Refineries run on tight margins, and every percentage point of yield matters. You’re managing complex units — distillation, hydrotreating, reforming, cracking — each with its own constraints, sensitivities, and operational risks. Feedstock variability, equipment aging, energy costs, and shifting product demand all add pressure. Traditional optimization models struggle to keep up with real‑time conditions. An AI‑driven refinery optimization capability helps you adjust operating parameters with more precision so you can improve throughput, reduce energy consumption, and increase product yield.
What the Use Case Is
Refinery process optimization uses machine learning to analyze real‑time process data, predict unit behavior, and recommend optimal operating conditions. It sits between process engineering, control room operations, and planning. You’re giving your teams a decision support layer that updates as new temperature, pressure, flow, and composition data comes in.
This capability fits naturally into daily operations. Engineers review AI‑generated recommendations during shift handovers. Control room operators monitor real‑time alerts that flag inefficiencies or off‑spec trends. Planning teams use the insights to adjust crude blends or product targets. Over time, the system becomes a shared operational lens that helps everyone run the refinery more efficiently.
Why It Works
The model works because it captures nonlinear relationships that traditional process models can’t. Unit performance is influenced by feedstock quality, catalyst activity, equipment condition, and operational decisions made minute by minute. AI models can ingest these signals continuously and surface patterns that indicate inefficiencies, fouling, or off‑spec risks.
This reduces friction across teams. Instead of relying on manual interpretation of process trends, everyone works from the same data‑driven recommendations. It also improves throughput by helping operators maintain optimal conditions more consistently. The result is higher yield, lower energy consumption, and fewer unplanned adjustments.
What Data Is Required
You need structured process and operational data. DCS and historian data — temperatures, pressures, flows, compositions, and energy consumption — form the backbone. Lab results, catalyst activity logs, and feedstock assays add deeper insight into unit behavior. Maintenance histories and equipment performance data help the model understand constraints.
Real‑time data is essential. Process deviations can develop quickly, so the model must ingest fresh telemetry from critical units. You also need metadata such as crude blend details, unit configurations, and operating limits. Data freshness matters because refinery conditions shift throughout the day.
First 30 Days
The first month focuses on scoping and validating the data needed for a reliable pilot. You start by selecting a single unit — often crude distillation, hydrotreater, or reformer — with strong historical data and consistent instrumentation. Data engineers clean historian records, reconcile lab results, and align operational events with known performance issues. You also define the optimization goals that matter most: yield, energy efficiency, or product quality.
A pilot model is trained and benchmarked against historical performance. Engineers review the recommendations during daily operations to compare them with actual decisions made in the past. Early wins often come from identifying energy inefficiencies or off‑spec risks that operators previously caught too late. This builds confidence before integrating the system into live operations.
First 90 Days
By the three‑month mark, you’re ready to integrate the model into real‑time refinery workflows. This includes automating data ingestion, setting up dashboards, and creating alert thresholds for inefficiencies or off‑spec conditions. You expand the pilot to additional units and incorporate more granular data sources such as catalyst activity or crude assay variability.
Governance becomes essential. You define who reviews recommendations, how operators respond to alerts, and how exceptions are handled. Cross‑functional teams meet weekly to review performance metrics such as yield improvement, energy reduction, and advisory accuracy. This rhythm ensures the capability becomes part of the operational fabric rather than a standalone analytics tool.
Common Pitfalls
Many operators underestimate the importance of clean historian data. Sensor drift, missing intervals, or inconsistent lab results can degrade model accuracy. Another common mistake is ignoring feedstock variability. Without crude assay data, the model may misinterpret normal shifts as inefficiencies.
Some teams also deploy the system without clear decision‑making workflows. If operators don’t know when or how to act on recommendations, adoption slows. Finally, refineries sometimes overlook the need for continuous updates. A model that isn’t retrained regularly can fall behind as catalysts age or feedstock mixes change.
Success Patterns
The operators that succeed treat refinery optimization as a collaborative capability. They involve process engineers and control room operators early so the model reflects real‑world conditions. They maintain strong data hygiene, especially around historian data and lab results. They also build simple workflows for reviewing and acting on recommendations, which keeps the system grounded in operational reality.
Successful teams refine the model regularly and incorporate new data sources as they become available. Over time, the capability becomes a trusted part of refinery operations, improving yield, reducing energy costs, and strengthening product quality.
A strong refinery optimization capability helps you run tighter, cleaner, and more predictable operations — and those gains show up directly in margin performance across every barrel you process.