Alarm Data Analysis for Safe Plant Operations: Case Study of Ethylene Plant

Mario R. Eden, Marianthi Ierapetritou and Gavin P. Towler (Editors) Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018 July 1-5, 2018, San Diego, California, USA © 2018 Elsevier B.V. All rights reserved. https://doi.org/10.1016/B978-0-444-64241-7.50380-3

Alarm Data Analysis for Safe Plant Operations:  Case Study of Ethylene Plant Zhexing Wang and Masaru Noda* Department of Chemical Engineering, Fukuoka University, Fukuoka 814-0180, Japan *[email protected]

Abstract Sequential alarms are sets of alarms occurring in succession within a short period of time after triggering an initial warning alarm about an abnormality. These types of alarms reduce the ability of plant operators to cope with operation abnormalities because critical alarms are often lost in numerous other correlated ones. Previously, we proposed an identification method for sequential alarms buried in noisy plant operation data using dot matrix. The dot matrix method is a sequence alignment method for identifying similar regions in DNA or RNA, which may be a consequence of functional, structural, or evolutionary relationships between sequences. In this case study, we use this dot matrix method with operation data from an industrial ethylene plant. The results revealed that sequential alarms in a high volume of operation data from the industrial ethylene plant could effectively be identified using the dot matrix method. Keywords: Plant Alarm System; Sequential Alarm; Dot Matrix Method; Plant Operation Data, Ethylene Plant

1. Introduction Advances in distributed control systems (DCS) in the chemical industry have made it possible to inexpensively and easily install numerous alarms in these DCS. While most alarms help operators detect an abnormality and identify its cause, some do not. A poor alarm system might cause sequential alarms, which are a series of alarms that generally occur quickly in specifically-timed succession. These sequential alarms reduce the operators’ ability to cope with plant abnormalities because the critical alarms get buried under many unnecessary ones. Therefore, it is very important to identify sequential alarms in plant operation data to ensure safe operations. Event correlation analysis (Nishiguchi et al., 2010) was first proposed to identify sequential alarms in noisy plant operation data. This method used plant operation data and a cross correlation function to quantify the degree of similarity on the basis of the time lag between two alarms. Sequential alarms were identified by grouping correlated alarms and operations in accordance with their degree of similarity. This event correlation analysis was applied to the operation data of an industrial ethylene plant, previously, and correctly identified similarities between correlated sequential alarms (Higuchi et al., 2010, Takai et al., 2012). However, the 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. A method for evaluating similarities between sequential alarms by using the normalized Levenshtein distance metric was proposed by Akatsuka et al. in 2013. The Levenshtein distance (Levenshtein 1966, Yujian and Bo, 2007) is a string metric for measuring the

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difference between two sequences defined as the minimum number of edit operations, such as insertion, deletion, and substitution of a single character, needed to transform one string into another. In our previous study (Wang et al., 2017), we proposed an identification method of sequential alarms by applying a dot matrix method to plant operation data. The Levenshtein distance was applied to the simulation data of an azeotropic distillation column, and the results revealed that the method was able to correctly identify similarities between correlated sequential alarms even when the event correlation analysis failed due to deletions, substitutions, and/or transpositions in the alarm sequence. However, the method was not able to identify sequential alarms hidden in plant operation data. To address this, we applied the dot matrix method to the operation data of an industrial ethylene plant in this study.

2. Dot Matrix Method A dot matrix method (Mount, 2004) is a sequence alignment method for identifying similar regions in DNA or RNA. Figure 1 shows an example of two DNA sequences. Similar regions in DNA or RNA may be a consequence of functional, structural, or evolutionary relationships between the sequences. In the dot matrix method, one sequence (S1) is listed across the bottom of the graph, and the other sequence (S2) is listed down the left side, as illustrated in Fig. 2. Starting with the first character in S2, the comparison moves across the graph in the first row and places a dot in any column where the character in S1 is the same. The second character in S2 is then compared to the entire S1 sequence, and a dot is placed in row 2 wherever a match occurs. This process is continued until the graph is filled with dots representing all the matches of S2 characters with S1 characters. A diagonal row of dots reveals the similarity between these two sequences. Dots not on a diagonal row represent random matches that are probably not related to any significant alignment.

S1: A G C T A G G A S2: G A C T A G G C Fig.1 Dot matrix comparison of two DNA sequences

The major advantage of the dot matrix analysis for finding sequence alignments is that all possible matches between two sequences are found, leaving the engineers the choice of identifying the most significant matches through an examination of the dot matrix for long runs of matches, which appear as diagonals.

