How Emerson and Aramco Use AI to Boost Refining Efficiency
Artificial intelligence is rapidly reshaping how oil refineries operate, shifting plants from reactive troubleshooting to real-time optimisation. Collaborations between technology providers like Emerson and major operators such as Aramco show how AI can make complex refining assets safer, cleaner, and more profitable. This article breaks down the core concepts, tools, and workflows behind AI‑driven refining improvements, without relying on proprietary project details. You’ll get a practical view of what’s changing on the control room screen and out in the field.
Why AI Matters in Modern Refining
Refineries are some of the most complex industrial systems in the world. Thousands of control loops, volatile feedstock qualities, tight environmental constraints, and razor-thin margins leave very little room for error. Traditional automation and control systems have done a strong job of keeping plants stable and safe, but they were not designed to continuously extract every last drop of efficiency.
Artificial intelligence (AI) and advanced analytics change this equation. Instead of relying only on fixed control logics and periodic human interventions, AI models can scan vast data streams in real time, detect subtle patterns, and propose or even implement changes that improve yields, reliability, and energy performance. Collaborations between industrial technology providers such as Emerson and global refiners like Aramco illustrate how this shift is moving from pilot projects to mainstream practice.
From Automation to Autonomy: The Refinery Control Evolution
To understand the impact of AI, it helps to see where refining automation has come from. Over the past few decades, refineries have typically followed this progression:
- Basic control: Single-loop controllers, manual setpoints, and limited visibility across units.
- Distributed control systems (DCS): Centralised monitoring, alarms, and consistent control logic across the plant.
- Advanced process control (APC): Model-predictive controllers adjusting dozens of variables together to keep operations near constraints.
- Real-time optimisation (RTO): Economic models suggesting where to run processes for maximum margin.
- AI-enhanced operations: Machine learning and hybrid models that learn from historical and live data to support or automate decisions.
AI doesn’t replace control systems like DCS or APC; it layers on top of them. Technology companies such as Emerson typically integrate AI modules into existing automation platforms, while operators like Aramco bring the domain knowledge, operating discipline, and massive data sets needed to train and validate models.
Key AI Use Cases in Refineries
While every refinery is unique, most early AI wins tend to cluster around a few high‑impact use cases.
1. Predictive Maintenance for Critical Equipment
Unplanned downtime on compressors, pumps, heat exchangers, and fired heaters can erase weeks of margin in a single incident. AI-driven predictive maintenance uses data from vibration sensors, temperatures, flows, and acoustic signals to estimate the probability of failure before it happens.
- Machine learning models detect deviations from normal behaviour long before they trigger alarms.
- Operators can schedule maintenance during planned turnarounds instead of reacting to breakdowns.
- Spare parts and contractor support can be aligned with actual predicted needs.
2. Yield and Product Quality Optimisation
Small improvements in yield across crude distillation, catalytic cracking, hydroprocessing, and reforming units translate directly into millions of dollars per year. AI models can:
- Correlate feedstock properties, operating conditions, and product specs with actual yields.
- Suggest setpoint changes that lift valuable product output while staying within constraints.
- Help refineries adapt faster to changing crude slates or product demand patterns.
3. Energy and Emissions Management
Energy is one of the largest operating costs in a refinery. Fired heaters, boilers, and hydrogen plants all consume significant fuel, while emissions limits tighten year by year. AI-enhanced energy management can:
- Optimise furnace firing to reduce fuel use for the same heat duty.
- Balance steam networks to minimise venting and let-down losses.
- Identify operating modes that lower CO₂ and NOx emissions without sacrificing throughput.
4. Anomaly Detection and Process Safety
Safety and reliability remain non-negotiable. AI tools can monitor thousands of tags simultaneously to detect abnormal situations that might be invisible to individual alarms. Early detection of fouling, leaks, or off‑spec recycles helps operators intervene before small issues cascade into incidents.
How AI Models Fit with APC and RTO
Many refineries already use advanced process control (APC) and real-time optimisation (RTO). AI complements these established tools rather than replacing them.
| Capability | APC/RTO | AI/Machine Learning |
|---|---|---|
| Primary focus | Maintain operation near constraints, optimise economics | Discover patterns, predict behaviour, classify anomalies |
| Model basis | First-principles & linear dynamic models | Data-driven and hybrid models |
| Update frequency | Re-tuned periodically by specialists | Can be re-trained as new data accumulates |
| Operator interaction | Setpoints and constraints managed from control system | Recommendations, diagnostic insights, risk scores |
In many Emerson-style architectures, AI models run in parallel with existing controllers, providing recommendations, soft sensors, or health indices. Over time, as confidence grows, some recommendations can be automated under strict governance, with operators retaining override authority.
Digital Twins: A Sandbox for Safer Optimisation
Digital twins—virtual replicas of processing units or entire refineries—are one of the most powerful enablers of AI in operations. By combining first-principles process models with live plant data, digital twins let teams test scenarios without touching the real equipment.
