Some comments:

1. First allow me to make the point there is a difference between statistical significance (which you control by how you run the experiment and the model you use) and practical significance. Without knowing how much of a change in Y is of practical significance, analysis will be suboptimal.

2. Regarding factors that are determined to be statistically insignificant, you may interpret this as **Given the levels you tested at**, those factors do not a have the influence of the other factors in the model. Whether you can infer or extrapolate those findings is more an engineering/scientific question than a statistical one.

3. Always use Rsquare Adj as the default. RSquares will always increase with the addition of degrees of freedom to the model, but the point is to determine the RSquare with terms that are considered significant. That is what RSquare Adj accounts for. If there are differences between RSquare and RSquare Adj it is an indication of an over specified model. Oh and by the way this has nothing to do with whether the model will be useful in the future or under a different inference space.

4. When choosing the "best" model a number of considerations must be taken into account. RSquare, RMSE, Residuals, Practical assessment, etc.

5. Running replicates is an excellent technique to test model adequacy. Using blocking method can be quite useful in exploring the effect of "noise" and in some cases to determine if the effects estimated in the first replicate (Block = -1) repeat over different noise conditions (Block = 1). This is one of the intended purposes, to see if factor effects are robust to noise. IMHO, Blocking should be well thought out prior to experimentation, but you may be able to salvage some useful information as a posteriori attempt.

6. Looks like you could have run a Res. III fractional factorial with factors at 2 levels in 8 treatments (vs. the DSD) and gotten the same results. You could have saved runs and used them for additional replicates...hindsight is always 20-20

"All models are wrong, some are useful" G.E.P. Box