Application of a Predictive Maintenance Strategy Based on Machine Learning in a Used Oil Refinery

The Itelyum Regeneration used oil re-refining plant in Pieve Fissiraga currently employs a condition-based maintenance strategy for its thermodeasphalting (TDA) section, particularly focusing on the TDA T-401 column. This strategy involves monitoring the real-time pressure differential (ΔP) between the column's top and bottom, which increases in time due to fouling phenomena. Maintenance is scheduled when ΔP exceeds a predetermined empirical threshold, ensuring that the T-401 column operates within normal operations limits. However, this approach has limitations with non-conventional used oils. To address this, a data-driven machine learning algorithm, previously successful in predicting key performance indicators of the PH-401B furnace in the TDA section, was applied to the T-401 column datasets. This algorithm, based on Gaussian Process Regressions, effectively predicts the evolution of ΔP and reduces the time during which T-401 operates in suboptimal conditions. The implementation of this machine learning approach marks a significant improvement in the maintenance strategy, shifting from a static, condition-based approach to a dynamic, predictive one, thus ensuring more efficient and reliable operations, even with non-conventional used oil.