Sorry, but I do not think there is quite enough information here to give very specific advice, and I may not completely understand your objective. So let me comment more generally.
My first question would be: Are you trying to understand the underlying causal mechanisms, or are you primarily trying to pick a winning material and set of operating conditions? Those can lead to rather different experimental strategies.
As I understand your description, you have one categorical factor, Material, with four levels, plus three continuous factors: process time, pretreatment time, and reagent addition. I am not quite sure what you mean by reagent addition. Is this the amount or concentration of reagent, making it a continuous factor, or simply reagent added/not added, which would make it categorical?
I would also question whether I would start with a response surface design. IMHO, RSM is most useful after you have developed a reasonable understanding of the underlying mechanisms, have identified the important variables, and have some understanding of the relevant noise. In other words, I generally want to have some confidence in a first-order model and know approximately where the interesting design space is before spending runs estimating curvature. Otherwise, you may build a very elegant mathematical model of a region you do not yet understand particularly well.
Now to your blocking question.
I would not block by material if material is one of the factors you want to compare. Blocking is principally a strategy for dealing with noise—variables that affect the response but that you are not willing or able to control as experimental factors. If you make each material a block, you confound the material effect with the block effect. You have then deliberately removed your ability to estimate cleanly the very effect you said you wanted to compare.
Instead, I would treat Material as a design factor.
If your hypothesis is that all four materials respond similarly to changes in the three continuous variables, that is actually an experimentally testable hypothesis. In addition to the main effect of Material, I would be interested in the Material × continuous-factor interactions. For example, does increasing pretreatment time have approximately the same effect for all four minerals? Does reagent amount affect all materials similarly? If those interactions are negligible, then your assumption of common behavior across materials has some experimental support. If they are important, simply comparing overall material averages could be quite misleading.
That distinction is important. Saying beforehand that you expect the materials to behave similarly is a prediction; the experiment gives you an opportunity to challenge that prediction.
This does not mean that you should not block. Blocking can be an excellent strategy, but I would block on a legitimate source of noise—for example, day, batch of raw material, operator, equipment setup, reagent lot, or some other condition under which groups of experimental runs must be performed. I often like blocks because they can also deliberately expand the inference space of the experiment and provide information about the robustness of the conclusions.
For example, if the experiment requires several days, I might deliberately distribute all four materials and the continuous-factor combinations across days rather than running Material 1 on Monday, Material 2 on Tuesday, etc. The latter would completely confound material with day. Randomizing the materials and treatment combinations within appropriate blocks would protect you from exactly the kind of bias you are concerned about.
So before worrying about how to create the block in JMP, I would first ask: What source of noise are you trying to block against? If the answer is simply "the four materials," then I would not call those blocks. They are experimental treatments and belong in the model as such.
"All models are wrong, some are useful" G.E.P. Box
