Capture System Reliability KPI

Capture System Reliability is critical for ensuring operational efficiency and maintaining financial health.

High reliability directly influences customer satisfaction and retention, while also impacting cash flow and working capital management.

By tracking this KPI, organizations can identify lagging metrics and improve overall business outcomes.

A robust KPI framework allows for better strategic alignment and data-driven decision-making.

Companies that excel in system reliability can expect enhanced ROI metrics and improved forecasting accuracy.

Ultimately, this KPI serves as a key figure in management reporting, driving informed actions across the organization.

How Capture System Reliability Connects to Your Strategy

Capture System Reliability belongs to one KPI group in KPI Depot, Carbon Capture & Storage, where it ranks seventh of one hundred and six members. The six the group puts ahead of it set the context it has to serve: CO2 Capture Efficiency and Total Emissions Reduced at the front, then Capture Rate, Carbon Storage Capacity, Leakage Rate and Capture System Uptime. Cost per Ton of CO2 Captured sits directly behind it. That places this KPI inside the group's operational block and just above the point where the group turns to money.

Deal first with the metric immediately above it. Capture System Uptime is the sixth priority in the same KPI group, and this KPI's stated formula, operational time over total time, is an availability calculation. Two neighboring entries, one arithmetic. The definitions are what separate them: uptime asks whether the train was running, while reliability is defined here as consistency in achieving the desired capture rate, which is a performance question, not a clock question. An operator that adopts both without writing that distinction into the tag logic will publish one series twice under two names, and the group's ordering will look like redundancy rather than a deliberate pair.

Its balanced scorecard perspective is internal process, shared with CO2 Capture Efficiency, Capture Rate, Leakage Rate and Capture System Uptime. That makes it a leading measure with respect to the two financial entries around it. Total Emissions Reduced and Cost per Ton of CO2 Captured settle at the end of a period and cannot be steered directly. Availability moves first, hour by hour, and it is the mechanism by which both of those later numbers arrive where they do.

The sharpest tension in the KPI group is with Cost per Ton of CO2 Captured, and the group's own guidance names the pair. Availability is something a plant buys: spare compression trains, redundant solvent pumps, a larger solvent inventory, standing maintenance coverage, more frequent reclaimer campaigns. Every one of those raises fixed cost per period, so a genuine reliability improvement can land alongside a worse unit cost and look like a failure. The reverse trade is more dangerous and less visible. Deferring a scheduled outage or stretching a reclaiming campaign lifts availability in the current quarter and transfers the loss to a later one, usually as an unplanned trip that costs more hours than the deferred work would have.

A second tension runs to CO2 Capture Efficiency, the group's first priority. A capture train has many ways to stay online while doing less: reduced solvent circulation, steam diverted away from the reboiler when the host plant needs it, operation on degraded solvent, partial load through the compression stage. Under a time-based reading every one of those is a fully reliable hour. Under the definition this KPI actually carries, none of them is. Reading reliability without capture efficiency beside it rewards the plant for keeping equipment spinning rather than for capturing CO2.

There is also a downstream constraint worth naming. Carbon Storage Capacity is the KPI group's fourth priority and sits in the growth perspective, which means the capture train is not the only gate. A capture system with excellent availability in front of a constrained injection well or a saturated storage site does not produce stored CO2; it produces a bottleneck one step further along. Where the reliability boundary is drawn, absorber only or absorber through compression, dehydration and injection, decides whether this metric can see that at all.

Measuring Capture System Reliability in Practice

The raw material is the process historian, and it is more ambiguous than it looks. Availability is normally derived from equipment run flags, and a run flag is a poor proxy for capture. A solvent pump can be running on recirculation, a compressor can be spinning on minimum flow with the CO2 going nowhere, an absorber can be online while the host plant sends no flue gas. Derive the operating state from CO2 mass flow past the export meter rather than from motor status, and reconcile that series against the maintenance system's work orders and the shift log before you trust either. The three records will disagree, and the disagreement is usually where the real outage causes are hiding.

The denominator is the first decision and the one most often left implicit. Total time can mean calendar hours, hours the unit was scheduled to operate, or hours the host plant produced flue gas at all. Those produce very different figures for the same plant. A capture unit bolted to a cycling power station is idle whenever the station is, and if host outages sit in the denominator the metric grades the host rather than the capture island. Retrofits and greenfield units are therefore not comparable on this metric without stating which convention each used. Planned outages need the same explicit treatment: excluding turnarounds gives you a measure of unexpected loss, including them gives you a measure of delivered service, and both are legitimate as long as one is chosen and held.

