Carbon Capture Facilities
A post-combustion capture plant is an absorber, a stripper and a reboiler running the solvent chemistry this platform was built and proven on. It is also an industry where the gap between a viable project and a stranded asset is a few tenths of a gigajoule per tonne — which makes it the clearest case anywhere for a model that prices energy while it is being spent.
SOURCEDGlobal CCS Institute, Global Status of CCS 2025 · peer-reviewed process assessments of amine-based capture. Every figure on this page is linked at the foot of it.
Part one — the capability
The engine compares what the plant is doing against what physics says a healthy unit should be doing, ranks the gaps, names the cause behind each one and prices the consequence. On a capture plant that price is denominated in the only unit the industry argues about: cost per tonne.
What goes wrongFoaming, absorber flooding, lean/rich exchanger fouling, reboiler scaling and corrosion, filter plugging, pump cavitation.
What comes back22 named faults, each with the root cause behind it and the evidence that fired. Capture rate held, and the reboiler duty behind every tonne accounted for rather than assumed.
What goes wrongOxidative and thermal degradation, heat-stable salt build-up, the slow climb in circulation needed to hold the same capture rate, reclaimer waste.
What comes backDegradation separated from operating change, so make-up cost and reclaiming timing are decided on a curve rather than on a laboratory sample that arrived three weeks late.
What goes wrongParticulates, SOₓ and NOₓ reaching the solvent, direct contact cooler performance, amine emissions and aerosol carryover from the wash section.
What comes backWhat the flue gas is actually doing to the solvent inventory, priced against make-up, and an emissions figure with a derivation rather than a design assumption.
What goes wrongCompressor efficiency decay, intercooler fouling, dehydration breakthrough, and specification drift at the pipeline or storage boundary.
What comes backCompression power against modelled power, so the parasitic load on the host plant is a measured number and not a line carried over from the FEED.
What goes wrongExtraction steam competing with power or heat the host could have sold, letdown losses, and a plant that quietly runs the capture unit at the wrong operating point to protect the host.
What comes backThe true cost of the steam the capture plant is taking, hour by hour, against what that steam was worth to the host — one number both sides can plan on.
What goes wrongCapture rate reconstructed after the fact, measurement gaps closed by estimate, and a reported tonnage that has to survive an auditor and a credit scheme.
What comes backModelled against measured balances, so a missing tonne has a location and a reported figure has a derivation someone can follow line by line.
One answer, four audiences
A finding only a specialist can read gets ignored; a finding only a manager can read gets distrusted. Because every call is denominated in both engineering units and money, one output serves everybody without being rewritten.
One ranked list at the start of a shift, in plant units, with the evidence attached. Not four hundred alarms and a colour.
The residual against expected-healthy, the indicators that fired, the ones that corroborate, and the ones that would have ruled the fault out. Something concrete to disagree with.
Which item is degrading, how quickly, and what each week of delay costs. Cleaning and outage scope argued from a number instead of from a habit.
Energy and losses per unit, priced daily. The margin review and the emissions report draw on one model, so they stop disagreeing with each other.
Part two — the market
Capture is not short of engineering or of capital appetite. It is short of operating evidence — and the evidence that decides the sector is energy per tonne, sustained, on a plant that is running. On the left is how that evidence is handled today. On the right is what it is worth.
To put one number on itThree tenths of a gigajoule per tonne of avoidable reboiler duty, on a plant capturing one million tonnes a year, is 300,000 GJ of steam a year — roughly $2.5–3.5 million at typical industrial steam cost. That is arithmetic with the steam price stated, not a forecast. The usual causes are a fouled lean/rich exchanger or a degraded solvent, and both are already among the twenty-two faults this platform names.
Why this and not another dashboard
A statistical model has to be shown the fault before it can find it. A physics reference model needs equipment datasheets. On a unit that has never failed inside its recorded history — which is most units, and every new-build — only one of those two is any use.
Every diagnosis carries the indicators that fired, the ones that corroborate it, and the ones that would have ruled it out. An engineer can argue with it. That is the only reason anyone in a control room ever acts on it.
Six consequences are priced directly, so a finding arrives as a cost per hour rather than a severity colour. The same output serves the operator, the planner and the board without being rewritten for each.
The cost of the next unit is datasheets, a tag list and a fitting pass — not a new product. That is the whole scaling argument: the platform grows by calibration, and calibration is the cheapest thing in this business to change.
The path from the calibrated unit
Each stage below reuses the same engine and the same knowledge-base structure. What changes is the unit it is fitted to — and that is deliberately the cheapest thing in this business to change.
Absorber, water wash, lean/rich exchanger, stripper, reboiler and the full solvent circuit, fitted against an operating amine train with thirteen days of one-minute history: 26 equipment items, 161 tags, 22 faults, 19 root causes, 54 indicators, 6 costed consequences.
Flue gas conditioning, the direct contact cooler, the water wash and the CO₂ compression and dehydration train. Same physics, documented equipment, and a calibration pass against your own history.
Where the capture plant stops being a standalone unit and starts being an argument with the host about extraction steam. Modelling both sides puts a number on that argument instead of a preference.
The two hard parts of an optimiser already exist here: a validated plant model and an objective denominated in money. Scoring candidate operating points automatically — rather than one at a time — turns diagnosis into a decision.
Being straight about it
The platform runs on a real amine train with thirteen days of one-minute history behind it — live diagnosis, ranked evidence, root causes and cost per hour. It is the same chemistry and the same fault set your capture plant runs on.
Sources
The industry figures on this page are public and third-party. The amine figures — 22 faults, 19 root causes, 54 indicators, 6 costed consequences, thirteen days of one-minute history — are ours, and are countable in the platform.