Global Chemical Manufacturer (Anonymous)

Steam cracking furnaces are among the highest-value assets in petrochemical production, converting lower-value feedstocks into ethylene, propylene, and other high-value olefins. For this global chemical manufacturer, maximizing furnace run length between decoking cycles is directly tied to output and margin. The company's existing monitoring infrastructure relied on univariate, threshold-based alarms — a fragmented approach that generated reactive alerts rather than early warnings. With no visibility into coking progression rates or impending failures, maintenance teams faced unpredictable outages, inconsistent decoking schedules, and significant lost production across a fleet of 200 furnaces worldwide.

The company partnered with C3.ai to deploy the C3 AI Reliability application on Microsoft Azure, starting with a 12-week pilot on two steam cracking furnaces. The deployment unified two years of historical operational data from seven enterprise systems — including 750 sensors and approximately 180 million rows of time-series data — into a single ML-ready data layer. C3.ai's team trained 24 machine learning models for multivariate failure prediction using advanced anomaly detection techniques, alongside 4 additional models specifically designed to forecast coking progression rates. The solution delivers a 5-day predictive look-ahead, giving operators actionable advance notice to optimize decoking schedules and intervene before failures occur. The architecture was designed from the outset for fleet-wide scaling.

Deploying C3 AI Reliability on two furnaces demonstrated measurable improvement in operational performance, with a clear scaling pathway across the full global fleet:

• +10 days average furnace run length (from 40 days to 50 days between decoking cycles)
• +1.4% improvement in furnace utilization
• $45M+ projected annual economic benefit at full deployment across 200 furnaces

Beyond the metrics, operators gained near real-time visibility into both asset health and coking progression — a qualitative shift from reactive alarm management to proactive schedule optimization. The platform's architecture enables consistent model deployment across the global furnace fleet without rebuilding from scratch at each site.

• Multivariate ML outperforms threshold alarms for continuous chemical processes — combining failure prediction with coking rate forecasting enables both reactive alerting and proactive scheduling in a single workflow.
• Data unification is the prerequisite: integrating 7 enterprise systems, including sensor historians, maintenance records, and engineering drawings, was foundational to model accuracy — not an afterthought.
• Design for scale from day one: a 12-week, 2-furnace pilot validated the approach, but the $45M value case only materializes when the architecture can replicate consistently across 200 assets.
• Pilot scope matters: starting with the highest-criticality asset class (steam crackers) ensures the business case is real before committing to fleet-wide rollout.