Improving Operational Efficiency Through Alarm Management in Water Treatment Processes Using Artificial Intelligence

Kaushik Ghosh; Gokula Krishnan Sivaprakasam

doi:doi:10.11648/j.ajche.20210906.13

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Improving Operational Efficiency Through Alarm Management in Water Treatment Processes Using Artificial Intelligence

Kaushik Ghosh, Gokula Krishnan Sivaprakasam

Published in American Journal of Chemical Engineering (Volume 9, Issue 6)

Received: 11 October 2021 Accepted: 10 November 2021 Published: 7 December 2021

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Abstract

Water Treatment Plants are controlled by modern industrial process control systems like SCADA or DCS. This facilitates to monitor, control, and troubleshoot water treatment processes and helps in maintaining continuous supply of water with adequate quality. At times and in contrary, these systems hamper process control by generating far too many alarms than needed. Many of the alarms are nuisance in nature and do not indicate any real abnormality. The true alarms which require prompt operator actions to normalize the process are often buried in the pool of nuisance alarms causing significant challenge for operator to take appropriate corrective actions in a timely manner. Many of the past major incidents occurring in the major process industries were attributed to operators’ inability to identify true alarms and take necessary actions. In this paper, we propose an Artificial Intelligence (AI) based pattern mining and advisory system to improve operational efficiency in alarm management. The identified alarm patterns bring out actionable insights in data by (i) identifying nuisance, chattering, redundant alarms, and (ii) Alarm response Pattern. A novel technique for sequential pattern mining in industrial Alarm & Event log data was developed based on State-of-the-art AI based association rule and pattern mining. The efficacy of the proposed method for systematically improving alarm management system in an actual plant environment is currently being studied in a water treatment plant in Singapore.

Published in	American Journal of Chemical Engineering (Volume 9, Issue 6)
DOI	10.11648/j.ajche.20210906.13
Page(s)	147-153
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Alarm Advisory and Decision Support, Alarm and Event Analysis, Alarm Response Pattern, No Action Alarms, Redundant Alarms

References

[1]	Mark D. Sen Gupta, 2019, Alarm Management Global Market 2018 -2023, ARC Market Analysis Report, p 23.
[2]	Bransby M. L., and Jenkinson J., 1998, The Management of Alarm Systems, Health and Safety Executive.
[3]	Rothenberg D., Alarm Management for Process Control. New York, NY, USA: Momentum Press, 2009.
[4]	Wang J., Yang F., Cheng T., and Shah S. L., 2016, An Overview of Industrial Alarm Systems: Main Cause of Alarm Overloading, Research Status, and Open Problems. IEEE transactions on Automation Science and Engineering, 13 (2), 1045.
[5]	EEMUA, 2013, EEMUA-191: Alarm Systems—A Guide to Design, Management and Procurement, Engineering Equipment and Materials Users Association. London, U.K.
[6]	O’Donoghue, N., Phillips, D. H., and Nicell, C., (2015). Reducing SCADA System Nuisance Alarms in the Water Industry in Northern Ireland. Water Environment Research, 87 (8), 751-757.
[7]	ISA, 2009, ANSI/ISA-18.2: Management of Alarm Systems for the Process Industries. International Society of Automation. Durham, NC, USA.
[8]	Ghosh K., and Sivaprakasam G., 2020, Title of Invention: Methods, Systems and Computer Programs for Alarm Handling, Patent Application No.: 10202013108S, Application for the grant of the patent filed with Intellectual Property Office of Singapore (IPOS) on 28 December 2020.
[9]	Naghoosi E., Izadi I., and Chen T., 2011, Estimation of alarm chattering, Journal of Process Control, 21, 1243–1249.
[10]	Wang J., and Chen, T., 2014, An online method to remove chattering and repeating alarms based on alarm durations and intervals, Computers and Chemical Engineering, 67, 43–52.
[11]	Wang J., and Chen, T., 2014, Online reduction of chattering alarms due to random noise and oscillation, Proceedings of the 19th World Congress the International Federation of Automatic Control, Cape Town, South Africa, August 24-29.
[12]	Wang J., and Chen T., 2013, An online method for detection and reduction of chattering alarms due to oscillation, Computers and Chemical Engineering, 54, 140–150.
[13]	Ghosh K., and Sivaprakasam, G., 2020, Aiding Alarm Rationalization by Automatic Identification of various sequential patterns in large volume of Alarm and Event log data, IOP Conference Series: Materials Science and Engineering, 2020.
[14]	Wang J., Li H., Huang J., and Su C., 2016, Association rules mining based analysis of consequential alarm sequences in chemical processes. Journal of Loss Prevention in the Process Industries, 41, 178-185.
[15]	Yang Z., Wang J., and Chen T., 2013, Detection of correlated alarms based on similarity coefficients of binary data, IEEE transactions on Automation Science and Engineering, 10 (4), 1014–1025.

