Abu Dhabi National Oil Company (Adnoc) has appointed US-based industrial digitalisation services provider Honeywell to undertake a predictive asset maintenance project.
Under the terms of a 10-year partnership agreement, Adnoc will use Honeywell’s asset monitoring and predictive analytics platform to maximise asset efficiency and integrity across its upstream and downstream operations.
Adnoc is looking to deploy Artifical Intelligence (AI) technologies, like machine learning and digital twins to help predict equipment stoppages, reduce unplanned equipment maintenance and downtime, increase reliability and safety, and enable substantial cost savings.
Adnoc’s predictive maintenance project is part of the company’s flagship Centralized Predictive Analytics & Diagnostics (CPAD) programme, which is aligned with cost optimisation goals as part of the company’s 2030 growth strategy.
Adnoc will deploy Honeywell Forge Asset Monitor and Predictive Analytics solutions at its Panorama Digital Command Center in its headquarters in Abu Dhabi.
The Panorama Digital Command Center currently aggregates real-time information across all business units and uses smart analytical models, AI and big data to generate operational insights and recommend new pathways.
The addition of Honeywell’s solutions will enable the central monitoring of up to 2,500 critical rotating equipment across all Adnoc Group companies.
With continuous online monitoring of equipment, aided by machine learning analytics and digital twin models, operators and maintenance personnel at Adnoc will be able to identify impending machinery issues earlier, and shift from reactive and preventative maintenance practices to a predictive maintenance approach.
Sophisticated insights into equipment health will also allow Adnoc to evaluate equipment overhaul extension programmes, and increase the availability of equipment and maximise production.
Built on a scalable enterprise platform, the Honeywell systems will accelerate time to value by providing Adnoc engineers with a host of embedded data science and simulation tools.
This article is sourced from Power Technology sister publication www.meed.com, a leading source of high-value business intelligence and economic analysis about the Middle East and North Africa. To access more MEED content register for the 30-day Free Guest User Programme.