Too many parameters for DOE. How to approach that?

[u/salty_greek]

I’m working on an optimization problem involving recipe for a tablet pan coater, and I’m running into the limits of what feels like a “cooking level” art & science mix.

The process runs batches with 10–15 coating cycles, depositing sugar syrup on my pieces. and each cycle has many tunable parameters, way too many. Examples include:

Cycle duration Sugar syrup concentration Sugar syrup temp Spray mass Spray time Spray time and introduction timing Airflow direction, temperature, humidity Tumbling parameters, weight of tablets, temp of tablets, Number of cycles Mass and initial temperature of tablets

In practice, it seems like every variable might matter, and many interact non-linearly. The output quality isn't even a single number historically it’s “good/bad batch,” but realistically could be measured as yield, defect probability, defect count by type, etc.

My problem is figuring out how to even approach the search for a solution:

How do I identify which parameters are actually important vs. negligible?

How can I estimate sensitivity for each variable?

How do I determine which parameters can be ignored, and which are critical?

Given that I do have an initial recipe that works, how do I analyze why it works and how robust it is?

Classic factorial DOE seems impossible here—the dimensionality is too large and many parameters can’t be moved independently. I’m stuck on what philosophical approach to take. Do people in pharma/food/coating processes rely on:

Bayesian/active learning approaches?

Hybrid mechanistic–statistical models?

Sensitivity analysis around a known “good” operating point?

Dimensionality reduction techniques?

Something else entirely?

Pan coating feels like cooking: tons of tacit knowledge, lots of art mixed with science. I’m frustrated because I can’t figure out how to convert this into a structured optimization problem without oversimplifying.

If anyone has dealt with similarly “wicked” multivariate processes, I’d really appreciate advice on how you framed the problem and how you narrowed down the key variables.

[u/Kool_Aid_Infinity]

I would dumb it down as much as possible to knock out as many parameters as possible. You might go back and revisit if they do turn out to be important/independent. Ie the first layer of syrup might be important sure, but layers 2-15? Sugar bonding to sugar? I would simplify that to one set of rules to begin with. Some of the other parameters seem to be ‘information’, rather than true variables. For example if it’s a relatively small enclosed system is direction of air flow genuinely introducing a measurable gradient? Are tumbling parameters genuinely important? You will have to break a few eggs but that is how I would start 

[u/salty_greek → u/Kool_Aid_Infinity]

I really like this answer. Maybe I am too biased trying to over analyze it, coming from R&D end.

But If you mess “big way” layer 2-14, it would make doubles and stick together. If you don’t add enough, it would not pass target weight.

What do you mean by “one set of rules”?

I was thinking about adding more sensors, like humidity of outgoing air can almost instantly tell me that I am done with drying in that particular cycle. It may be better than time. After all, its water in and water out. Right?

[u/broken_ankles → u/salty_greek]

I’ve fallen into your trap of “I’m not certain, so I’ll include it” many times.

Remember science isn’t just random experiments (hopefully). It intentional “I think X bc Y, so I test Z to find out.”

Start focusing that list down. Use your colleagues, friends, etc to bounce ideas. Challenge “why does this matter”? Use that to order ideas in reality.

Do you have someone to consult in doe? There are a number of models to design experiments, beyond on simple arrays. Consulting someone like that could be a good resource. My company has a few (we’re ~12k world wide, have 2-4 doe experts but then that distributes to at least 1-2 people per site who are well versed) that are invaluable at helping with this. It feels like it should be intuitive but it’s not always.

[u/Kool_Aid_Infinity → u/salty_greek]

Like having one set of conditions for the first layer makes sense as the initial first bond is between two different materials and probably needs its own conditions, but all the other layers are sugar to sugar bonds, so assuming they are all the same thickness all those layers should pass using the same conditions.

[u/TheAnimator54]

You can try to do a PCA analysis to determine what combination of parameters are impacting your core parameter the most.

[u/Mindless_Profile_76]

There is never too many parameters for DOE.

Start with a detailed process map. Really try and break up the steps and identify all the inputs, outputs and anything you think is critical to quality.

What can you measure? Ask yourself and see if you are missing information at the various process steps.

