Emerson & Aramco: Using AI to Improve Refining Efficiency Emerson has introduced an advanced AI-powered optimisation solution for Aramco, one of the world’s leading integrated energy and chemicals companies, in a move designed to strengthen efficiency across global refining operations. At the core of the initiative is the integration of Emerson’s Aspen Hybrid Models into Aramco’s refinery planning systems, forming one of the most extensive multi-site, multi-period optimisation frameworks in the energy sector. By combining first-principles engineering models with industrial AI and deep operational expertise, the solution captures complex relationships in yield and product quality. This has enabled prediction accuracy of up to 98.5% in critical refinery units, supporting more reliable and data-driven decision-making across refining assets. Expanding AI in refining operations Aramco has deployed the hybrid models across Continuous Catalyst Regeneration (CCR) and Platformer Units, where they are enhancing feedstock blending strategies, tightening alignment between planning and execution and improving margin forecasting across its global refining portfolio. The company is now extending the approach to hydrocracker units, a step expected to further improve predictive accuracy while demonstrating how scalable AI-led optimisation can support increasingly complex energy systems. "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 implementation delivers measurable gains across refinery performance. High prediction accuracy is helping to increase yields and improve product quality across varied feedstocks, operating conditions and throughput levels. At the same time, optimised blending strategies are enabling more flexible feedstock selection, supporting both profitability and sustainability objectives. Improving efficiency across energy assets