Emerson and Aramco: Deploying AI to Boost Global Efficiency

2026-04-29 · Source: AI Magazine · Field: Energy & Utilities — Energy Efficiency & Conservation, Traditional Energy & Fossil Fuels, Artificial Intelligence & Machine Learning · Depth: Intermediate, short

Summary

Emerson has successfully deployed an AI-driven optimization solution for Aramco, integrating Emerson's Aspen Hybrid Models into Aramco's global refining operations. This collaboration has created one of the world's largest multi-site, multi-period optimization models, enhancing refinery planning by combining first-principles models with industrial AI. The system has achieved yield and quality prediction accuracy of up to 98.5% in key refinery units like Continuous Catalyst Regeneration (CCR) and Platformer Units. This improved accuracy enables more precise feedstock blending, minimizes gaps between planning and execution, and enhances margin forecasting across Aramco's global network. Future efforts aim to expand this hybrid modeling approach to hydrocracker units, further demonstrating its scalability and potential for broader application in process industries.

Key takeaway

For refining executives seeking to enhance operational efficiency and profitability, Emerson's deployment of Aspen Hybrid Models at Aramco demonstrates a clear path. Implementing similar hybrid AI solutions can significantly improve yield and quality prediction accuracy, enabling more precise planning decisions and reducing manual adjustments across global assets. Consider adopting these advanced models to diversify feedstock selection and uncover new value in complex refining operations.

Key insights

Hybrid AI models combine first-principles engineering with machine learning for highly accurate industrial optimization.

Principles

• Combine domain expertise with AI.
• Calibrate models with actual plant data.
• Automate model updates for efficiency.

Method

Integrate Aspen Hybrid Models into existing planning frameworks, using thousands of converged simulation cases built on rigorous first-principles models calibrated with plant data to create nonlinear optimizations.

In practice

• Optimize feedstock blending.
• Reduce planning-execution gaps.
• Improve margin forecasting accuracy.

Topics

• Industrial AI
• Aspen Hybrid Models
• Refinery Optimization
• Aramco
• Emerson

Best for: Executive, AI Architect, Director of AI/ML, Operations Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Magazine.