In a typical refinery deployment:
- Historical operating data is used to calibrate the digital twin against reality.
- AI models are trained and validated using the twin plus plant data.
- New control strategies or economic scenarios are rehearsed virtually before roll-out.
For a large operator such as Aramco, this approach reduces risk when implementing advanced optimisation strategies across multiple refineries with differing configurations.
Quick Checklist for a Refinery AI Pilot
1) Choose a constrained, high-value unit (e.g., CDU or FCC). 2) Secure high-quality historical data. 3) Define 2–3 measurable KPIs (energy, yield, uptime). 4) Build a digital twin or at least a robust process model. 5) Start with advisory mode before closing the loop to automation.
Data Foundations: Making Refinery AI Possible
AI is only as good as the data it receives. One reason partnerships between automation vendors and operators work well is that both sides contribute critical pieces of the data puzzle.
Core Data Requirements
- Historian and DCS data: High-resolution time-series from process tags, lab results, and events.
- Maintenance data: Work orders, vibration reports, and failure histories from CMMS systems.
- Planning and economics: Crude assays, product prices, and blending constraints.
- Contextual metadata: Equipment tags, process flow diagrams, and operating procedures.
Typical Data Challenges
- Missing or bad-quality sensor data requiring cleansing and reconstruction.
- Differences in tag naming and engineering units across sites.
- Cybersecurity and connectivity constraints between OT and IT networks.
Industrial automation providers often supply secure connectivity layers and edge analytics solutions, while refiners contribute historian access, subject matter experts, and governance over how and where AI models operate.
Step-by-Step: Designing an AI Refining Project
Moving from interest to implementation requires a structured approach. While details vary, most successful refinery AI initiatives follow a similar path.
- Identify business objectives: Align with corporate goals such as margin uplift, energy reduction, or reliability improvements, and quantify the target value range.
- Select a pilot unit: Choose a unit with good data history, clear constraints, and supportive operations leadership.
- Assemble a cross-functional team: Blend process engineers, control engineers, data scientists, IT/OT security experts, and experienced operators.
- Prepare and explore data: Clean historian data, label events, and create initial feature sets for models.
- Build and validate models: Develop predictive or optimisation models and test them against historical scenarios and the digital twin.
- Deploy in advisory mode: Feed recommendations or health scores to the control room without auto-actuation; gather feedback.
- Measure and scale: Confirm KPI improvements, refine governance, and extend the solution to additional units or sites.
Human-in-the-Loop: Keeping Operators Central
AI-enhanced refineries are not “lights-out” plants. Operators remain central to safe and efficient operations; AI simply augments their capabilities.
Effective human-in-the-loop design includes:
- Transparent recommendations: Show why a suggestion is made—key variables, confidence levels, and expected impact.
- Clear override rights: Operators can always reject or postpone AI-driven actions.
- Training and change management: Control room staff and field crews need time and support to adapt to new tools.
- Feedback loops: Operator feedback should feed back into model refinement and rule updates.
In collaborations between global operators and automation suppliers, co-design workshops and joint commissioning help turn AI outputs into dashboards, alarms, and workflows that fit the realities of shift work and local practices.
Benefits and Risks: A Balanced View
Potential Benefits
- Higher on-stream factors and fewer unplanned outages.
- Improved yields of high-value products and reduced quality give‑away.
- Lower specific energy consumption and associated emissions.
- Faster adaptation to changing market demands and crude slates.
Key Risks and Mitigations
- Model drift: Processes change over time; periodic re-training and validation routines are essential.
- Cybersecurity: Strict segmentation, secure protocols, and vendor/operator collaboration protect OT networks.
- Overreliance on automation: Preserving core operator skills and manual procedures guards against rare events.
- Pilot fatigue: Aligning projects with clear business cases avoids “AI for AI’s sake.”
What Collaborations Like Emerson & Aramco Signal for the Industry
When a leading automation provider and a major refining operator join forces around AI, it sends a signal that the technology is moving beyond isolated proofs of concept. It suggests a future in which:
- Refineries treat AI as a standard part of the control and optimisation toolkit.
- Digital twins are maintained throughout the asset lifecycle, not only for design.
- Data scientists and process engineers work side by side as a normal way of operating.
- Best practices, libraries of models, and reference architectures are reused across sites and even across companies.
This doesn’t mean instant transformation. Most plants will progress unit by unit, project by project. However, the direction of travel is clear: AI will play an increasing role in how refineries plan, operate, and maintain their assets.
Final Thoughts
AI in refining is no longer just a buzzword. By combining industrial automation platforms, domain expertise, and large historical data sets, refineries can capture tangible gains in efficiency, reliability, and environmental performance. Collaborations between companies like Emerson and Aramco exemplify how technology providers and operators can work together to move from static control to dynamic, data-driven optimisation at scale.
Editorial note: This article provides a general, non-proprietary overview of how AI can improve refining efficiency, inspired by public reporting on collaborations such as Emerson & Aramco. For the original context, visit Energy Digital.