Data-Based Fault Detection for Safe and Reliable Process Start-up and Recovery after Natural Disasters
- Conference: AIChE Spring Meeting and Global Congress on Process Safety
- Year: 2018
- Proceeding: 2018 Spring Meeting and 14th Global Congress on Process Safety
- Group: Fuels and Petrochemicals Division - See Also Topicals 4, 6, and 7
- Time: Wednesday, April 25, 2018 - 1:00am-1:30am
To achieve this goal, we propose the use of novel data-based fault detection methods to provide early detection of changes (faults) in a process that are incurred during a natural disaster, increasing process safety and ensuring reliable operation. Our novel diagnostic method combines nonlinear fault detection methods with advanced statistical composite hypothesis testing to accurately and reliably detect equipment damage during plant start-up. Our novel framework substantially improves the detection ability over previously developed methods for process monitoring found in the literature by reducing the missed detection and false alarm rates, leading to higher accuracy and dependability . Additionally, the developed multiscale kernel partial least square (MS-KPLS) based generalized likelihood ratio test (GLRT) method incorporates an ability to independently detect and isolate multiple simultaneous faults within a process, a critical factor when catastrophic events can cause damage to different critical loops simultaneously currently missing from traditional fault detection software. Our MS-KPLS based GLRT algorithm has also been developed to handle process noise and correlated data sets to increase reliability when determining faults within a chemical system [4, 5]. Going further, we will demonstrate abnormal event management capabilities with state-of-the-art techniques in state observer design to provide a first-of-its-kind isolation method aimed toward speeding up repair efforts and helping industrial processes recover after natural disaster events. While this work is not specific to any industrial process, relying only on the accessibility of data from prior to and after the occurrence of the disaster, we will demonstrate the improved fault detection algorithm using the Tennessee Eastman Process problem as a case study. This work will demonstrate how databased models can be used to ensure plant safety post-disaster with an accurate and reliable detection and isolation strategy.
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- Botre, C., Mansouri, M., Nounou, M., Nounou, H. & Karim, M.N. Kernel PLS-based GLRT method for fault detection of chemical processes. Loss Prev. Process Ind. 43, 212â224 (2016).
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