Choice of DoE setup

Robert1

I have a question over the setup of a DoE and which might be most appropriate from all the options available.

My experiment relates to maximising the yield of a particular chemical extraction and I have ~5, 2-level parameters both continuous (e.g. time, temp, volume) and categorical (e.g. sample #, shaking on/off).  I would like to have triplicates as the results can be noisy, and I am limited to 24 experiments in one run.  I am considering the merits of centre points also.  My aim is really to find which parameters are critical to the output, and which don't make a difference, rather than try and predict outputs exactly.  I also wonder whether it would be beneficial to split this out into multiple DoEs e.g. eliminate non-critical parameters first then probe more deeply those that are critical.

 

In JMP you have the options of starting with a custom design, definitive screening, screening, response surface, full factorial, mixture and taguchi.  I can eliminate response surface as I believe that is for only continous parameters.  However for the rest I'm unsure which to start with, and it seems you can arrive at the same point from different routes e.g. screening -> choose from list -> full factorial.  Screening seems like it might be most appropriate but I would be interested to hear thoughts from the community on how one decides which approach is best.

 

cwillden

From your description of the objective, it definitely sounds like you are needing a screening design.  However, I do want to point out that response surface designs are not just for all continuous factors.  You'll eventually want to do one if you ever need to go for good prediction within your design space.

 

5 factors in 24 runs is certainly do-able for a screening design, though you're not going to be able to replicate every point multiple times.  I'd recommend Custom Design and specifying a model of all main effects and 2-factor interactions.  When I ran 5 factors in 24 runs through there, I got a design with 8 of the points replicated, so you'll have a decent estimate of pure error.

 

Definitive Screening is probably not right for you unless you want to get something of a response surface at the same time and feel up to the task of attempting a much more complex analysis.  Mixture designs are not relevant here, you don't have enough runs for a full factorial (32 runs for 5 factors), and Taguchi arrays are pretty old school and usually for situations where you need a highly fractionated design (not your case).

 

In the Screening Design platform, you'll basically have a choice of factorials (full and fractional) and Plackett Burman.  A fractional factorial would only allow powers of 2, so you'd you have to choose either 8, 16, or 32 runs for your design.  At 16 runs, you would have no degrees of freedom for error since you are starting with 16 potential model terms.  You can do that if you're really just looking for the big knobs in your experiment.  Placket Burman is for 12 runs and really only gives you main effects.

 

You can't go wrong with Custom Design.  It will very often reproduce the classical designs for the same number of runs, but with Custom Design you have complete flexibility over the model you want to estimate and how many runs to include.  For optimality criteria, choose D-optimality for a screening scenario.  That criterion emphasizes power/precise parameter estimates, so you have the highest possible chance of detecting any significant effects for the number of runs you can do.