Question
-
To what extent do you see the deployment of digital approaches to maintain and operate facilities while leveraging artificial intelligence (AI) in the restructured operations of the petrochemical industry?
Mar-2023
Answers
-
Andrew Ledlie, Solenis, aledlie@solenis.com
Refineries within the petrochemicals industry are increasingly employing digital technologies, including AI, that support their water treatment management efforts to achieve stricter sustainability targets for reducing water use and improving energy efficiency. This trend, because of the usefulness of these technologies, is likely to continue.
The latest digital approaches to water treatment in refineries leverage three key areas: instrumentation, remote monitoring, and predictive analytics using AI. AI is becoming a key tool for predicting at an early stage the scaling, corrosion, and fouling tendency within cooling water systems.
In terms of instrumentation, many innovative devices, including sensors, analysers, and controllers, have been developed in recent years. For example, Solenis developed a patented analyser that employs ultrasound to measure accurately fouling and deposition in situ. Consequently, when the analyser is used to measure fouling in heat exchangers, the heat exchangers do not need to be opened as frequently for inspection because the ultrasound device gives a real-time in situ picture of any fouling.
Remote monitoring is a powerful way to provide all key stakeholders instant access to critical information in real- time. This enables faster troubleshooting of emerging problems. Waiting for plant personnel to assemble and provide data or for experts to visit the site is costly when downtime or extended production slowdowns occur. Use of a trusted cloud platform, such as Solenis Cloud, addresses this need and allows all stakeholders to see the flow of problem resolution remotely in real-time, thereby reducing stress and providing peace of mind. This platform uses statistical process control tools and techniques to process and display data, thus enabling refinery operators to easily monitor and optimise the performance of their water treatment programmes.
Lastly, predictive analytical tools that crunch large amounts of data are being adopted more widely, provided the operator feels comfortable sharing their data. Solenis’ HexEval performance monitoring program for heat exchangers is an example of AI enabling decision-makers to identify, with confidence, which heat exchangers pose the greatest threat to reliable operation due to scale, corrosion, and/or fouling. Consequently, plant personnel can develop appropriate plans to optimise heat exchanger efficiency. Digital twins are another form of emerging AI that allows refiners to model the impact of process changes before implementation.
With refinery operators striving to improve sustainability, for example, by reducing water use, these digital solutions are critical to ensuring that production, efficiency, and asset protection are not sacrificed in exchange for sustainability improvements. Reducing water use, for example, often increases scaling, corrosion, and fouling, which all negatively affect energy use, maintenance costs, and downtime. Seeing in real-time or via digital twins how each step change in water use reduction affects key performance indicators is powerful and readily available through industry leaders.
Mar-2023
SPONSORS