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Find out how AVEVA customers are putting industrial intelligence to work
- Innergex
- Meridian Energy
- SUEZ recycling and recovery UK
When risk becomes opportunity
Innergex are on a mission to help build a better world.
As a global leader in renewable energy, Innergex has a rapidly expanding network, with many sites in remote locations. But with these facilities using different operating systems and software, communication could be unreliable, performance was inconsistent, and data was often lost.
To dial up efficiency – and hit their climate goals – Innergex set out to:
- Standardize and consolidate operational control
- Improve data analysis and reporting
- Create a system that would eliminate data loss
Innergex needed an open, agnostic approach to unify these disparate systems. And they also required a flexible solution that would grow and scale with the business. This made AVEVA software a natural fit.
The transition to a clean economy, turning a risk into an opportunity, will be powered by renewable energy. This represents a unique opportunity to meet our collective climate goals while promoting sustainable economic development and enhancing the human condition.
Operation: control
Working with AVEVA, Innergex brought these disparate systems together into one environment. This gave operators a holistic view of exactly what was happening across all sites, enabling them to address issues proactively and optimize performance on-the-go.
This seamless connectivity and functionality significantly improved operational agility, scalability, and sustainability. And thanks to AVEVA software, Innergex was able to put a robust data structure in place to counter data dark spots and safeguard data integrity.
Plotting a path towards sustainable expansion
These new solutions have completely reshaped Innergex's processes and procedures.
Troubleshooting has been transformed, with operations staff at every plant, as well as the managers from the corporate office, all using the same data. Operational excellence and efficiency have hit new heights. And seamless access to data means stronger business cases for investment.
By putting industrial intelligence to work, Innergex is better equipped to navigate the renewable energy landscape, capitalize on emerging opportunities, and fight climate change.
AVEVA Operations Control provides significant flexibility to deploy what we need and makes it easy to upgrade to remain current.
When downtime becomes real-time
The teams at Meridian Energy, New Zealand’s largest renewable energy generator, believe in making a difference through data.
However, the company faced difficulty in assessing the performance of its assets. Limited information made it hard to proactively identify maintenance issues. And the lack of real-time insights made performance optimization a challenge.
If the team at Meridian wanted to see how their generating assets were performing, they had to shut them down to access data. Routine maintenance and investigations into asset degradation required shutdowns, as well.
Frequent operational disruptions reduced efficiency. And the inflexibility of outage scheduling, compounded by market demand constraints, added further complications.
Our goal was to move towards a condition-based approach, to optimize resources through condition-based monitoring. That’s where the AVEVA PI System came in.
The platform takes center stage
Overall, these challenges highlighted the critical need for a comprehensive solution that provides actionable insights into asset performance without relying on plant shutdowns. By partnering with AVEVA and implementing the AVEVA PI System, Meridian found their answer.
AVEVA PI System provides an open and agnostic centralized platform for collecting data, which delivers critical metrics to various business units in the formats most relevant to them.
Significant improvements are realized in both data collection and analysis. Unlocking real-time data transparency facilitates informed decision-making across the organization. Teams gain valuable insights into asset performance and they’re able to optimize maintenance schedules and enhance overall efficiency.
Decision-making processes are streamlined. And without the need for frequent shutdowns, asset reliability is improved, downtime minimized, and operational efficiency results in cost savings.
Difference through data
As the world transitions towards sustainable energy, innovative technologies become an imperative to drive efficiency, resilience, and decarbonization.
Meridian's strategic collaboration with AVEVA serves as a compelling example of how data-driven decision-making can empower organizations to overcome operational challenges.
The integration of real-time monitoring and predictive analytics capabilities enables Meridian to optimize resource usage, reduce environmental impact, and ensure reliable energy supply for consumers.
By putting industrial intelligence to work, Meridian has set a precedent for proactive asset management practices, driving innovation, efficiency, and sustainability for the benefit of society and the environment.
The AVEVA PI System has given us a foundation to build upon with the hopes of creating more complex analytics in the future
When uncertainty becomes reliability
SUEZ recycling and recovery UK operates 11 energy-from-waste facilities across the UK which recover energy from its customers’ residual waste. The company is continually striving to reach even higher levels of reliability and to improve the operational efficiency of its facilities.
SUEZ faced specific challenges:
- Asset performance was difficult to assess during changing process conditions.
- Small process deviations undetectable through human analysis alone were creating larger issues.
- Failures identified too late by existing monitoring tools to allow repairs to be scheduled for a planned shutdown.
The key problem the team needed to overcome was reducing the risk of boiler downtime. Failures in assets or processes within the plant typically result in boilers being taken out of service for five days, with an associated costly loss of generation capacity. In addition, unprocessed waste needs to be redirected. More significantly, the likelihood of future downtime increases with every day that equipment is offline.
SUEZ turned to AI-based predictive analytics software from AVEVA for a solution to meet its needs.
We knew that to make further steps in improving performance reliability, we needed a system to analyze condition-monitoring data alongside process data. Allowing performance of processes and individual assets to be assessed in varying conditions.
Pilot proves the value
In November 2022, SUEZ commenced a trial of AVEVA™ Predictive Analytics at its largest energy-from-waste facility. The team modelled multiple critical assets and processes, including assets which had previously experienced failure. These included water tube boilers, steam turbine generators, and other auxiliary systems.
AVEVA software enabled the team to monitor real time asset behaviour, and alert when operation differed from the historical norm. During the trial, the team detected multiple instrumentation issues, including some new failure scenarios.
Most significantly, the team tested a previous failure event relating to a combustion air bearing. The software was able to successfully identify the issue 18 months earlier than had happened in real life. Earlier detection at the time would have prevented 5 days of unplanned boiler downtime, so the test gave the team confidence in the huge preventative value of applying the analytics and AI.
Rolling out to all plants
By putting industrial intelligence to work, SUEZ recycling and recovery UK, found a trusted and proven way to reduce costly downtime. The centralized deployment within the SUEZ UK data centre, is helping the team to roll out the solution across its other 10 energy-from-waste plants.
We viewed several predictive monitoring systems before we chose AVEVA™ Predictive Analytics. What made [AVEVA] Predictive Analytics stand out for us was the ease and speed that digital models could be built, that could be used to model both processes and physical assets, and the ability of the system to learn from historical data.
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