Application of Spatial Statistics to Quantify Mixing and the Potential for Reaction
Many chemical reactions can produce unwanted byproducts which require additional purification steps and lead to unwanted waste. Additional purification steps consume large amounts of energy, and waste products can have a significant environmental impact. The chemistry can be modified so that the desired products are favored over the unwanted byproducts, and the mixing can be intensified so that molecules are more rapidly and intimately mixed. Both of these approaches will reduce byproducts. In this project, the equations describing mixing and reaction rates are solved for various mixing rates and sets of reactions to identify the important variables. Then industrial data sets are analysed using spatial statistics to link the mixing conditions in the fundamental models to realistic data sets for an industrial reactor. This work has implications for industrial flares, pharmaceutical and fine chemical reactors, reaction injection molding, and petrochemical reactors.