See It Work
See It Work
SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+ SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+

Condition Monitoring · POWER UTILITIES

Catch boiler-tube corrosion in the demin-water chemistry, not at failure.

Boiler-tube corrosion is one of the most expensive failure modes in power generation — and it starts as a chemistry drift in the demineralised water cycle long before it shows up as a tube leak. The XMPro AO Platform monitors demin-water chemistry continuously and surfaces corrosion risk as a managed engineering signal.

THE CHALLENGE

What's getting in the way today.

Demin-water chemistry sits upstream of boiler-tube integrity. Three pressures compound:

ISSUE 01 OPEN

Slow chemistry drift

Conductivity, pH and dissolved-oxygen changes develop quietly across the demineralised water cycle — tube corrosion is well underway before the chemistry trend is noticed.

ISSUE 02 OPEN

Reactive failure response

Boiler-tube leaks force unplanned outages that cascade across the generation schedule — the cost of the failure dwarfs the cost of monitoring it.

ISSUE 03 OPEN

Fragmented chemistry data

Conductivity meters, dissolved-oxygen analysers and silica monitors live in separate systems — the correlation that signals corrosion risk goes unseen.

THE SOLUTION

Demin Water Monitoring for Boiler Tube Corrosion — how it works.

A continuous corrosion-risk picture of the demineralised water cycle — fed by the chemistry sensors already in service and tied to predictive maintenance.

Real-time data integration Predictive analytics Anomaly detection Automated recommendations Operational dashboards

The platform integrates conductivity, pH, dissolved-oxygen, silica and cation-conductivity telemetry from across the demineralised water cycle continuously. ML models analyse the chemistry streams to flag corrosion-risk patterns — dissolved-oxygen breakthrough, cation-conductivity drift, silica carry-over — with confidence scoring and time-to-action windows. Threshold breaches trigger ranked recommendations to chemistry and boiler teams, and dashboards drill from cycle-level chemistry health into individual sample points so corrosion risk becomes a managed engineering signal rather than a post-failure investigation.

SEE IT IN YOUR ENVIRONMENT

Scope this for your operation.

Tell us about your fleet, your control maturity and the lever that matters most. We’ll map this use case to your starting point.

WHAT CHANGES

What this looks like in operation.

Corrosion as an upstream signal

Chemistry drift is flagged before it manifests as tube damage — corrective action moves into the demin-water cycle rather than the boiler.

Fewer forced outages

Predicted corrosion risk lets generation planning move tube inspection into scheduled outages rather than reacting to leaks.

Defensible chemistry decisions

Chemistry-control decisions are backed by trends the engineering team can audit, not by spot samples.

DEPLOYED IN

Built for these industries.

PRODUCTION-PROVEN

Not a concept. In production.

XMPro is deployed at Tier 1 global operators across asset-intensive and mission-critical industries — delivering measurable results across predictive maintenance, process optimisation and operational intelligence.

VERIFIED RESULT — OIL & GAS
$16M Saved every year
18% Reduction in field service trips
95% Reduction in maintenance planning

Customer Case Study

Using XMPro, a global oil and gas supermajor rapidly composed and deployed an intelligent oil well maintenance solution in just three months -- achieving over $8 million in calculated value within the first six months.

VERIFIED RESULT — MINING
$10M Saved every year
30% Reduction in conveyor downtime
9,000t Saved every month

Customer Case Study

Using XMPro, the world's largest potash mining company rapidly composed and deployed a predictive maintenance solution for over 50 miles of underground conveyors in just 30 days, achieving $10 million in savings every year by reducing unplanned downtime by over 30%.

VERIFIED RESULT — ENTERPRISE SCALE
6 Sites with in-house adoption
1,000+ Assets monitored
35+ Operational, tactical and strategic use cases

Customer Case Study

XMPro enabled the in-house engineering team at a major North American miner to independently compose 35 operational, tactical and strategic solutions across six sites, scaling to monitor and manage over 1,000 diverse critical assets.

"XMPro successfully triggered a real predictive maintenance alert for a Haul Truck that appears to have a Strut issue - This was particularly impressive, considering we have only deployed the development environment a few weeks ago"

-- Advanced Predictive Maintenance Lead, major global mining company

AUTONOMOUS OPERATIONS

Now pushing the frontier.

MAGS agents are achieving what no other industrial platform has demonstrated — sustained autonomous operations at enterprise scale.

15+
Days Autonomous
Safety-critical petrochemical operations
3-5+
Agents Per Team
Specialized agents coordinating per use case
50+
Teams Deployable
Scale across sites and business units
100%
Governed
Every agent, every decision, every action, auditable

SCOPE FOR YOUR SITE

Let’s scope this for your operation.

Talk to an XMPro engineer about your environment, your starting HAS level and the lever that matters most — or browse more solutions.