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One thing I have often found is that data context matters for a lot of modelling work. It's all well and good to throw 1000 inputs named F_1...F_1000 into a mod

One thing I have often found is that data context matters for a lot of modelling work. It's all well and good to throw 1000 inputs named F_1...F_1000 into a model and see what gets spat out but I question how valid that is going to be. To give a recent example from my workplace: There is an online gas chromatograph hooked up to a reactor - external modelling (of the throw 1000+ variables at the wall and see what sticks variety) showed the percentage of nitrogen, as measured by chromatograph was strongly correlated to something we'd asked them to investigate. The people doing the modelling who had no experience with basic chemistry latched onto this and started to get really excited they wrote a large report full of recommendations about nitrogen. When my manager saw this he burst out laughing - nitrogen is an inert gas it plays no role in the reaction what the data was actually showing is that nitrogen content varies in response to the concentration of other gasses (H2, Oxygen and CO/CO2) changing - if you recall air is typically 78% nitrogen. We don't control Nitrogen at all it's value is determined based on the other gasses in the reactor. I think this demonstrates pretty well how easily people can see something in the data and jump to some conclusions that while well meaning lack a fundamental understanding. Model explainability is good but input explainability is just as important - important question to ask are often. -What am I feeding into this model? -What is the expected behavior of this input? (I don't know is a valid answer here) -Did the input behave as I expected it to in the model? -If Not why? (This is the interesting and potentially valuable part)

adrianratnapala 8y ago
So if I am understanding this story, some people found a correlation and mistook it for causation. An obvious no-no, albeit a easy trap to fall into. That's assuming what you were looking for was a handle on how you could get more or less of whatever it was you wanted. But if you just wanted a diagnostic, then the data is telling you that the nitrogen content is (in practice) such a diagnostic. Depending on what you already knew, that might or might not be interesting or useful, but it is hardly nonsense.
- bigger_cheese 8y ago
  Yes that's essentially correct the N2 means something just not what the analysts thought it meant. Their recommendations were misleading and could have saved some time if they'd discussed it with someone familiar with process.
thereisnospork 8y ago
At moderate risk of putting my foot in my mouth, not knowing your specific situation[1]: It sounds like the modelers did exactly what they were supposed to. Just because N2 isn't directly controlled doesn't mean [N2] isn't a valid factor - even if its only mechanistic action is as a diluent. Its entirely possible an aspect of a reaction is sensitive to absolute reactant partial pressures as opposed to just relative concentration. Just as often as modellers going nuts, abusing either the chemistry or signal-to-noise, I've also seen chemists argue that multivariate-modelling of some rather simple system is impossible or 'not science', relegating research to inefficient linear trial and error. [1] For instance, if that is a Fischer-Tropsch reactor you are describing, then yes [N2] would likely go up with higher yield as your reactive gas species coalesce into heavier molecules.
- bigger_cheese 8y ago
  They were modelling a fluidized bed reactor. The issue was their understanding of what the signal meant, they wrote a rather lengthy report full of nonsensical recommendations on basis of not properly understanding what the input variable represented. edit: At least at my org we use multivariate a lot this is mostly primary metallurgy not pure chem eng though.

adrianratnapala 8y ago

So if I am understanding this story, some people found a correlation and mistook it for causation. An obvious no-no, albeit a easy trap to fall into. That's assuming what you were looking for was a handle on how you could get more or less of whatever it was you wanted. But if you just wanted a diagnostic, then the data is telling you that the nitrogen content is (in practice) such a diagnostic. Depending on what you already knew, that might or might not be interesting or useful, but it is hardly nonsense.

bigger_cheese 8y ago
Yes that's essentially correct the N2 means something just not what the analysts thought it meant. Their recommendations were misleading and could have saved some time if they'd discussed it with someone familiar with process.

thereisnospork 8y ago

At moderate risk of putting my foot in my mouth, not knowing your specific situation[1]: It sounds like the modelers did exactly what they were supposed to. Just because N2 isn't directly controlled doesn't mean [N2] isn't a valid factor - even if its only mechanistic action is as a diluent. Its entirely possible an aspect of a reaction is sensitive to absolute reactant partial pressures as opposed to just relative concentration. Just as often as modellers going nuts, abusing either the chemistry or signal-to-noise, I've also seen chemists argue that multivariate-modelling of some rather simple system is impossible or 'not science', relegating research to inefficient linear trial and error. [1] For instance, if that is a Fischer-Tropsch reactor you are describing, then yes [N2] would likely go up with higher yield as your reactive gas species coalesce into heavier molecules.

bigger_cheese 8y ago
They were modelling a fluidized bed reactor. The issue was their understanding of what the signal meant, they wrote a rather lengthy report full of nonsensical recommendations on basis of not properly understanding what the input variable represented. edit: At least at my org we use multivariate a lot this is mostly primary metallurgy not pure chem eng though.