Refining margins move with crack spreads, and when spreads narrow, operational efficiency decides which plants stay competitive. Margins fell sharply through 2024, and S&P Global Commodity Insights expected the pressure to continue into 2025, pushing refiners toward hard decisions about capacity.
Energy is among the largest operating costs on every barrel, so higher fuel, steam and power costs cut deeper when margins are thin. Downstream operators that apply industrial AI to energy management, real-time operations, supply chain, safety and maintenance can protect margin while meeting reliability and emissions targets. The five areas below reinforce one another, so a gain in one tends to support the next.
Energy management
Much of a refinery's energy use is set by a handful of operating choices: furnace firing, column temperature profiles, pumparounds, reflux and side-draw balance. Every megawatt saved on them goes to the bottom line.
Machine learning models trained on years of historian data and live sensor streams can tune those variables continuously. Running in closed loop, the controller writes setpoints to the distributed control system (DCS) in real time, inside limits set by plant engineers, instead of waiting for the next manual adjustment. Because it learns from the plant's own data, it keeps adjusting as feed and ambient conditions change.
Real-time process control
Fuel, steam, power and flaring all rise when setpoints drift from their economic optimum. Machine-learning-enhanced advanced process control (APC) adds data-driven optimization on top of the existing control layers, learning from historian data to hold units at energy-optimal conditions.
Whether it writes setpoints directly or advises operators, the optimizer anticipates feed swings and ambient changes and reduces variability and energy intensity. Nonlinear dynamic models, placed within the established control hierarchy, capture interactions between variables and constraints that linear APC models approximate poorly.
Multi-objective optimization balances energy, product quality and emissions costs, so margin improves without giving up compliance. Because the optimizer works on top of existing controls, it fits within established safety governance. Operators keep override authority, and the system holds the plant closer to its economic optimum across shifts.
Supply chain and logistics
Flat U.S. fuel demand is tightening working capital across terminals and retail networks, which makes downstream forecasting more important. Machine learning models take in sales history, terminal liftings, macroeconomic indicators, weather and local events to predict demand by grade with less manual tuning than spreadsheets.
With clearer visibility of expected drawdowns, planners can set safety stocks and reorder points that keep stations supplied without tying up excess product, and reduce the risk of run-outs across the distribution network.
Logistics optimization builds on those forecasts:
- Scheduling selects loads, routes and timing across marine, rail, pipeline and truck.
- Constraint handling respects driver-hour limits, hazmat regulations and berth availability.
- Integration with existing dispatch systems keeps the plan practical.
- Cost control reduces empty miles, detention fees and demurrage.
Closer to the refinery, blending optimizers reduce octane and sulfur giveaway, dynamic routing sends finished barrels to the most profitable terminals, and live tanker arrival estimates let the refinery adjust unit rates before ships reach the dock. Together these give faster response to prices, leaner inventories and fewer stockouts as demand volatility, travel patterns and product-grade requirements change.
Safety and risk
Safety is the boundary downstream operations never cross, yet in complex systems hazards are hard to see until they become problems. Models trained on sensor data, historian tags, work orders and incident logs learn normal behavior and flag early changes.
Computer vision and robotics extend that monitoring across the site:
- Drones inspect tank roofs for corrosion and ground robots scan connections for leaks, reducing confined-space entries and hot work.
- Analytics on near-miss reports surface systemic precursors, such as repeated permit deviations, so procedures can be refined in line with API RP 754.
- Real-time monitoring of emissions and flare operations supports compliance.
When vibration patterns shift on critical pumps or pressure differentials trend upward in columns, anomaly detection can raise an alert hours before a trip. That allows a controlled intervention instead of an emergency shutdown.
Predictive maintenance
Unplanned outages are among the most expensive events in a refinery. Moving from fixed-interval checks to machine learning condition monitoring detects early warning signs and lets repairs be scheduled before a fault becomes a shutdown.
Models compare vibration, temperature, pressure, flow and electrical signals against learned healthy patterns and flag anomalies hours or days before a trip. Digital twins let teams test what-if repairs or loading scenarios without touching live equipment. The assets that benefit most include:
- rotating equipment: pumps, compressors and turbines
- heat exchange networks: condensers and coolers
- fired equipment: furnaces and heaters
- electrical systems: motors, drives and switchgear
Across these assets, condition monitoring raises overall equipment effectiveness and availability, and keeps production flowing when markets are tight.
How Imubit applies this
Imubit applies Map, Model and Control to energy, margin and reliability in refining. Map traces plan-to-plant gaps to the recurring decisions behind them. Model uses Deep Learning Process Control® (DLPC®) to learn a nonlinear dynamic model of each unit from its own history, guided by first principles. Control takes the critical decisions to closed loop, writing setpoints to the existing APC or DCS minute by minute, inside limits set by plant engineers. In refining, this has delivered a $0.25/bbl margin improvement.
