Mathematical models for polyether production from 1,3-propanediol

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Cerenol® (poly(trimethylene ether) glycol, PO3G) is a type of renewably-sourced polyether that has been commercialized by DuPont. This polymer is used in cosmetics, breathable membranes, biodegradable lubricants, architectural coatings, adhesives and elastomeric fibres. It is made by condensation polymerization of the monomer 1,3-propanediol (PDO), which is produced using a biomass fermentation process. This new type of commercial polymer is renewable, highly bio-degradable and environmentally sustainable. The objective of the proposed research is to develop and validate mathematical models (i.e., sets of equations within computer programs) for DuPont that can be used to predict the effects of catalyst and temperature on the production rate of PO3G and the quality of the final product. These models will account for important chemical reactions and will be able to accurately predict changes in the degree of polymerization and in concentrations of hydroxyl end groups, unsaturated end groups and other species that influence the quality of the Cerenol®. In addition, the influence of evaporation of small molecules (e.g., water, monomer, propanal) and linear and cyclic oligomers from polymer melt into the nitrogen bubbles will be investigated. Information from the literature and experimental data will be used to estimate selected kinetic and mass-transfer parameters so that model predictions will be reliable. The main benefit of this research to DuPont Canada is that they will obtain a mathematical model that scientists and engineers can use to predict the behaviour of PO3G production processes using different operating conditions. In addition, researchers at DuPont and Queen's will obtain enhanced knowledge for understanding condensation polymerization using bio-based monomers, which will be useful for designing advanced batch/continuous reactors and for optimizing commercial production processes. Two graduate students will obtain experience collaborating with DuPont scientists and will develop modeling and statistical skills that will benefit their future employers.