The DSD is somewhat rigid and does not provide a solution for every case. That situation is when you should use custom design. The rest of my reply is based on custom design.
Have you screened the 38 factors already? Is the data available? Might it be possible to augment the existing data to fit the new model and test the interactions? Augmentation is a great way to learn more with new evidence while leveraging existing data for economy.
When you say, "make no sense," do you mean that these treatments are physically impossible or do you mean that they are likely to produce bad outcomes (undesirable responses)? A test is a kind of guessing game used to "pick the winner." An experiment is a designed plan to collect data to fit a model, regardless of the desirability of the responses. There is a big difference between these two approaches and the mindsets behind them. If your case is the former, then there are ways to eliminate nonsense runs. If your case is the latter, then I would not eliminate any runs based on prejudice.
I would add all two-factor interactions. Do you have process knowledge about the interaction effects? If you know that an interaction is not possible, remove this term. If you know that an interaction is active, keep it (primary effect). For the rest of them, change their Estimability from Necessary to If Possible (potential effect). This change will produce a Bayesian optimal design. It resets the minimum number of runs, which is always equal to the number of parameters that are necessary to estimate. This change will help the economy of the design to meet your budget. We recommend using more than the minimum number of runs. A good approach is to consider the potential interactions and decide how many will actually be active. Of course, you do not know this number, but make an educated guess. Add 3-4 runs for every one of these effects. For example, if I have 30 potential interactions but I expect only 4 of them to be active, then I would add 12 to 16 runs to the minimum.
Is this study primarily to screen effects? You stated that you already know that the 30+8 factors are active, so you would not need to screen factors. If so, then leave the recommended criterion: D-optimal. If your primary goal is model selection followed by optimization, then change the criterion to I-optimal. If your goal is a combination of screening effects and optimization, then change the criterion to A-optimal.
You want to balance the need for economy with the need for good estimates of the model parameters or the response. The large number of terms in the model leads to a large number of runs but not necessarily a large number of error degrees of freedom that will be used to establish confidence intervals and hypothesis tests for your decisions. So you generally hope to have effects that are 3-4 times the size of the response standard deviation. Make sure that you widen the range of the continuous factors in order to produce the largest effects. Do not narrow the ranges around the level that you expect to be optimal. That is a testing approach. The wider range supports and experimenting approach.
