(40e) Reactor Scale-up: Looking Forward By Looking Backward

Various dedicated monographs and reviews of reactor scale up have appeared over the last fifty years of which the following citations are but a small sample (Donati, 1997; Duduković, 2014; Fox, 2006; Gianetto, 1986; Johnstone, 1957; Marchisio, 2006; Meroni, 2021; Nauman, 2008; Rüdisüli, 2012; Thoenes, 2013). A survey of the presentations given in the various CRE Division sessions shows that they have been heavily dominated by academic researchers whose works were mainly focused on either development or application of reactor models of various degrees of complexity, experimental studies on relatively small-scale systems, or reactor modeling combined with an attempt at experimental validation. Conversely, contributions on reactor scale-up from reaction engineers from industry are very limited in number. Given that this latter group has developed a wealth of know-how and experience in scale-up of commercial processes that spans many decades, or even nearly a century for some mature technologies, the knowledge base associated with this aspect is usually maintained as proprietary information and hence is not shared with their academic counterparts or competitors for obvious business reasons. While the evolution of fundamental reactor models and the development of specialized experimental tools for quantification of reaction kinetics, transport-kinetic interactions, hydrodynamics, and fluid flow patterns by academia has improved predictive models, robust approaches for reactor design and scale-up from the laboratory to commercial-scale reactors are still lagging (Duduković, 2014). Hence, in spite of advances in kinetics, catalysis and reaction engineering, methods and models available for reactor scale-up and design from laboratory-scale to commercial units at realistic process conditions have not taken notable steps forward and generally lack traction in technology advancement (Duduković, 2009) .Linkages will be revisited between key research needs in the original four cross-cutting areas, namely, (1) experimental tools; (2) modeling and property estimation; (3) sensors; and (4) systems integration.