(246l) Comparison of Two Identification Methods for Identifying Sequential Alarms in Plant Operation Data

The advance of distributed
control systems (DCS) in the chemical industry has made it possible to
inexpensively and easily install numerous alarms in DCS. While most alarms help
operators detect an abnormality and identify its cause, some are unnecessary. A
poor alarm system might cause sequential alarms, which are a collection of
numerous alarms that almost always occur simultaneously with specific time lags
within a short time (EEMUA, 2007). Sequential alarms reduce the ability of
operators to cope with plant abnormalities because critical alarms are buried
under many unnecessary ones. Sequential alarms are usually caused by poor alarm
rationalization (Hollifield, et al., 2007). Therefore, it is very important to identify
sequential alarms in a plant operation data for improving the alarm system and
process operation.

The grouping correlated
sequential alarms in accordance with their degree of similarity helps to reduce
sequential alarms more effectively than by analyzing individual sequential
alarms. Event correlation analysis (Nishiguchi, et
al., 2010) was proposed to identify sequential alarms in noisy plant
operation data. Event correlation analysis was applied to the operation data of
an industrial ethylene plant and was able to correctly identify similarities
between correlated sequential alarms (Higuchi, et al., 2010). However,
event correlation analysis occasionally failed to detect similarities between
two physically related sequential alarms when deletions, substitutions, and/or
transpositions occurred in the alarm sequence.

In previous study (Wang, et al.,
2015), we proposed a new identification method of sequential alarms by applying
the dot matrix analysis to a plant operation data (Kurata
et al., 2011). Dot matrix analysis (Gibbs, et al., 1970) is one
of the sequence alignment methods for identifying similar regions in DNA or
RNA. Similar regions in DNA or RNA may be a consequence of functional,
structural, or evolutionary relationships between the sequences. In this
research, in order to demonstrate the usefulness of the previous study, we
compare it with the event correlation analysis. Two methods were applied to the
plant operation data of an azeotropic distillation
column. The results revealed that the dot matrix analysis is able to identify
similar sequential alarms in plant operation data, when deletions,
substitutions, and/or transpositions occurred in the alarm sequence..

References

The
Engineering Equipment and Material Users' Association (EEMUA): Alarm Systems A
Guide to Design, Management and Procurement Publication No. 191 Edition 2,EEMUA, London (2007)

Gibbs,
A. J., McIntyre, G. A.: The diagram method for Comparing Sequences. Its Use with Amino Acid and Nucleotide Sequences, Eur. J.
Biochem., 16, 1-11 (1970)

Hollifield,
B. R., Habibi, E.: Alarm Management: Seven Effective
Methods for Optimum Performance, ISA, Research Triangle Park (2007)

Higuchi,
F., Noda, M., Nishitani, H.: Alarm Reduction of
Ethylene Plant using Event Correlation Analysis (in Japanese), Kagaku Kogaku Ronbunshu, 36(6),
576-581 (2010)

Kurata,
K., Noda, M., Kikuchi, Y., and Hirao, M.: Extension
of Event Correlation Analysis for Rationalization of Plant Alarm Systems (in
Japanese), Kagaku Kogaku Ronbunshu,
37, 338-343 (2011)

Nishiguchi,
J., and Takai, T: IPL2&3 Performance Improvement
Method for Process Safety Using the Event Correlation Analysis, Computers
& Chemical Engineering, 34, 2007-2013 (2010)

Wang,
Z., Noda, M. : Identification of Sequential Alarms in Plant Operation Data by
using Dot Matrix Analysis (in Japanese), Kagaku Kogaku
Ronbunshu, 41, 333-339 (2015)