@Victor_G,

Thank you for the response. It is very helpful. I have responses to your questions below and an additional question at the end.

What is your goal regarding the use of this design (screening, exploration, prediction, ...) ?

I would say we are in the exploration phase. We have done some past screening work and are interested in learning more about the impact of a couple of new additives at various concentrations.

What is your knowledge about the system ? What is the assumed model for this design ? Are there any other factors you may be interested to include and study ?

We have decent knowledge of the product, but are evaluating it for a different use with new additives. We have decided to hold other factors steady at this point.

Can X1 and X2 vary independently if they relate to the same main component (these factors look like covariates) ? Why are X1 and X2 categorical if they are chemical and physical characteristics/properties ? They could be set/transformed to continuous covariates, see the presentation Coding with Continuous and Mixture Variables to Explore More of the Input Space (2022-US-45MP-1103) )?

X1 and X2 can vary independently. They are categorical because in practice, that can't be controlled with the precision necessary to use continuous variables. Rather than getting a product with a specific value for chemistry, we are dealing with ranges that we then assign categorical values (e.g., low, med, high).

I'm inclined to add a 0 to X4 and move forward.

I am curious about the tradeoffs between using a discrete numeric vs continuous variable. X4 could be either. But, it seems to me that I could get more information about the effect of concentration by setting more discrete values (e.g., 0, 0.1, 0.2, 0.3) than it is possible to set with continuous variable and a mid-point (0, 0.15, 0.3). I'm expecting a linear response, but am not certain that it will be. Any thoughts on this?