Four plain tests
Frozen: the value has not changed for hours. Missing: gaps in the window. Negative: a flow, level or pressure below zero. Spiky: samples far outside the window’s own spread.

Can you trust the reading?
Every other tool reads the instruments and believes them. Verify checks them first: every recorded tag is tested for frozen, missing, below-zero and spiky readings, and the tags the diagnosis relies on are scored for trust.
MVPEvery figure above is counted in the minimum viable product: the amine train modelled for gas sweetening and post-combustion carbon capture.
What it does
Frozen: the value has not changed for hours. Missing: gaps in the window. Negative: a flow, level or pressure below zero. Spiky: samples far outside the window’s own spread.
Ok, note, suspect or bad — with the detail that caused it, the coverage and the frozen minutes.
The share of tags the diagnostic engine relies on that came through clean. Every other tool is only as honest as this number.
A controller output held constant by design, or an analyser sitting at its floor, is a note — never a suspect.
How it works
The hours behind the moment you want to trust.
Frozen, missing, negative and spiky, each with a severity.
Ok, note, suspect or bad.
The trust figure for the tags the engine depends on.
Where it applies
The platform is focused on acid gas removal — gas sweetening and post-combustion carbon capture — because that is where the model is calibrated and where the fault library is real. The method underneath is not amine-specific: a physics reference model, residuals against expected-healthy, fuzzy diagnosis over graded indicators, and costed consequences. Extending it to another unit means new datasheets and a new calibration, not a new platform.
The platform runs on a real amine train with thirteen days of one-minute history behind it. Nothing on these pages is a mock-up.
Open the platform