With the widespread availability of Distributed Control Systems (DCS), continuous monitoring of chemical process operations is greatly facilitated. Plant operators are asked to manage the operation in such a way as to ensure optimal production levels, while attending occasional alarm situations that may result from equipment malfunctions. Timely identification of such abnormal situations may prove to be critical when there is a potential for a safety hazard that may affect not only the plant and its personnel but also the surrounding communities. It is imperative that human expertise is complemented by computerized support systems which consist of various data analysis and interpretation strategies that can provide guidance to the plant personnel for handling abnormal situations. Our group uses a number of techniques ranging from hidden Markov models and Principal Components Analysis to clustering to detect and diagnose faulty operations.
Related Publications
- Gajjar, S., A. Palazoglu, “A Data-Driven Multidimensional Visualization Technique for Process Fault Detection and Diagnosis,” Chemometrics and Intelligent Laboratory Systems, 154, 122-136 (2016).
- Tong, C., A. Palazoglu, “Dissimilarity-Based Fault Diagnosis through Ensemble Filtering of Informative Variables,” Ind. & Eng. Chemistry Research, 55, 8774−8783 (2016).
- Tong, C., A. Palazoglu, X. Yan, “Improved ICA for Process Monitoring Based on Ensemble Learning and Bayesian Inference,” Chemometrics and Intelligent Laboratory Systems, 135, 141-149 (2014).
- Tong, C., A. Palazoglu, N.H. El-Farra, X. Yan, “Fault Detection and Isolation in Hybrid Process Systems Using a Combined Data-driven and Observer-design Methodology,” AIChE J.,60(8), 2805-2814 (2014).
- Tong, C., A. Palazoglu, X. Yan, “An Adaptive Multimode Process Monitoring Strategy Based on Mode Clustering and Mode Unfolding,” J. Process Control, 23, 1497-1507 (2013).
- Chen, Q., G. Yang, W. Sun, A. Palazoglu and K. Feng, “Fault Diagnosis of Rolling Bearing Based on Wavelet Transform and Envelope Spectrum Correlation,” J. Vibration and Control, 19(6), 924-941 (2013).
- Zhu, Z., Z. Song and A. Palazoglu, “Process Pattern Construction and Multi-Mode Monitoring,” J. Process Control, 22(1), 247-262 (2012).
- Zhu, Z., Z. Song and A. Palazoglu, “Transition Process Modeling and Monitoring Based on Dynamic Ensemble Clustering and Multi-class Support Vector Data Description,” Ind. & Eng. Chem. Research, 50, 13969-13983 (2011).
- Sun, W., Y. Meng, J. Zhao, A. Palazoglu, H. Zhang and J. Zhang, “A Method for Multiphase Batch Process Monitoring Based on Auto Phase Identification,” J. Process Control, 21, 627-638 (2011).
- Beaver, S. and A. Palazoglu and J.A. Romagnoli, “Cluster Analysis for Autocorrelated and Cyclic Chemical Process Data,” Ind. & Eng. Chem. Research, 46, 3610-3622 (2007).