Refiners rely on digital twins to support planning, optimization, and emissions reduction. But as operations become more dynamic and interconnected, maintaining model accuracy has become a growing challenge. Even well-built simulation models, often developed in first-principles, physics-based process simulation technology like Petro-SIM®, drift from reality over time due to feed variability, equipment aging, and data inconsistencies.
This drift rarely shows up as failure. It accumulates quietly, eroding optimization performance, increasing energy use, and reducing confidence in model-driven decisions. Over time, planning models begin to reflect assumed rather than achievable performance. In many cases, this is accepted as operational noise rather than recognized as a structural issue.
A new generation of digital twin technology is addressing this challenge through automated lifecycle management. By integrating simulation, machine learning, and cloud computing, these systems monitor model health and update in near real time. Within environments such as the KBC Acuity™ Process Twin Pro, this creates an ongoing feedback loop, keeping models aligned with live plant conditions.
This article explores: