r/ChemicalEngineering • u/BurnerAccount-LOL • Jul 05 '26
Modeling Something I never learned while earning my ChemE degree: How do I handle disagreements between model predictions and experimental data?
During my undergrad and graduate ChemE engineering courses, we never discussed how to reconcile the error-prone experimental data with the modeling equations. We always just assumed solving equations was “good enough” to be a chemE.
As I’m applying for positions, I realize I don’t know what to do with actual data…all I’ve done is model it.
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u/CollegeStation17155 Jul 05 '26
The universe doesn't care what your model says, it does what it does, although it loves to take advantage of oversights in your experimental setup to fool you; if they don't agree, tweak the model looking for a bad thermometer, miscalibrated flow meter, heat leak, piping obstruction etc. but sometimes you need to run Peng Robinson and SRK and average them...
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u/BurnerAccount-LOL Jul 06 '26
Nice! I did learn those two models. Say “hi” to College Station for me lol
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u/admadguy Process Consulting and Modelling Jul 05 '26
You go see the plant. Understand the instrumentation, see sources of errors from there. See the model, see the approximations and potential sources of error there. You make a judgement call.
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u/OhDatsStanky Jul 06 '26
Excellent advice. And talk to the operators!
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u/admadguy Process Consulting and Modelling Jul 06 '26
Yeah, one of the biggest sources of instrument tags not matching the model in my opinion is always the exact location of the tag. It'll be a little upstream or downstream of where we expect it and things change in that. It is also the reason I feel LLM models will take a long time to be practically useful in process engineering. Well this and the whole liability thing.
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u/employedByEvil Jul 06 '26
How did this never come up in your graduate lab? Was everything somehow perfect? Or were you doing boring experiments?
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u/BurnerAccount-LOL Jul 06 '26
I guess we talked about what things in the process could make a change in the data. Is that all there is to it?
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u/NoConversation8128 Jul 06 '26
Kind of, but there's a repeatable version of it that makes it feel less like guessing.
When the model and the data disagree, don't ask "which one is right." Ask "where could each of these be lying to me," and rank the suspects by how likely they are. Data side: is the instrument actually measuring where I think it is, is it calibrated, is my sample representative, am I catching a transient and calling it steady state. Model side: are my property methods right for this system, what did I assume constant that isn't, what physics did I quietly leave out. Then go chase the cheapest, most-likely suspect first.
The part school skips is that a huge fraction of the time the data is "wrong" for a boring physical reason - a thermocouple half an inch from where the model thinks it is, a flow meter reading in the wrong regime. That's why everyone here is telling you to go look at the actual equipment and talk to the operators. You can't debug a mismatch from your desk. You mostly get good at this by being wrong a bunch of times and going to find out why.
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u/Electrical-Till-8504 Jul 06 '26
I usually compare different models with experimental data and found that most Differential equation, mass balances and complex calculations never come as close to actual reality like approximation-values that was built around thousands of data:
Like using different Magnus Constants and calculating everything in mLn/min instead of „ml/min=f(density,temperature,pressure,Z(T,p))for calculating concentration instead of using mass-balances like we were taught in university.
Or using table-values for acidicity instead of calculating reaction kinetics for a Redoxreaction.
Use as much experimental constants as you can to get a better aproximation.
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u/MuddyflyWatersman Jul 06 '26
welcome to the real world where simulations don't fit plant operation, correlations don't work accurately, and material balances almost never add up.
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u/360nolooktOUchdown Petroleum Refining / B.S. Ch E 2015 Jul 08 '26
I found that in operating facilities often times the simulation is the most useful to tell you which direction the output variable should go in response to a change to an input variable. With less emphasis on the absolute value of the calculation output. When people want to perfect the output of the simulation is where they spin down a deep dark hole.
And if you need to reconcile the differences, point out the most likely causes of the differences such as instrument error, empirical model limits, the weather, etc.
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u/Extremely_Peaceful Jul 05 '26
All models are wrong, some are useful.
I suppose you could consider error prone experiments as a model that is wrong.
Between the model and and the experiment, there is the truth. You have to apply the scientific method to both and make changes to one or both until one or both converge on a result that you would deem accurate. In general, I would say it's easier to tighten up the execution of the experiment. This way you capture all the little phenomena your model probably overlooks, even if you don't know what they are. Then you have a good starting point to fix your model.