Micol Federica Tresoldi, Senior Research Statistician, Dow Chemical
Xinjie Tong, Senior Research Statistician, Dow Chemical
This case study investigates chemical mixtures to achieve optimal properties using design of experiment (DOE) data. The formulation space consists of four input variables: Chemical A Type, Chemical B Type, Chemical C Type, and Chemical D Content. The first two variables represent different compositions for making Chemical A and B, respectively, and as such, can be coded both as categorical factors, as well as continuous mixture variables.
We created the DOE treating them as categorical due to the experimental constraints. However, at the data analysis stage, even after considering thousands of simulated hypothetical formulations, none of them was predicted to meet the desired properties. At that point, to be able to identify promising subregions, we needed to overcome the discreteness of the space. So, we recoded those two factors as continuous and mixture variables, derived the equivalent regression model, and reran the simulations. Indeed, under certain assumptions, this coding strategy enables one to interpolate and consider missing compositions not present in the original DOE.
In this presentation, we demonstrate how to use JMP Pro 16 Profiler Simulation feature with Graph Builder to achieve an extensive and insightful exploration of the formulation space applicable to diverse fields.
