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.

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

Manenti F.
2024-01-01

Abstract

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.
2024
Computer Aided Chemical Engineering
9780443288241
Data-driven
Machine learning
Predictive maintenance
Thermodeasphalting
Used oil
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1272634
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