AI Adoption Case Study: AVEVA's work with Drax and SUEZ on AI driven predictive maintenance
techUK’s AI adoption collection of case studies showcases examples of how organisations are Putting AI into Action, either through the adoption of AI models within their organisations, or by developing AI tools that can be leveraged by others.
By shining a light on these use cases, techUK hopes to demonstrate examples of best practice from across sectors and from organisations of all sizes.
1. Challenge:
Drax are one of the top producers of low carbon electricity in the UK, while SUEZ manages and processes household waste for a large number of local authorities, endowing it with a key role in the circular economy. Both companies have a major part to play in the country’s transition to Net Zero.
Latent degradation and issues with high integrity industrial equipment in Drax and SUEZ plants such as boilers, turbines, pumps and generators can grow into defects which eventually cause unplanned downtime, interrupting the reliable production of power and the processing of waste. Large industrial plants are fitted with tens of thousands of IoT sensors measuring temperatures, pressures, flows, vibrations the recording huge amounts of data minute by minute every day. Over the past two years Drax and SUEZ both worked with AVEVA to deploy a complete diagnostics process harnessing that data to reveal the hidden heartbeat of their equipment health.
2. Solution:
Time series data historians such as AVEVA’s Pi system constantly store the data coming from equipment sensors, organise and contextualise the data and performing checks to ensure data quality. Predictive Analytics, a key AI enabling technology harnesses that data using an easily explainable machine learning anomaly detection model to encode the good operational behaviour of a piece of equipment from the historical sensor data. Once that model is deployed on the live data it is silently watching, twenty four hours a day and seven days a week, for the first hints of when an equipment departing from the good historical envelope. This gives warning, long in advance, and allows Drax and SUEZ to plan, assemble spare parts and schedule maintenance interventions when they will be least impactful to electricity production or waste processing. The solution is configured for Engineers and equipment experts to generate valuable outcomes; not only does it detect anomalies in the data, but it recognises signatures of typical faults, for example a damaged bearing, giving prescriptive advice for Engineers to be able to follow up with the appropriate corrective actions. When an anomaly is caught the system is gives precise citations to show the user which sensors are contributing such that the results of the system are highly accountable and not obscured in an AI ‘black-box’. The system is highly scalable capable of monitoring hundreds or thousands of pieces of critical industrial equipment, but is computationally highly efficient requiring little more than a desktop computer to run.
3. Impact:
From the first implementation of the software both Drax and SUEZ started to detect issues with their equipment.
Drax found a generator transformer gas in oil measurement increasing indicative of a serious fault. A similar transformer had previously failed causing an unplanned stop of the unit for more than four weeks. With the early detection provided by Predictive Analytics Drax were able to operate the transformer differently, perform a short remedial repair and continue operation until the next major planned stop.
One of SUEZ steam turbines showed signs of a large axial movement of the rotor; a tell-tale sign of a balance-piston seal failure. When the machine was later opened, the inspection verified that the seal was indeed severely damaged. Although this case didn’t pose an immediate risk to operation, the early detection allowed better preparation for the next planned overhaul for the risk that more work would be required to remediate.
Usman Ikhlaq
Usman joined techUK in January 2024 as Programme Manager for Artificial Intelligence.
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Kir Nuthi
Kir Nuthi
Kir Nuthi is the Head of AI and Data at techUK.
She holds over seven years of Government Affairs and Tech Policy experience in the US and UK. Kir previously headed up the regulatory portfolio at a UK advocacy group for tech startups and held various public affairs in US tech policy. All involved policy research and campaigns on competition, artificial intelligence, access to data, and pro-innovation regulation.
Kir has an MSc in International Public Policy from University College London and a BA in both Political Science (International Relations) and Economics from the University of California San Diego.
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Usman Ikhlaq
Usman Ikhlaq
Usman joined techUK in January 2024 as Programme Manager for Artificial Intelligence.
He leads techUK’s AI Adoption programme, supporting members of all sizes and sectors in adopting AI at scale. His work involves identifying barriers to adoption, exploring solutions, and helping to unlock AI’s transformative potential, particularly its benefits for people, the economy, society, and the planet. He is also committed to advancing the UK’s AI sector and ensuring the UK remains a global leader in AI by working closely with techUK members, the UK Government, regulators, and devolved and local authorities.
Since joining techUK, Usman has delivered a regular drumbeat of activity to engage members and advance techUK's AI programme. This has included two campaign weeks, the creation of the AI Adoption Hub (now the AI Hub), the AI Leader's Event Series, the Putting AI into Action webinar series and the Industrial AI sprint campaign.
Before joining techUK, Usman worked as a policy, regulatory and government/public affairs professional in the advertising sector. He has also worked in sales, marketing, and FinTech.
Usman holds an MSc from the London School of Economics and Political Science (LSE), a GDL and LLB from BPP Law School, and a BA from Queen Mary University of London.
When he isn’t working, Usman enjoys spending time with his family and friends. He also has a keen interest in running, reading and travelling.
Sue Daley OBE
Sue Daley OBE
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In 2025, Sue was honoured with an Order of the British Empire (OBE) for services to the Technology Industry in the New Year Honours List.
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A key influencer in driving forward the data agenda in the UK, Sue was co-chair of the UK government's National Data Strategy Forum until July 2024. As well as being recognised in the UK's Big Data 100 and the Global Top 100 Data Visionaries for 2020 Sue has also been shortlisted for the Milton Keynes Women Leaders Awards and was a judge for the Loebner Prize in AI. In addition to being a regular industry speaker on issues including AI ethics, data protection and cyber security, Sue was recently a judge for the UK Tech 50 and is a regular judge of the annual UK Cloud Awards.
Prior to joining techUK in January 2015 Sue was responsible for Symantec's Government Relations in the UK and Ireland. She has spoken at events including the UK-China Internet Forum in Beijing, UN IGF and European RSA on issues ranging from data usage and privacy, cloud computing and online child safety. Before joining Symantec, Sue was senior policy advisor at the Confederation of British Industry (CBI). Sue has an BA degree on History and American Studies from Leeds University and a Masters Degree on International Relations and Diplomacy from the University of Birmingham. Sue is a keen sportswoman and in 2016 achieved a lifelong ambition to swim the English Channel.
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