Nashrieh Shimi va Mohandesi Shimi Iran

Nashrieh Shimi va Mohandesi Shimi Iran

Fault Detection and Diagnosis in Distillation Tower of the Tennessee-Eastman Model using Hotelling's T-squared Method

Document Type : Research Article

Authors
Seyed Mohammad Amin Khatami 1
Hooman Fatoorehchi 1
Ehsan Maani 2
1 Faculty of Chemical Engineering, Technical Faculties Campus, University of Tehran, Tehran, I.R. IRAN
2 Faculty of Engineering Sciences, College of Engineering, University of Tehran, Tehran, Iran
Abstract
Control of the distillation tower is one of the main concerns in the design of chemical processes due to its high importance. Operational errors may occur in distillation towers for various reasons, leading to serious disruptions in process performance or the occurrence of undesirable incidents. In this research, an attempt has been made to use one of the existing validated models for the Tennessee-Eastman Process distillation tower in the MATLAB software to identify potential malfunctions in the tower's operation related to this process. Hotelling's T-squared test is employed for error detection. After detecting errors using the proposed diagnostic method, the type of fault can be determined. The presented method can accurately detect potential model errors within a short period (up to 0.1 hour).
Keywords
Tennessee-Eastman Process
fault detection
distillation tower
Hotelling's T-squared

Subjects

[1] Precup R.-E., Radac M.-B., Roman R.-C., Petriuc E.M., Model-Free Sliding Mode Control of Nonlinear Systems: Algorithms and Experiments, Information Sciences, 381: 176-192 (2017).
[2] Amin M.T., Khan F., Imtiaz S., Fault Detection and Pathway Analysis Using a Dynamic Bayesian Network, Chemical Engineering Science, 195: 777-790 (2019).
[3] Mirakhorli E., Fault Diagnosis in a Distillation Column Using a Support Vector Machine based Classifier, International Journal of Smart Electrical Engineering, 8(3): 105-116 (2019).
[4] Li C., Zhao D., Mu S., Zhang W., Shi N., Li L., Fault Diagnosis for Distillation Process based on CNN–DAE, Chinese Journal of Chemical Engineering, 27(3): 598-604 (2019).
[5] Taqvi S.A., Tufa L D., Zabiri H., Maulud A.S., Uddin F., Fault Detection in Distillation Column Using NARX Neural Network, Neural Computing and Applications, 32(8): 3503-3519 (2020).
[6] Harkat M.F., Mansouri M., Abodayeh K., Nounou M., Nounou H., New Sensor Fault Detection and Isolation Strategy–based Interval‐Valued Data, Journal of Chemometrics, 34(5): e3222 (2020).
[7] Ricker N.L., Lee J., Nonlinear Model Predictive Control of the Tennessee Eastman Challenge Process, Computers & Chemical Engineering, 19(9): 961-981 (1995).
[8] Tian W., Guo Q., Sun S., Dynamic Simulation based Fault Detection and Diagnosis for Distillation Column, Korean Journal of Chemical Engineering, 29(1): 9-17 (2012).
[9] Hotelling H., The Generalization of Student’s Ratio, in Breakthroughs in Statistics, Springer. 54-65 (1992).
[10] Kitano K., Kano M., Gopaluni B., Fault Identification with Modified Reconstruction-based Contribution based on Kernel Principal Component Analysis, in 2017 11th Asian Control Conference (ASCC), IEEE, (2017).
[11] Smith A.J., Powell K.M., Fault Detection on Big Data: A Novel Algorithm for Clustering Big Data to Detect and Diagnose Faults, IFAC-PapersOnLine, 52(10): 328-333 (2019).
[12] Luo L., Xie L., Su H., Mao F., A Probabilistic Model with Spike-and-Slab Regularization for Inferential Fault Detection and Isolation of Industrial Processes, Journal of the Taiwan Institute of Chemical Engineers, 123: 65-78 (2021).
[13] Downs J.J., Vogel E.F., A Plant-Wide Industrial Process Control Problem, Computers & chemical engineering, 17(3): 245-255 (1993).
[14] Ricker N.L., Decentralized Control of the Tennessee Eastman Challenge Process, Journal of process control, 6(4): 205-221 (1996).
[15] Kent J., Bibby J., Mardia K., "Multivariate analysis", Academic Press, Amsterdam, (1979).