BEGIN:VCALENDAR PRODID:-//Microsoft Corporation//Outlook 16.0 MIMEDIR//EN VERSION:2.0 METHOD:PUBLISH X-MS-OLK-FORCEINSPECTOROPEN:TRUE BEGIN:VTIMEZONE TZID:GMT Standard Time BEGIN:STANDARD DTSTART:16011028T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZOFFSETFROM:+0100 TZOFFSETTO:-0000 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010325T010000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZOFFSETFROM:-0000 TZOFFSETTO:+0100 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT CATEGORIES:Stage 4 CLASS:PUBLIC CREATED:20240430T154603Z DESCRIPTION:Presentation\nImproving the Utilisation of Observations using A rtificial Intelligence and Machine Learning\nhttps://www.chemicalukexpo.co m/speakers/morgan-walters/\n \nSpeaker\nMorgan Walters - Data Scientist - Empirisys\n \nAbout this presentation\nThe process of collecting observati ons in high-risk industrial settings is currently time-consuming\, complic ated\, and highly subjective. This creates inconsistent data that can seld om be used to identify patterns and trends across a facility.\n \nIn respo nse\, Empirisys created Boost. Developed closely with academia and industr ial bodies\, Boost utilises artificial intelligence and machine learning t o improve the observation submission and analysis process.\n \nBy analysin g the observation description\, Boost automatically identifies safety crit ical barriers and life-saving rules at risk\, additionally providing sugge sted actions and potential consequences. This simplifies the process of su bmission\, while ensuring that high quality data is collected. The increas e in accuracy\, consistency and completeness enables the utilisation of ob servations as a leading process safety performance indicator.\n \nThis pre sentation will introduce the research and development of Boost from a proc ess safety and technical perspective\, finishing with a demonstration of t he tool in action.\n DTEND;TZID="GMT Standard Time":20240515T144500 DTSTAMP:20240430T154603Z DTSTART;TZID="GMT Standard Time":20240515T143000 LAST-MODIFIED:20240430T154603Z LOCATION:Stage 4 PRIORITY:5 SEQUENCE:0 SUMMARY;LANGUAGE=en-gb:Improving the Utilisation of Observations using Arti ficial Intelligence and Machine Learning TRANSP:OPAQUE UID:040000008200E00074C5B7101A82E00800000000B07B57713D92DA01000000000000000 01000000044243417912F08478D0DCD73B55E1CD1 X-ALT-DESC;FMTTYPE=text/html:\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n

Presentation

Improving the Utilis ation of Observations using Artificial Intelligence and Machine Learning

https://www.chemicalukexpo.com/speakers/mor gan-walters/

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Speaker

Morgan Walters - Data Scientist - Empirisys

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About this presentation

The process of collecting observations in high-risk industrial settings is currently t ime-consuming\, complicated\, and highly subjective. This creates inconsis tent data that can seldom be used to identify patterns and trends across a facility.

 \;

In response\, Empirisys created Boost . Developed closely with academia and industrial bodies\, Boost utilises a rtificial intelligence and machine learning to improve the observation sub mission and analysis process.

 \ ;

By analysing the observation description\, B oost automatically identifies safety critical barriers and life-saving rul es at risk\, additionally providing suggested actions and potential conseq uences. This simplifies the process of submission\, while ensuring that hi gh quality data is collected. The increase in accuracy\, consistency and c ompleteness enables the utilisation of observations as a leading process s afety performance indicator.

 \;

This presentation will introduce the research and development of Boost from a process safety and technical perspective\ , finishing with a demonstration of the tool in action.

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