Emerson and Aramco: Deploying AI to Boost Global Efficiency

Emerson has successfully deployed an AI-driven optimisation solution for Aramco, one of the world's leading integrated energy and chemicals companies, marking a significant advancement in industrial AI applications.

The collaboration integrates Emerson's Aspen Hybrid Models into Aramco's existing refinery planning framework, creating one of the world's largest multi-site, multi-period optimisation models.

The deployment demonstrates how combining first-principles models with deep domain expertise and purpose-built industrial AI can capture nonlinear relationships in yield and quality responses, enhancing the accuracy of refinery planning models.

It has achieved yield and quality prediction accuracy of up to 98.5% in key refinery units.

Scaling AI-driven optimisation

Aramco has implemented the hybrid AI models in Continuous Catalyst Regeneration (CCR) and Platformer Units, enabling more precise feedstock blending, minimising gaps between planning and execution and improving margin forecasting accuracy across Aramco's global refining network.

Current efforts focus on expanding the hybrid modelling approach to hydrocracker units, which could further enhance model accuracy and demonstrate the scalability of this AI-driven optimisation strategy.

"This deployment represents a significant milestone in Aramco's AI strategy and our long-standing relationship with Emerson," says Ahmad Alkudmani, Director of the Global Optimiser Department at Aramco.

"We are committed to leveraging innovative technologies for smarter, more efficient refining optimisation.

"With improved model accuracy, we are enhancing planning decisions, reducing manual adjustments and uncovering new value across our global assets."

The Aspen Hybrid Models implementation delivers several key benefits.

Prediction accuracy of 98.5% substantially increases yield volume and enhances stream quality across diverse feedstocks, operating conditions and throughputs.

The system enables optimised feedstock blending, diversifying feedstock selection and blending recipes for more profitable and sustainable operations.

Reducing planning and execution gaps

The solution addresses a critical challenge by minimising discrepancies between plans and actual plant performance, reducing the need for manual adjustments.

Enhanced model accuracy captures complex non-linearities in critical unit operations such as reactors, while improved operational efficiency comes from automating model updates and reducing manual tuning requirements.

The scalable solution maintains model applicability across a wide range of refinery operations worldwide.

Aramco is using Aspen Hybrid Models built and deployed in Emerson's AspenTech Performance Engineering and Manufacturing and Supply Chain product suites.

This approach has enabled Aramco to create highly-accurate nonlinear optimisations using thousands of converged simulation cases built upon rigorous first-principles models calibrated with actual plant data.

The deployment illustrates how industrial AI applications are evolving beyond traditional automation, combining machine learning capabilities with established engineering principles to address complex operational challenges.

Combining domain expertise with AI

The success in CCR and Platformer Units provides a blueprint for similar applications across other refinery operations, demonstrating the broader applicability of hybrid AI models in process industries.

"Aramco continues to set the standard for operational excellence through digital innovation," adds Claudio Fayad, CTO of Emerson's Aspen Technology business.

"This deployment of AI-driven Aspen Hybrid Models to optimise complex, multi-site, multi-period planning workflows demonstrates the tangible value of combining deep domain expertise with advanced AI. 

"We're excited to expand our strategic relationship with Aramco as they advance their digital transformation goals."

The collaboration between Emerson and Aramco demonstrates the potential for AI-driven solutions to transform refinery planning and operations.

As the technology expands to additional units and sites, it could set new benchmarks for operational efficiency in the global refining industry, showing how hybrid models can bridge the gap between theoretical planning and practical execution in complex industrial environments.