. The need for increased efficiency at low capital investment is significant.

This thesis study focuses on model-based optimization strategies to improve process performance of existing assets. Particularly, we develop dynamic optimization and optimal scheduling methods, and their integration, for industrial base-catalyzed alkoxylation processes. We develop rigorous dynamic reactor models for both 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 the dynamic optimization algorithm, using the simultaneous collocation strategy and nonlinear programming techniques. Polymerization times are reduced by optimizing 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 and solve it with the generalized Benders decomposition scheme. 

This PhD project falls under the University Partnership Initiative between The Dow Chemical Company and Carnegie Mellon University. The technical presentation by Nie entitled "Reactor modeling and recipe optimization of polyether polyol processes: Polypropylene glycol" has received the Best Presentation award in the session "Modeling and Control of Polymer Processes" of the 2012 AICHE Annual Meeting. The implementation effort was recognized by Frost and Sullivan. The project team won the 2014 Manufacturing Leadership Award in the category of Engineering & Production Technology Leadership. The team has also received multiple awards internal to Dow. The recipe optimization effort continues to thrive in Dow, and the scheduling optimization technology implementation is under development.