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APA Style

Kaushik Ghosh, Gokula Krishnan Sivaprakasam. (2021). Improving Operational Efficiency Through Alarm Management in Water Treatment Processes Using Artificial Intelligence. American Journal of Chemical Engineering, 9(6), 147-153. https://doi.org/10.11648/j.ajche.20210906.13

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ACS Style

Kaushik Ghosh; Gokula Krishnan Sivaprakasam. Improving Operational Efficiency Through Alarm Management in Water Treatment Processes Using Artificial Intelligence. Am. J. Chem. Eng. 2021, 9(6), 147-153. doi: 10.11648/j.ajche.20210906.13

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AMA Style

Kaushik Ghosh, Gokula Krishnan Sivaprakasam. Improving Operational Efficiency Through Alarm Management in Water Treatment Processes Using Artificial Intelligence. Am J Chem Eng. 2021;9(6):147-153. doi: 10.11648/j.ajche.20210906.13

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@article{10.11648/j.ajche.20210906.13,
  author = {Kaushik Ghosh and Gokula Krishnan Sivaprakasam},
  title = {Improving Operational Efficiency Through Alarm Management in Water Treatment Processes Using Artificial Intelligence},
  journal = {American Journal of Chemical Engineering},
  volume = {9},
  number = {6},
  pages = {147-153},
  doi = {10.11648/j.ajche.20210906.13},
  url = {https://doi.org/10.11648/j.ajche.20210906.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajche.20210906.13},
  abstract = {Water Treatment Plants are controlled by modern industrial process control systems like SCADA or DCS. This facilitates to monitor, control, and troubleshoot water treatment processes and helps in maintaining continuous supply of water with adequate quality. At times and in contrary, these systems hamper process control by generating far too many alarms than needed. Many of the alarms are nuisance in nature and do not indicate any real abnormality. The true alarms which require prompt operator actions to normalize the process are often buried in the pool of nuisance alarms causing significant challenge for operator to take appropriate corrective actions in a timely manner. Many of the past major incidents occurring in the major process industries were attributed to operators’ inability to identify true alarms and take necessary actions. In this paper, we propose an Artificial Intelligence (AI) based pattern mining and advisory system to improve operational efficiency in alarm management. The identified alarm patterns bring out actionable insights in data by (i) identifying nuisance, chattering, redundant alarms, and (ii) Alarm response Pattern. A novel technique for sequential pattern mining in industrial Alarm & Event log data was developed based on State-of-the-art AI based association rule and pattern mining. The efficacy of the proposed method for systematically improving alarm management system in an actual plant environment is currently being studied in a water treatment plant in Singapore.},
 year = {2021}
}

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TY  - JOUR
T1  - Improving Operational Efficiency Through Alarm Management in Water Treatment Processes Using Artificial Intelligence
AU  - Kaushik Ghosh
AU  - Gokula Krishnan Sivaprakasam
Y1  - 2021/12/07
PY  - 2021
N1  - https://doi.org/10.11648/j.ajche.20210906.13
DO  - 10.11648/j.ajche.20210906.13
T2  - American Journal of Chemical Engineering
JF  - American Journal of Chemical Engineering
JO  - American Journal of Chemical Engineering
SP  - 147
EP  - 153
PB  - Science Publishing Group
SN  - 2330-8613
UR  - https://doi.org/10.11648/j.ajche.20210906.13
AB  - Water Treatment Plants are controlled by modern industrial process control systems like SCADA or DCS. This facilitates to monitor, control, and troubleshoot water treatment processes and helps in maintaining continuous supply of water with adequate quality. At times and in contrary, these systems hamper process control by generating far too many alarms than needed. Many of the alarms are nuisance in nature and do not indicate any real abnormality. The true alarms which require prompt operator actions to normalize the process are often buried in the pool of nuisance alarms causing significant challenge for operator to take appropriate corrective actions in a timely manner. Many of the past major incidents occurring in the major process industries were attributed to operators’ inability to identify true alarms and take necessary actions. In this paper, we propose an Artificial Intelligence (AI) based pattern mining and advisory system to improve operational efficiency in alarm management. The identified alarm patterns bring out actionable insights in data by (i) identifying nuisance, chattering, redundant alarms, and (ii) Alarm response Pattern. A novel technique for sequential pattern mining in industrial Alarm & Event log data was developed based on State-of-the-art AI based association rule and pattern mining. The efficacy of the proposed method for systematically improving alarm management system in an actual plant environment is currently being studied in a water treatment plant in Singapore.
VL  - 9
IS  - 6
ER  -

