Our Approach
The Method
A black-box model learns what your plant did. A physics model knows what it should do. We run the physics and let your data calibrate it — which is why a fault the model has never seen still shows up on the first day.
Kremser absorption factors for the absorber. Counter-current ε-NTU and LMTD for the exchangers. Performance curves for the pumps, energy balances across the reboiler. Built from the equipment datasheets, not fitted to a training set.
The calibrated parameters are UA, cleanliness, fouling resistance and stage efficiency. When one drifts, something physical is happening and an engineer can say what. A drifting weight in a neural network tells you nothing.
Machine learning needs historical examples of each failure before it can recognise one. A residual against expected-healthy does not. That is the difference between a model that works from day one and one that waits for you to break something first.
Rich-amine flow to the lean/rich exchanger has no meter anywhere on the reference train. The model computes it from the lean circulation, the acid gas absorbed across the feed and treated analysers, and the filter side-stream.
The trade you are making
More on how it works
The platform runs on a real amine train with thirteen days of one-minute history behind it. Open a diagnosis and read the evidence for yourself.
Open the platform