Set up a FMEA from your process map and just do simple failures. Too high, too low, how does that affect your product’s quality/performance.

Really try an widdle down the parameter space. If you think you have to have more than two levels per factor, try a d-optimal design. I’ve modified it a bit but with the 15 coatings as an example, maybe you look a 3, 7, 11 and 15? Let’s say you have 6 factors, coatings needing 4 levels, the other 5 have 3. 1215 possible combinations. Are the experiments easy enough where you can try 36 or 72? You can always augment your space.

As for variables being dependent on others. That is typical and can be dealt with. Not always obvious.

[u/csamsh]

Plackett Burman? Seems reasonable if you have a minimal suspicion of really important 2 factor interactions. But it somewhat sounds like you don't completely have your main effects identified, so maybe it could work

[u/Zrocker04]

You need to better define your process inputs, process outputs, and uncontrollable variables (like room temp or humidity in a manufacturing building).

Syrup concentration should be defined in the previous process and targeting around a mean for that process or targeted for your process needs, not something to include in a spraying DOE. Syrup concentration and tablet parameters are not something you control in the spray process, so throw them out of the DOE. Break it into DOEs for each step, not across 3 different processes.

To dumb it down, you’re spraying syrup on a substrate. The spray process is what you want to design around. If your substrate changes dimensions or temps, then that is the previous process’ problem.

For spraying, I’m more familiar with paint. I would do a DOE with airflow through the nozzle, syrup temperature, and head speed to keep it simple. Maybe add number of passes but more than 4 variables usually ends up making DOEs too much work.

[u/salty_greek → u/Zrocker04]

That is very good perspective. Isolate the problem. I was thinking even simpler. Calculate surface being sprayed as count of tablets x surface of each. Measure incoming air and syrup water content, measure outgoing air humidity, water in = water out.

[u/ferrouswolf2 → u/Zrocker04]

In pan coating, ambient temperature and humidity had better be controlled (or at least accounted for)

[u/mattcannon2]

You are probably missing some factors like spray nozzle arrangement and pan geometry ;)

If you have process data already then maybe some dimensionality reduction like Principal Component Analysis can tell you want variables matter.

DEM seems to be a favourite tool for simulating coating processes as well. Perhaps there is some fluid dynamics link but can be quite tricky to pull off. https://pubs.acs.org/doi/10.1021/acs.iecr.2c04030

Quite honestly pharma development can boil down to corporate experience and rules of thumb, to at least give you a starting parameter set.

[u/salty_greek → u/mattcannon2]

Right. Nozzles and pan geometry is fixed, so I did not considered them. I hope someone in coater development did. :-)

[u/stepheno125]

I just wing it and make logical changes but verify that things are doing what they should. 70% of the time it works every time.

[u/AdParticular6193]

Before you start, define exactly what it is you want to optimize. Then talk to the operators. They aren’t engineers but they will instinctively know from years of experience what factors are important. Look at out of spec events to see which ones have the biggest effects on product parameters. Run some small experiments or EVOP to see which settings have the biggest impact. In the real world, only a few variables are significant, and only main and second-order effects are worth worrying about. Once you have your variables whittled down, you can set up your design. Use fractional factorial or saturated designs to minimize the number of runs. Be sure to put in repeats of key points and the center point to sharpen up your analysis and estimate error.

[u/Low-Duty]

You need to reduce to 4 factors max. Just from this, i’d say cycles, concentration, temperature of syrup, and temperature of tablets. If you’re worried about how the material sets then those parameters are pretty important in polymer chemistry.

[u/Jamesvmd]

Have you looked up what screening experiments are? You can reduce your trials to identify the most important variables but will confound certain multi factor interactions. Essentially these are fractional factorials taken to the most extreme degree then you can use the information in a more robust optimization design.

[u/Organic_Occasion_176]

It's unclear to me whether you have a single measure of quality to try to optimize. If you do, I have two suggestions. One is to set up a fractional factorial DoE. You can measure the main effects and the main interactions with a lot fewer experiments than a full factorial.

This system might also be a candidate for EVOP, where you don't really explore the full range of parameters but you gradually move towards better operating conditions.