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G A C T A G G C

Alarm Data Analysis for Safe Plant Operations: Ethylene Plant

A

G C T

A G G A

Fig.2 Dot matrix comparison of two DNA sequences

3. Identification Method for Sequential Alarms The plant operation data recorded in DCS generally consisted of the alarm timings and their tag names, as listed in Table 1. Plant operation data was first converted into a single alarm sequence by putting them in order of the time the alarm occurred. Then, similar regions in the alarm sequences were identified by comparing the alarm alignment with the converted sequence. Figure 3 shows an example of the plant operation data dot matrix analysis. Finally, the identified similar regions, which were assumed to be sequential alarms, were classified into smaller sets of similar sequential alarms in accordance with the similarities between them. Table 1 Example of plant operation data Alarm tag name A1 A4 A2 :

A1 A4 A2 A3 A2 A1 A4 A5

Date/Time 2013/01/01 00:08:53 2013/01/01 00:09:36 2013/01/01 00:11:42 :

A1 A4 A2 A3 A2 A1 A4 A5 Fig. 3 Dot matrix analysis example from plant operation data

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4. Case Study of Ethylene Plant We focused our study on an Idemitsu Kosan Co. Ltd. ethylene plant in Chiba, which started operations in 1985. Figure 4 is a process flow diagram for the ethylene plant, which has two board operators using DCS. The plant IDs in Fig. 4 indicate the unit identification number, which are summarized in Table 2. The total number of alarms in the DCS was 3236 for process control and monitoring. When an alarm occurred, the alarm name and time were recorded in the operation log data.

D1

F1

H2 Off Gas C2H4

K1

V10

Naphtha

V1

V2

C1

V11 V12

V3

V4 V5

V6

H1-H8

C2H6 C3H6 C3H8 C4

G1 R1 V7 V13

T1

V8

V9

U1 Gasoline Heavy Oil

Fig. 4 Process flow diagram for ethylene plant (Higuchi et al., 2010) Table 2 Units in ethylene plant No. C1 D1 F1 G1 H1–H8 K1 P1 R1 T1 U1 V1

Unit name Cracked gas compressor DeNOx section Feed Gas turbine Cracking furnaces 1–8 Exhaust gas stack Product processing unit Refrigeration compressor Tank Utility section Primary fractionator

No. V2 V3 V4 V5 V6 V7 V8 V9 V11 V12 V13

Unit name Quench water tower Demethanizer Deethanizer Acetylene absorber Ethylene fractionator Depropanizer Propylene fractionator Debutanizer Dryer Chill train Hydrogenation Reactor

The plant log data gathered in one month included 914 different types of alarms. A total of 16803 alarms were generated. Figure 5 shows the times at which the 914 types of

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alarms and operation events occurred. It was difficult to identify sequential alarms from just the data shown in Fig. 5.

Fig. 5 Plant operation data of ethylene plant (Higuchi et al., 2010) Figure 6 shows the result of a dot matrix analysis of the plant operation data. A large number of sequential alarms in the plant operation data can be inferred from the multiple diagonal lines represented in black. Table 3 lists the top 10 longest sequential alarms identified by our method. The results revealed that our method is able to correctly identify similar sequential alarms in plant operation data.

Fig. 6 Results of dot matrix analysis

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Fre.

Len.

Alarm sequence

1 2 3 4 5 6 7 8 9 10

2 2 2 2 2 2 2 2 2 2

8 7 7 6 6 6 6 6 6 6

A451→A128→A451→A278→A451→A128→A451→A128 A450→A451→A128→A516→A450→A451→A128 A128→A451→A128→A451→A128→A165→A784 A893→A128→A812→A813→A814→A815 A826→A164→A165→A784→A451→A128 A516→A451→A128→A451→A516→A128 A451→A816→A451→A816→A128→A816 A451→A812→A813→A814→A815→A128 A165→A784→A278→A451→A128→A893 A128→A451→A128→A278→A451→A128

Related units V2, U1 H2, U1 H2, U1 H6 H2, U1 H2, U1 H6, U1 H6, U1 U1, U2 U1

5. Conclusions A dot matrix method using the Levenshtein distance metric was applied to an ethylene plant operation data. The results revealed that the method was able to correctly identify similar sequential alarms in the data. By classifying sequential alarms into smaller groups, a dot matrix method using the Levenshtein distance metric effectively identified similar types of alarms. These results revealed that this method can correctly identify similarities, despite changes to sequences, in plant operation data to ensure safer operations at an industrial chemical plant.

Acknowledgment This work was supported by JSPS KAKENHI Grant Number 17K06909.

References Akatsuka, S. and Noda, M.: Similarity Analysis of Sequential Alarms in Plant Operation Data by Using Levenshtein Distance, Proc. of PSE Asia 2013, Kuala Lumpur (2013) Higuchi, F., Noda, M., and Nishitani, H.: Alarm Reduction of Ethylene Plant using Event Correlation Analysis (in Japanese), Kagaku Kogaku Ronbunshu, 36(6), 576-581 (2010) Levenshtein, V.I.: Binary Codes Capable of Correcting Deletions, Insertions, and Rever-sals, Cybernetics and Control Theory, 10(8), 707-710 (1966) Mount, D.W.: Bioinformatics Sequence and Genome Analysis Second Edition, Cold Spring Harbor Laboratory Press, New York (2004) 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). Takai, T., Noda, M., and Higuchi, F.: Identification of Nuisance Alarms in Operation Log Data of Ethylene Plant by Event Correlation Analysis (in Japanese), Kagaku Kogaku Ronbunshu, 38(2), 110-116 (2012) Wang, Z. and Noda, M.: Identification of Repeated Sequential Alarms in Noisy Plant Operation Data Using Dot Matrix Method with Sliding Window, Journal of Chemical Engineering of Japan, 50(6), 445-449 (2017) Yujian, L. and Bo, L.: A Normalized Levenshtein Distance Metric, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 1091-1095 (2007)