Machine Learning-Driven Soft Sensor Implementation for Real-Time Fault Detection in CDU of Oil Refinery
Received: 30 November 2024 | Revised: 1 January 2025 | Accepted: 4 January 2025 | Online: 14 January 2025
Corresponding author: Mothena Fakhri Shaker AlRijeb
Abstract
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%.
Keywords:
oil refinery, soft sensor, machine learning, BDT, SVMDownloads
References
A. S. Yamashita, A. C. Zanin, and D. Odloak, "Tuning the Model Predictive Control of a Crude Distillation Unit," ISA Transactions, vol. 60, pp. 178–190, Jan. 2016.
"Operating manual of Al Doura oil refinery," Aldoura Oil Refinery, Baghdad, Iraq, Technical Report, 2010.
A. Raimondi, A. Favela-Contreras, F. Beltrán-Carbajal, A. Piñón-Rubio, and J. L. De La Peña-Elizondo, "Design of an adaptive predictive control strategy for crude oil atmospheric distillation process," Control Engineering Practice, vol. 34, pp. 39–48, Jan. 2015.
V. T. Minh and A. M. Abdul Rani, "Modeling and Control of Distillation Column in a Petroleum Process," Mathematical Problems in Engineering, vol. 2009, no. 1, Jan. 2009, Art. no. 404702.
T. Takahama and D. Akasaka, "Model Predictive Control Approach to Design Practical Adaptive Cruise Control for Traffic Jam," International Journal of Automotive Engineering, vol. 9, no. 3, pp. 99–104, 2018.
S. Kemaloğlu, E. Ö. Kuzu, D. Gökçe, and Ö. Çetin, "Model predictive control of a crude distillation unit," IFAC Proceedings Volumes, vol. 42, no. 11, pp. 880–885, 2009.
B. Shi, X. Yang, and L. Yan, "Optimization of a crude distillation unit using a combination of wavelet neural network and line-up competition algorithm," Chinese Journal of Chemical Engineering, vol. 25, no. 8, pp. 1013–1021, Aug. 2017.
L. Fortyna, S. Graziani, A. Rizzo, and G. Maria, Soft Sensors for Monitoring and Control of Industrial Processes. London, UK: Springer London, 2007.
S. M. Jafari, M. Ganje, D. Dehnad, and V. Ghanbari, "Mathematical, Fuzzy Logic and Artificial Neural Network Modeling Techniques to Predict Drying Kinetics of Onion: Comparison of Modeling Techniques for Onion Drying," Journal of Food Processing and Preservation, vol. 40, no. 2, pp. 329–339, Apr. 2016.
B. Bidar, J. Sadeghi, F. Shahraki, and M. M. Khalilipour, "Data-driven soft sensor approach for online quality prediction using state dependent parameter models," Chemometrics and Intelligent Laboratory Systems, vol. 162, pp. 130–141, Mar. 2017.
C. Martin, H. Zhang, J. Costacurta, M. Nica, and A. R. Stinchcombe, "Solving Elliptic Equations with Brownian Motion: Bias Reduction and Temporal Difference Learning," Methodology and Computing in Applied Probability, vol. 24, no. 3, pp. 1603–1626, Sep. 2022.
C. A. Duchanoy, M. A. Moreno-Armendáriz, L. Urbina, C. A. Cruz-Villar, H. Calvo, and J. De J. Rubio, "A novel recurrent neural network soft sensor via a differential evolution training algorithm for the tire contact patch," Neurocomputing, vol. 235, pp. 71–82, Apr. 2017.
P. Ilius, M. Almuhaini, M. Javaid, and M. Abido, "A Machine Learning–Based Approach for Fault Detection in Power Systems," Engineering, Technology & Applied Science Research, vol. 13, no. 4, pp. 11216–11221, Aug. 2023.
A. H. H. Al Jlibawi, M. L. Othman, A. Ishak, B. S. Moh Noor, and A. H. M. S. Sajitt, "Optimization of Distribution Control System in Oil Refinery by Applying Hybrid Machine Learning Techniques," IEEE Access, vol. 10, pp. 3890–3903, 2022.
A. Wongchai, S. K. Shukla, M. A. Ahmed, U. Sakthi, M. Jagdish, and R. Kumar, "Artificial intelligence - enabled soft sensor and internet of things for sustainable agriculture using ensemble deep learning architecture," Computers and Electrical Engineering, vol. 102, Sep. 2022, Art. no. 108128.
R. F. Tate, "Correlation Between a Discrete and a Continuous Variable. Point-Biserial Correlation," The Annals of Mathematical Statistics, vol. 25, no. 3, pp. 603–607, 1954.
J. Pan, Y. Zhuang, and S. Fong, "The Impact of Data Normalization on Stock Market Prediction: Using SVM and Technical Indicators," in Soft Computing in Data Science, Singapore, 2016, pp. 72–88.
J. Zhu, Z. Ge, Z. Song, and F. Gao, "Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data," Annual Reviews in Control, vol. 46, pp. 107–133, Jan. 2018.
M. L. Zhang and Z. H. Zhou, "ML-KNN: A lazy learning approach to multi-label learning," Pattern Recognition, vol. 40, no. 7, pp. 2038–2048, Jul. 2007.
L. F. A. Napier and C. Aldrich, "An IsaMillTM Soft Sensor based on Random Forests and Principal Component Analysis," IFAC-PapersOnLine, vol. 50, no. 1, pp. 1175–1180, Jul. 2017.
U. Fayyad, "Data mining and knowledge discovery in databases: implications for scientific databases," in Proceedings. Ninth International Conference on Scientific and Statistical Database Management (Cat. No.97TB100150), Olympia, WA, USA, 1997, pp. 2–11.
L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, Oct. 2001.
C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273–297, Sep. 1995.
Downloads
How to Cite
License
Copyright (c) 2025 Mothena Fakhri Shaker AlRijeb, Mohammad Lutfi Othman, Aris Ishak, Mohd Khair Hassan, Baraa Munqith Albaker
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
