As refineries face increasing complexity, ageing control systems, and growing sustainability pressures, traditional control methods are struggling to keep pace. Static models and manual tuning often fall short in dynamic, non-linear environments, limiting both efficiency and responsiveness.
This article explores how AI-driven autonomy, using reinforcement learning, enables a shift from rule-based control to continuous, adaptive decision making. Unlike traditional approaches, these systems learn directly from real-time operations, adjusting to changing conditions without the need for constant reconfiguration.
Tested in live plant environments, this approach has demonstrated measurable impact, including significant reductions in energy use, lower emissions, improved process stability, and reduced operator burden. In one case, autonomous control of a distillation column delivered sustained performance improvements while maintaining product quality and operational reliability.
By embedding AI into core operations, refineries can move beyond incremental optimisation toward more resilient, efficient, and sustainable systems. This marks a fundamental shift from reactive control to predictive, system-level intelligence.
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This article originally appeared in PTQ via Digital Refining.