The second decision is what counts as operational, and it is the fork built into this KPI's own text. The definition speaks of achieving desired capture rates while the formula counts time. Between those sits every derated hour. Decide whether the unit must be at design capture rate, above some agreed floor, or merely energized, and record partial-capacity hours as a fraction rather than rounding them to fully up or fully down. Equivalent availability, weighting each hour by the capture achieved in it, is the more honest construction, and it stops the metric from paying for hours of token operation.

Segmentation earns its keep on cause and on subsystem. Split loss of availability by whether it originated in the host plant, the utilities and steam supply, the capture island itself, the compression and dehydration train, or downstream transport and injection, because the improvement levers for those five owners are entirely different and only one or two belong to the capture team. Then split by subsystem within the island: solvent circulation, the reboiler and steam interface, the compressor, dehydration. Compression is the frequent offender and it is often outside the boundary that the reported metric uses. Season matters too, since cooling water temperature and ambient conditions change both trip frequency and achievable capture.

Several instrumentation habits distort this metric specifically. Historian compression and dead-banding swallow short excursions, and short trips are exactly what a solvent system dislikes most, so a series that looks clean can sit on top of dozens of invisible restarts. Outage logs recorded to the nearest shift make anything shorter than a shift disappear. Cause codes backfilled days later drift toward whichever owner is least contested. Commissioning and ramp-up months left in the trend line make every subsequent period look like an improvement that has nothing to do with maintenance practice. And the slowest distortion of all is solvent degradation, which almost never registers as downtime: the unit runs, the hours accrue, capture quietly falls, and a time-based reliability figure records a perfect period while the plant progressively stops doing its job.

One last practice. Publish the outage count beside the availability figure. A period built from one long planned outage and a period built from many short unplanned trips can produce the same availability, and they say opposite things about the state of the asset.

Common Pitfalls

Many organizations overlook the importance of system reliability, focusing instead on short-term gains.

• Failing to regularly update technology can lead to outdated systems that hinder performance. Legacy systems often struggle to keep pace with modern demands, increasing downtime and operational costs.
• Neglecting to train staff on new processes results in inefficiencies. Employees may not fully utilize available tools, leading to missed opportunities for improvement.
• Ignoring data analytics prevents organizations from identifying trends and issues. Without quantitative analysis, it becomes challenging to make informed decisions that enhance system reliability.
• Overcomplicating workflows can create bottlenecks that disrupt operations. Streamlined processes are essential for maintaining high reliability and ensuring quick response times.

Improvement Levers

Enhancing Capture System Reliability requires a proactive approach to identify and eliminate potential issues.

• Invest in modernizing technology infrastructure to improve performance. Upgrading systems can reduce downtime and enhance overall operational efficiency.
• Implement regular training programs for staff to ensure they understand new tools and processes. Empowered employees are more likely to contribute to system reliability.
• Utilize data analytics to track results and identify areas for improvement. Regularly reviewing performance indicators can help organizations stay ahead of potential issues.
• Streamline workflows to eliminate unnecessary steps that could lead to delays. Simplified processes enhance reliability and improve response times.

KPI Depot is trusted by consulting, strategy, finance, and analytics teams at leading organizations worldwide, including those listed below.

OKRs That Use Capture System Reliability

The Carbon Capture & Storage KPI group names this metric directly in its own OKR material, under the objective to optimize operational costs and energy use without compromising capture quality. It appears there beside Cost per Ton of CO2 Captured and Energy Consumption per Ton of CO2 Captured, and the group's rationale explains why the three travel together: reliability suppresses the unplanned expense and lost output that inflate unit cost, while the cost and energy key results stop reliability from being purchased at any price. The objective is written so that no single one of the three can be satisfied on its own.

That gives a clean directional key result. Lift capture availability while cutting cost per ton of CO2 captured and energy use per ton captured over the same cycle, with the availability figure reported on an equivalent basis so derated hours cannot be counted as full ones. The group's own best-practice guidance points the same way when it advises reducing unit cost without sacrificing reliability, which is a warning that the cheap route to a better cost number is deferred maintenance and a worse asset a year later.

A second framing comes from the group's objective to maximize carbon capture performance, which carries CO2 Capture Efficiency, Capture Rate, Capture System Uptime and CO2 purity as its key results. Reliability is the qualifier on that whole set. Efficiency and purity are measured while the plant runs, so a team can hit all of them across a period in which the unit was available for only part of the time. Written as a key result under this objective, reliability commits the team to holding those gains continuously rather than in the good weeks, which is precisely the point the group's guidance makes about turning capture improvements into everyday output instead of sporadic spikes. Any level attached to either objective is the team's own target for its own plant and vintage of technology, not a standard drawn from elsewhere.

See OKR Examples for Carbon Capture & Storage

KPI Categories

This KPI is associated with the following categories and industries in our KPI database: