Emerson has announced the successful deployment of an AI-driven optimization solution for Saudi Aramco, aimed at improving refinery yield, quality, and operational efficiency across its global operations.
At the core of the collaboration is the integration of Emerson’s Aspen Hybrid Models™ into Aramco’s refinery planning framework, enabling the creation of one of the world’s largest multi-site, multi-period optimization models. The AI-powered solution combines first-principles engineering models with industrial AI to better capture complex, nonlinear relationships in refinery processes.
The deployment has already demonstrated significant impact, achieving up to 98.5% accuracy in yield and quality predictions across key refinery units. These models are currently being applied in Continuous Catalyst Regeneration (CCR) and Platformer Units, enabling more precise feedstock blending, improved margin forecasting, and reduced gaps between planning and execution.
“This deployment represents a significant milestone in our AI strategy, helping enhance planning decisions and unlock new value across our global assets,” said Ahmad Alkudmani, Director of the Global Optimizer Department at Aramco.
Aramco is now expanding the use of hybrid AI models to hydrocracker units, further scaling the solution across its refining network.
Ahmad Alkudmani highlighted the operational benefits, noting that improved model accuracy reduces the need for manual intervention while enhancing decision-making across complex refining environments.
Claudio Fayad, Chief Technology Officer of Emerson’s Aspen Technology business, said the deployment reflects the growing value of combining domain expertise with advanced AI. He added that the collaboration demonstrates how AI-driven optimization can transform complex, large-scale industrial workflows.
Key benefits of the deployment include:
- High prediction accuracy: Up to 98.5% accuracy in yield and quality forecasting
- Optimized feedstock blending: Improved profitability and operational flexibility
- Reduced planning gaps: Better alignment between planning models and real-world performance
- Enhanced efficiency: Automated model updates and reduced manual tuning
- Scalability: Applicability across multiple refinery sites and operating conditions
By leveraging Aspen Hybrid Models within Emerson’s broader AspenTech suites, Aramco has developed a scalable and robust optimization framework built on thousands of simulation scenarios calibrated with real plant data.
The collaboration underscores a broader trend of AI adoption in the energy sector, where companies are increasingly using advanced analytics and automation to drive efficiency, sustainability, and operational excellence at scale.