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Author Information

Kaushik Ghosh

Industrial Solutions Research Department, Yokogawa Engineering Asia Pte, Ltd., Singapore
Gokula Krishnan Sivaprakasam

Industrial Solutions Research Department, Yokogawa Engineering Asia Pte, Ltd., Singapore

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Plain Text BibTeX RIS

APA Style

Kaushik Ghosh, Gokula Krishnan Sivaprakasam. (2021). Improving Operational Efficiency Through Alarm Management in Water Treatment Processes Using Artificial Intelligence. American Journal of Chemical Engineering, 9(6), 147-153. https://doi.org/10.11648/j.ajche.20210906.13

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ACS Style

Kaushik Ghosh; Gokula Krishnan Sivaprakasam. Improving Operational Efficiency Through Alarm Management in Water Treatment Processes Using Artificial Intelligence. Am. J. Chem. Eng. 2021, 9(6), 147-153. doi: 10.11648/j.ajche.20210906.13

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AMA Style

Kaushik Ghosh, Gokula Krishnan Sivaprakasam. Improving Operational Efficiency Through Alarm Management in Water Treatment Processes Using Artificial Intelligence. Am J Chem Eng. 2021;9(6):147-153. doi: 10.11648/j.ajche.20210906.13

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@article{10.11648/j.ajche.20210906.13,
  author = {Kaushik Ghosh and Gokula Krishnan Sivaprakasam},
  title = {Improving Operational Efficiency Through Alarm Management in Water Treatment Processes Using Artificial Intelligence},
  journal = {American Journal of Chemical Engineering},
  volume = {9},
  number = {6},
  pages = {147-153},
  doi = {10.11648/j.ajche.20210906.13},
  url = {https://doi.org/10.11648/j.ajche.20210906.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajche.20210906.13},
  abstract = {Water Treatment Plants are controlled by modern industrial process control systems like SCADA or DCS. This facilitates to monitor, control, and troubleshoot water treatment processes and helps in maintaining continuous supply of water with adequate quality. At times and in contrary, these systems hamper process control by generating far too many alarms than needed. Many of the alarms are nuisance in nature and do not indicate any real abnormality. The true alarms which require prompt operator actions to normalize the process are often buried in the pool of nuisance alarms causing significant challenge for operator to take appropriate corrective actions in a timely manner. Many of the past major incidents occurring in the major process industries were attributed to operators’ inability to identify true alarms and take necessary actions. In this paper, we propose an Artificial Intelligence (AI) based pattern mining and advisory system to improve operational efficiency in alarm management. The identified alarm patterns bring out actionable insights in data by (i) identifying nuisance, chattering, redundant alarms, and (ii) Alarm response Pattern. A novel technique for sequential pattern mining in industrial Alarm & Event log data was developed based on State-of-the-art AI based association rule and pattern mining. The efficacy of the proposed method for systematically improving alarm management system in an actual plant environment is currently being studied in a water treatment plant in Singapore.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Improving Operational Efficiency Through Alarm Management in Water Treatment Processes Using Artificial Intelligence
AU  - Kaushik Ghosh
AU  - Gokula Krishnan Sivaprakasam
Y1  - 2021/12/07
PY  - 2021
N1  - https://doi.org/10.11648/j.ajche.20210906.13
DO  - 10.11648/j.ajche.20210906.13
T2  - American Journal of Chemical Engineering
JF  - American Journal of Chemical Engineering
JO  - American Journal of Chemical Engineering
SP  - 147
EP  - 153
PB  - Science Publishing Group
SN  - 2330-8613
UR  - https://doi.org/10.11648/j.ajche.20210906.13
AB  - Water Treatment Plants are controlled by modern industrial process control systems like SCADA or DCS. This facilitates to monitor, control, and troubleshoot water treatment processes and helps in maintaining continuous supply of water with adequate quality. At times and in contrary, these systems hamper process control by generating far too many alarms than needed. Many of the alarms are nuisance in nature and do not indicate any real abnormality. The true alarms which require prompt operator actions to normalize the process are often buried in the pool of nuisance alarms causing significant challenge for operator to take appropriate corrective actions in a timely manner. Many of the past major incidents occurring in the major process industries were attributed to operators’ inability to identify true alarms and take necessary actions. In this paper, we propose an Artificial Intelligence (AI) based pattern mining and advisory system to improve operational efficiency in alarm management. The identified alarm patterns bring out actionable insights in data by (i) identifying nuisance, chattering, redundant alarms, and (ii) Alarm response Pattern. A novel technique for sequential pattern mining in industrial Alarm & Event log data was developed based on State-of-the-art AI based association rule and pattern mining. The efficacy of the proposed method for systematically improving alarm management system in an actual plant environment is currently being studied in a water treatment plant in Singapore.
VL  - 9
IS  - 6
ER  -

Copy | Download