Machine learning-driven soft sensor implementation for real-time fault detection in CDU of oil refinery

Soft sensors in oil refineries provide operators with important insights into the behavior and performance of processes using real-time and historical data to generate predictions. This data-driven strategy makes it easier to make wise decisions for detecting faults, thus improving process optimization and control. The Crude Distillation Unit (CDU) imposes very harsh working environments for measuring instruments, imposing both the use of a very robust sensory system and periodic maintenance procedures, which are time-consuming and costly. Notwithstanding such precautions, faults in those measuring devices, such as temperature and pressure sensors, still occur, and the presence of a sensor fault deteriorates the efficiency, productivity, and reliability of the refinery process. Recent works focused only on some fault types (e.g., bias and drift), ignoring others. This study presents the design of a soft sensor to detect all possible fault types in the real-time processing of an oil refinery. This method used actual data collected from the Salahuddin oil refinery in Iraq, several preprocessing methods, and a machine-learning approach. The proposed soft sensor was designed using several stages, including data collection, preprocessing, clustering, and classification. In the classification stage, an approach based on a Bagged Decision Tree (BDT) and Support Vector Machine (SVM) was implemented to classify the detected faults. The proposed soft sensor was trained and tested using actual data, achieving a high fault detection and classification result of 99.96%.

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