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AI-Powered Conversion Optimization for a Leading European Downstream Operator

~2%

increase in Conversion Efficiency
Enhanced operational stability resulting in higher throughput
Better catalyst management reducing waste
Driving bottom line impact

Business Challenge​

A major European downstream oil & gas operator sought to improve conversion efficiency and operational reliability within its LC-FINING unit, which processes heavy feedstock into lighter, high-value products. Despite sophisticated DCS systems and historical data, the plant faced difficulties in detecting early performance deviations due to variability in feed composition, catalyst fluctuations, and changing process dynamics. The lack of real-time correlation between key parameters—such as viscosity, reactor firing, and feed pump behavior—and the absence of a structured optimization approach limited their ability to consistently maximize yield and maintain process stability.

Solutions Deployed

  • A remote monitoring framework was deployed, enabling real-time visibility into process behavior flagging developing anomalies, recommending corrective actions ahead of time.
  • ML-Based Conversion Optimization Engine – ML models were trained on DCS data, lab reports, and operating history to predict:
    • Short Heavy Fraction Tolerance (SHFT), Feed Operability Index, Conversion potential under multiple operating constraints (e.g., reactor exotherm, catalyst availability)
  • Real-Time Dashboard with live insights into:
    • Feed integrity and reactor behavior
    • Catalyst performance and SHFT trends
    • Optimization targets and operating recommendations
  • Correlated Alerting System – Instead of isolated alarms, alerts were configured based on the combined movement of multiple process KPIs, enabling faster identification of pattern-based anomalies.
  • What-If Simulation & Decision Support – During steady-state periods, the system proposed setpoint changes using real-time model outputs, allowing operators to take proactive decisions grounded in data and business logic.

Benefits

  • ~2% increase in conversion efficiency, driving significant bottom-line impact
  • Faster anomaly detection and resolution, reducing unplanned variability and interventions
  • Enhanced operational stability, resulting in consistent product quality and throughput
  • Better catalyst management, reducing waste and improving overall asset performance