In an earlier R&D study I sucessfully used a 4 factor mixture DOE (3 mixture factors and one 3-level categorical "Isomer" effect) to find optimal settings for a chemical formulation with three quality attributes Y1, Y2 and Y3.
I used the case in a DOE course and used simulation formulas to compute the responses Y123. Good models are obtained and optimization was straightforward and OK, see "ANALYSIS MIXTURE DOE CHEMICAL FORMULATION" in attachment.
Starting from a 12 run mixture Kowalski model DOE I am struggling with Bayesian optimization (see annex "BAYESIAN OPTIMIZATION") This keeps failing because of a very unreliable Y2 model? Why does Bayesian recommends "Max Desirability" with this very unreliable Y2 model? Starting from other screening & space filling DOE's I keep getting the same unreliable Y2 model.
What is the problem? How to solve?
