Computational modeling toward full chain of polypropylene production: From molecular to industrial scale

Since polypropylene was synthesized in 1954, tremendous breakthroughs have been achieved in transferring polypropylene from a discovery in the laboratory to an indispensable industrial product. One of the most difficult issue in polypropylene production is the precise control of the synthesis process to tailor the microstructure and the end-use properties, which needs deep understanding of the quantitative relationships among process, polymer structures and properties. However, semi-empirical correlations and experimental measurements are not able to capture the complex multi-scale characteristics of propylene polymerization process. In recent years, mathematical models have been intensively developed to quantitatively link the microstructure of polymer to final macroscopic properties at multi-scales. This review provides an overview of progress in computational modeling of polypropylene production from the perspectives of science and engineering aspects covering synthesis, structure–property relationship, reactor design, processing, composites, and applications. The developed mathematical models at various scales from molecular scale, particle scale and reactor scale toward plant scale throughout the full chain of production process are elaborated. The coupling strategies of models among different scales will be presented. In addition, model-based determination of quantitative relationships among process, apparatus, structure, and property for polypropylene are fully discussed including the recently developed emerging numerical approaches such as machine learning assisted modeling.