Revolutionizing process alarm management in refinery operations: Strategies for reducing operational risks and improving system reliability
1 Aradel Holdings Plc (Refinery), Port Harcourt, Nigeria.
2 Tomba Resources, Warri, Nigeria.
3 Waltersmith Refining and Petrochemical Company Ltd, Lagos, Nigeria.
4 Shell Petroleum Development Company, SPDC – Port Harcourt, Rivers State Nigeria.
Review Article
Magna Scientia Advanced Research and Reviews, 2023, 09(02), 187–194
Article DOI: 10.30574/msarr.2023.9.2.0156
Publication history:
Received on 15 October 2023; revised on 26 November 2023; accepted on 29 November 2023
Abstract:
Effective alarm management in refinery operations is crucial for maintaining safety, reliability, and efficiency. This paper explores the challenges inherent in traditional alarm systems, including alarm floods, nuisance alarms, and the resultant alarm fatigue experienced by operators. It highlights the negative impacts of poor alarm management, such as increased operational risks and reduced system reliability. The paper then delves into innovative strategies for enhancing alarm management, emphasizing advanced techniques like dynamic alarm management, alarm shelving, and suppression. The integration of predictive analytics and machine learning is discussed as a transformative approach for proactive monitoring and early detection of potential issues. Best alarm rationalization and prioritization practices are outlined, stressing the importance of comprehensive alarm audits, the development of alarm philosophy documents, and continuous operator training. The paper concludes by detailing the significant benefits of enhanced alarm management systems, including the reduction of operational risks, improvements in system reliability, and better regulatory compliance. Strategic recommendations for implementing advanced alarm management systems are provided to help refineries achieve safer, more reliable, and efficient operations.
Keywords:
Alarm Management; Refinery Operations; Operational Risk; Predictive Analytics; Machine Learning; System Reliability
Full text article in PDF:
Copyright information:
Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0