Every refinery runs against the same arithmetic: feedstock discounts swing wildly, energy costs keep climbing, and cracks thin out as crude price volatility erodes whatever heavy-crude advantage the economics team built into the plan. LP models update monthly, APC configurations get tuned quarterly, and crude quality can shift two or three times in between. Maintenance backlogs add drag, and fired heaters consume the largest share of the site’s total energy budget.

Industrial processing plants that have adopted AI have reported 10–15% production increases alongside 4–5% EBITA improvements, and the cumulative margin lift across crude selection, utilities, and blending can decide whether a unit runs or shuts.

Industrial AI offers a practical way to close that gap by learning directly from a site’s own historian, lab, and market data in real time. Rather than relying on static planning models and periodic tuning, AI models continuously adapt. The result is sustainable profit improvements across three high-impact areas of the crude oil refining process.

TL;DR: How AI Improves the Crude Oil Refining Process

AI optimization targets the crude oil refining process at its most margin-sensitive points. It delivers measurable value even before full automation.

Crude-Slate, Energy, and Yield Applications

  • AI models trained on a site’s historian data forecast how each cargo affects downstream units. This allows real-time cut-point and feed-ratio adjustments.
  • Reinforcement learning controllers can reduce fired-heater fuel intensity by optimizing air-to-fuel ratios, coil-outlet temperatures, and draft.
  • Predictive models capture nonlinear interactions across reactors and blending headers, maximizing high-value output.

From Advisory Mode to Closed Loop

  • Plants typically start in advisory mode, validating recommendations against operator judgment before enabling closed loop control.
  • Each stage delivers standalone value; crude-slate, energy, and yield improvements compound when coordinated at the plant level.

Here’s how each application works in practice.

1. Mastering Crude-Slate Variability

Every crude shipment arrives with its own fingerprint of sulfur, metals, and boiling-point curves, and those differences can throw an LP plan off within hours. When geopolitical shifts shuffle cargo flows, feed quality swings even wider, leaving operators to chase targets while margins erode.

The operational burden compounds quickly: every new blend forces fresh cut-point estimates, catalyst adjustments, and risk of off-spec product. Without end-to-end visibility, the lack of coordinated optimization leads to missed yield opportunities, especially when discounts on heavy or sour crudes narrow unpredictably. The result is a trade-off most refinery teams know well. Run conservatively and sacrifice profit, or push constraints and pay for reblends, lost throughput, or unplanned shutdowns.

Industrial AI can eliminate that trade-off. Models trained on years of a site’s historian data, sample results, and spot-price curves forecast how a cargo’s properties will ripple through every downstream unit. The software then continuously recalculates optimal cut points, feed ratios, and hydrogen usage as new lab data streams in, writing setpoints back to the DCS in real time. By the time a new LP vector is published, operating conditions may have shifted two or three times, leaving operators to interpolate between outdated targets. AI closes that gap by recalculating continuously rather than waiting for the next planning cycle.

A Four-Stage Deployment Path

The deployment path typically follows four stages.

  • First, gather and clean historian, sample, and LP data for at least one representative operating year.
  • Then train and stress-test the model on historical crude changes and price scenarios.
  • Next, validate recommendations in advisory mode while operators compare outputs against their own best practice: does the model’s cut-point suggestion match what the most experienced board operator would do?
  • Finally, grant closed loop control once the model proves it can hold constraints and improve profits reliably.

Advisory mode alone delivers real value here. Operators gain a consistent, data-driven second opinion on every crude switch. This reduces shift-to-shift variability and improves cross-functional alignment between planning and operations before any closed loop automation is enabled. That same four-stage progression, from data collection through advisory validation to closed loop control, applies across energy and yield applications as well.

2. Reducing Fired-Heater and Utilities Energy Costs

Energy is typically the single largest variable cost a refinery wrestles with, and fired heaters consume the biggest share. That concentration makes them the most powerful lever for shrinking fuel bills and direct CO₂ emissions. But the opportunity extends beyond heaters. Steam networks, cooling-water systems, and hydrogen plants all interact with heater performance, so a reduction in firebox duty can cascade into savings across the utilities envelope. BCG research found that core business functions, not support functions, are where AI generates the majority of its value, and energy-intensive refining operations fit that pattern.

Yet traditional optimization still relies on manual adjustments or rule-based advanced process control that gets revisited only during scheduled tune-ups. The consequence is familiar to anyone who’s watched a heater after a crude switch: operators run conservatively, combustion drifts, and the site pays for wasted fuel while static control configurations take hours to catch up.

