Most "digital twin" posts you'll read are about mechanical systems — wind turbines, jet engines, manufacturing lines. The recipe is well understood: instrument the asset, fit a physics-informed model, and use the residual for anomaly detection.
Chemical processes break that recipe in a few specific ways.
The state space is enormous and partially observable
A distillation column has perhaps a dozen sensors and tens of thousands of internal variables you'd actually like to know — concentrations of trace species, local temperature gradients, fouling on each tray. You can't just "instrument harder"; many of the variables you care about are physically inaccessible at runtime.
This is why first-principles models still matter so much in our field. They aren't just regularizers — they're the only way to know things you can't measure.
Time scales span eight orders of magnitude
Reaction kinetics happen in milliseconds. Heat exchange happens in seconds. Catalyst deactivation happens over months. A useful twin has to handle all of these simultaneously — and a single ODE solver tuned for one regime is wrong for the others.
The economic loop is short and brutal
A 1% improvement in yield on a commodity chemical line is real money. A 1% drift in your model is also real money — in the wrong direction. The bar for "good enough" in chemical twins is higher than in most other domains, because the operator will notice almost immediately when your model is wrong.
I'll come back to each of these in more detail. For now, the takeaway: when someone shows you a digital twin demo, the question to ask isn't "is the model accurate?" It's "what's the loss function, and over what time horizon?"