(186e) Detection and Isolation of Abnormal Event in Nonlinear Industrial Processes By a Novel Data-Based Method
- Conference: AIChE Spring Meeting and Global Congress on Process Safety
- Year: 2019
- Proceeding: 2019 Spring Meeting and 15th Global Congress on Process Safety
- Group: Fuels and Petrochemicals Division - See Also The 31st Ethylene Producers, 19th Topical Conference on Gas Utilization, and 22nd Topical Conference on Refinery Processing
- Time: Wednesday, April 3, 2019 - 2:30pm-3:00pm
Detection and Isolation of Abnormal Event in Nonlinear
Industrial Processes by a Novel Data-Based Method
Hazem N. Nounoub,
Mohamed N. Nounouc, and M. Nazmul
a Artie McFerrin Dept. of Chemical Engineering, Texas
A&M University, College Station, Texas 77843, USA
and Computer Engineering Program, Texas A&M University at Qatar, Doha,
Engineering Department, Texas A&M University at Qatar, Doha, QATAR
Detection and isolation of abnormal events are
important aspects in the industry to ensure safe and proper operation of the
plant. It is important to accurately, reliably, and quickly detect changes or
faults within the process, determine the cause of these changes, and apply
corrective actions to mitigate risk. Multivariate statistical techniques are
powerful tools that utilize data based model reduction methods capable of
efficiently handling process noise and correlated data sets. In literature,
multivariate statistical methods are widely discussed for applications in
process monitoring and fault detection. Moreover, data based process monitoring
techniques have been successfully applied to applications where the accurate
process model are not available. Fault detection can be carried out in two
phases; data based model reduction and statistical fault detection.
The partial least square (PLS) method is an
input-output model that has been effectively applied to linear processes and is
most commonly used for fault detection applications. A kernel extension of PLS
has been proposed to provide an effective technique for modeling nonlinear
industrial processes. In order to enhance the fault detection performance of
KPLS model we have developed a new method that optimizes KPLS model through the
use of a multi-objective genetic optimization approach. Our multi-scale kernel
partial least square (MSKPLS) - based generalized likelihood ratio test (GLRT)
method, has the ability to handle process noise, non-normal data distribution,
and auto-correlated data sets to provide an effective fault detection technique
that can be applied to nonlinear industrial process data.
Once process faults have been detected, accurate
isolation of the specific fault within the plant must be
achieved to aid swift corrective action. Our approach to fault detection using
the advanced statistical GLRT method also allows for multiple faults in the
process to be isolated. The isolation method is called a contribution plot and
uses the calculated statistical values to determine specific process variables
responsible for the process fault, narrowing the possible fault location from
plant-wide consideration to the contributions of a key sub-block consisting of
a few unit operations. We have developed a novel fault isolation and
identification hybrid approach by using a targeted approach where the
generalized likelihood ratio (GLRT) based contribution plot is used to isolate
the faulty sub-block of a process, and then hybrid model-based observer with a
neural network is used to isolate and identify the faulty equipment. We will
demonstrate the improved fault detection and isolation algorithm using the nonlinear
simulated continuously stirred tank reactor (CSTR) data and Tennessee Eastman
Process problem as a case study.
Wavelet function, Hybrid observers, Fault isolation, Neural
network, Continuously stirred tank reactor (CSTR).
1. Bakshi, B. R. Multiscale PCA with
application to multivariate statistical process monitoring. AIChE
J. 44, 15961610 (1998).
Chiranjivi, Majdi Mansouri,
Mohamed Nounou, Hazem Nounou, and M. Nazmul Karim.
2016. Kernel PLS-Based GLRT Method for Fault Detection of Chemical Processes.
Journal of Loss Prevention in the
Process Industries 43 (September): 21224.
3. Botre, C., Mansouri, M., Karim, M. N., Nounou, H. & Nounou, M.
Multiscale PLS-based GLRT for fault detection of chemical processes. J. Loss
Prev. Process Ind. 46, 143153 (2017).
4. MacGregor, J.F., and T. Kourti. Statistical Process Control of Multivariate
Processes. Control Engineering Practice 3, no. 3 (March 1995): 40314.
5. Teppola, Pekka,
and Pentti Minkkinen. 2000.
WaveletPLS Regression Models for Both Exploratory Data Analysis and Process
Monitoring. Journal of Chemometrics 14 (5-6): 38399.