Narrative
Large-scale industrial installations - such as oil refineries and power stations - rely on safety-critical systems that employ distributed sensor networks coupled to alarms. A sub-optimal or erroneous alarm configuration at these installations can have catastrophic environmental, health, and economical consequences.Researchers at The University of Manchester (UoM) and the SME Argent & Waugh Ltd have used new mathematical algorithms and methods to develop an innovative approach to improve alarm configurations that identify redundancies reliably and in real time. The new approach is deployed at industrial sites via the widely-used Sabisu software platform, with more than 6,000 individual license owners worldwide, at companies such as SABIC, Royal Dutch Shell, and Huntsman. For SABIC alone, the savings from using Sabisu are estimated at GBP1,500,000 per annum, resulting from more efficient real-time monitoring of equipment.
Further benefits arise from increased plant safety and reduced operator workload in the plants, and for Argent & Waugh through increased profits and enhanced capability.
Impact date | 1 Aug 2013 → 31 Jul 2020 |
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Category of impact | Economic |
Impact level | Adoption |
Documents & Links
- REF2021 impact case- Making industrial installations safer and more efficient by identifying real and false alarms
File: application/pdf, 486 KB
Type: Text
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Research output
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Detecting and reducing redundancy in alarm networks
Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review
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Matching Exponential-Based and Resolvent-Based Centrality Measures
Research output: Contribution to journal › Article › peer-review
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Computing the action of the matrix exponential, with an application to exponential integrators
Research output: Contribution to journal › Article › peer-review
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Scaled and squared subdiagonal padé approximation for the matrix exponential
Research output: Contribution to journal › Article › peer-review
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Automatic real-time fault detection for industrial assets using metasensors
Research output: Contribution to conference › Paper › peer-review