Industrial AI changes the tempo. Reinforcement learning controllers trained on years of a plant’s own historical data, sample results, and live sensor feeds learn how air-to-fuel ratio, coil-outlet temperature, draft, and feed composition interact under real operating conditions. Once validated, the model writes optimal setpoints back to the DCS in real time, always within safety constraints the operations team defines. And because the controller keeps learning, it adapts when crude quality shifts or burners age, something static models weren’t designed to handle. The result is lower fuel intensity, steadier heater duty, and fewer excursions that threaten product quality or environmental compliance.

What the Advisory Phase Reveals

The advisory phase for energy applications often surfaces unexpected value beyond the AI model itself. Data-quality gaps in historian tags and stack-gas analyzers typically emerge during validation, and resolving those gaps improves the site’s broader instrumentation strategy. It’s not uncommon for the data cleanup alone to change how operators think about measurement reliability. Once closed loop control is active, weekly KPI reviews confirm that savings hold as operating conditions evolve.

3. Maximizing High-Value Product Yields

Maximizing gasoline, jet fuel, and diesel yield from every barrel processed flows straight to the bottom line, yet the constraint is complexity. Thousands of variables, including feed composition, reactor severity, catalyst age, and downstream blending economics, shift together in nonlinear ways that traditional LP models and APC weren’t built to capture. Anyone who’s tuned a fluid catalytic cracker or reformer knows the problem: catalyst deactivation introduces a drift that static models ignore until the next scheduled update, often leaving margin on the table for weeks.

Industrial AI addresses this by training models on years of a plant’s sample results, historian tags, and market data, building a plant-specific digital representation far more faithful than any static LP. These models learn the nonlinear relationships between reactor conditions and product quality, including gasoline octane, diesel cetane, and sulfur slip. When predictive models feed a global optimization solver, the refinery can move beyond unit-level targets to an economic optimum recalculated minute by minute. The optimizer considers the entire product pool simultaneously. This reduces quality giveaway by aligning unit-level setpoints to system-wide objectives rather than treating each unit as an independent problem.

Transparency matters here, because operators won’t trust a model that proposes moving reactor severity or blend recipes without explaining why. Modern AI optimization platforms expose performance dashboards, constraint monitoring, and scenario analysis so operators can see exactly what’s driving a recommended setpoint change. By embedding first-principles relationships such as mass balance, energy conservation, and unit-specific safety limits, the technology supports human-AI collaboration rather than asking operators to take the model’s word for it.

Coordinating Across Units

Plants that start with advisory mode on a single high-value unit, such as the FCC or reformer, can validate the model’s decision-making process against experienced operators before expanding scope. As trust develops, coordinating cut points and blend recipes across units captures margin that isolated optimization misses. Quality giveaway is a good example: when each unit optimizes independently, the reformer may produce octane well above the blend target while the FCC overshoots on sulfur removal. A plant-wide optimizer aligns both units to meet spec with minimal excess, converting that giveaway into additional high-value product or reduced severity that extends catalyst life. And because crude-slate adjustments, energy savings, and yield improvements are fundamentally interconnected, refinery sites that address all three tend to see compounding returns that exceed the sum of individual applications.

Putting AI to Work in Your Refinery

For refinery operations leaders seeking sustainable margin and emissions improvements, Imubit’s Closed Loop AI Optimization solution offers a data-first approach grounded in real-world plant operations. The Industrial AI Platform learns from site-specific data and continuously adapts to changing conditions, while the Value Sustainment service tracks realized dollars every month. Workforce Transformation builds internal capability so operators and engineers can extend the technology’s impact across the organization. Plants can start in advisory mode and progress toward closed loop optimization at their own pace, capturing value at every stage of the journey.

Schedule a complimentary assessment to quantify your refinery’s optimization potential before volatility erodes another dollar of margin.

Frequently Asked Questions

What data readiness does a refinery need before starting AI optimization?

Most refineries already have the foundation: two or more years of historian data, routine lab samples, and LP model outputs. The initial phase focuses on cleaning and validating these existing records rather than installing new infrastructure. Sites with gaps in key process measurements often discover and resolve those gaps during the data-preparation stage, which improves broader operational visibility regardless of the AI project’s outcome.

How does AI optimization handle crude grades the model hasn’t seen before?

When a new or unfamiliar crude enters the system, the model draws on compositional similarities to historical cargoes and their documented effects on downstream units. Advisory mode is particularly valuable during these transitions: operators review AI recommendations against their own experience with similar feedstocks, and the model incorporates actual performance data from the new crude to strengthen its predictions over time.

Can AI yield optimization work alongside existing LP and APC systems?

AI optimization complements, rather than replaces, existing planning and control infrastructure. LP models continue to set weekly or monthly targets, while the AI layer recalculates setpoints between planning cycles as conditions shift. APC systems handle regulatory control at the loop level, and the AI optimizer coordinates across those loops to pursue plant-level objectives. The result is tighter alignment between planning intent and real-time execution without requiring a rip-and-replace of current systems.