Maximising your assets
Published: 07 June, 2021
Andrew Normand* looks at how the power of AI can save plant operators a small fortune
In today’s intensely competitive trading environment, it has never been more important to get the most out of your assets. In this context, using technological innovations such as artificial intelligence (AI) can maximise the efficiency and productivity, not only of individual pieces of equipment, but also entire process systems and networks of equipment.
These systems range from large, complex power plant networks to small-scale cooling systems. The efficiency of these systems depends not only on the equipment itself, but on a range of wider factors, including upstream/downstream environmental conditions and the efficacy of other pieces of equipment within the system.
Equipment readings can be impacted by a range of different factors based on conditions upstream or downstream, through auxiliary equipment and environmental factors such as sudden, unexpected deviations in weather patterns. The problem is that it’s often very difficult to create predefined rules that will be able to deal with all the complexity of these interacting variables. Plant operators therefore have to settle for identifying more pronounced symptoms with limited signals to avoid false alarms, whilst still catching an issue before a major failure. However, the damage has already been done and it still doesn’t identify the upstream/downstream operations that may have caused the issue.
This can lead to numerous problems including system failure, loss of efficiency and reduced availability leading to lost productivity – which could potentially cost plant operators millions of pounds in lost revenue. Therefore, there’s a need for a technology that can review the entire system as a whole, taking in more than just the main equipment but also the environmental factors and the impacts of auxiliary systems and operating conditions.
Step forward AI. When properly targeted with purpose-built applications – for plant monitoring purposes, for example – AI is capable of reviewing entire systems and stripping away the noise to provide engineers with a far greater accuracy of understanding and much more confidence in the warnings. This leads to much more targeted investigations, giving engineers the tools to understand quickly and more accurately what is happening with their equipment.
AI intelligence can remove the affecting variables to reveal the underlying performance of a system. New patterns that weren’t previously visible can now be identified. Problems can be detected much earlier and rectified before they cause failures or reduce the efficiency of the system.
So, how does it work in practice? To give an example, Andrew Normand, UptimeAI partnership lead for Encora Energy, explains that his company’s “AI Expert” software uses an AI engine that continuously learns from historic and ongoing data and identifies how each of the parameters involved in the system change in relation to each other. From this data, it is able to continually read current new data and predict an expected value based on other parameters. It then compares this predicted value against the actual data and determines any discrepancies, creating an anomaly score that indicates the overall health of the system. It also runs a diagnostic engine with built-in world-class domain knowledge to diagnose and define the issue and uses built-in process plant knowledge to identify specific issues within the system. This knowledge is gathered through the insights and input of UptimeAI’s experts and also an in-built feedback loop that allows the system to recognise and respond to new events – meaning that it can learn and adapt in the same way that a human can.
Essentially, the technology can analyse huge amounts of data, allowing plant operators to see the system as a whole and identify the impacts of different parts of the system (e.g. the pieces of equipment within it), the external forces (e.g. environmental factors) and how all of these elements interlink with each other. On top of this, there are no rules to define and manage as the AI engine can develop its own understanding of what is significant and what is due to external influence. It’s also capable of continuously learning from new experiences and can recognise new types of events – effectively learning in the same way that a human engineer learns.
There are numerous benefits of using an AI engine such as this. Not only can it identify and diagnose problems (such as equipment faults and inefficiencies within the system), it can also make recommendations on how to resolve them and prevent them from re-occurring in the future. It can also simultaneously predict how well each piece of equipment in the system will work and flag up reliability, efficiency, and product quality problems before they happen – potentially saving companies millions of pounds in lost revenue.
CASE STUDY:
UptimeAI was asked to look at the efficiency of a condensing steam turbine in a power plant to determine any lost efficiency and improvements that could be made. The company’s “AI Expert” software was fed with historic data for the entire turbine system which included not just the turbine itself, but also the condenser and the entire cooling water circuit as one system. The turbine system under review was heavily influenced by a large variance in cooling water temperature due to seasonal and daily environmental changes. Teasing out the effects of large seasonal variations and operating changes to see the underlying causes is beyond human analysis.
Feeding historic data through the AI application revealed a continued upward trend including three targeted alarms over 36 months indicating points of significant change. The alarms were generated taking into account the impacts of turbine exhaust, seasonality and load fluctuations. By analysing where the anomalies were greatest, the AI diagnosis tool was able to make predictions on the likely failure mechanisms and prescriptive recommendations using the application’s built-in engineering knowledge. This was able to diagnose specific cooling water issues that were affecting the efficiency of the condenser and hence the turbine.
The UptimeAI application was able to identify a total improvement opportunity of 0.016bar of condenser vacuum. This was made up of cooling water tower/return inefficiency (circa 25%), cooling water discharge pump low pressure (circa 24%), condenser fouling (circa 32%) and air ingress (circa 19%). This equated to a 2.2% improvement opportunity in backpressure worth an estimated £140,000 per year in increased efficienc
*Andrew Normand is UptimeAI partnership lead for Encora Energy
https://www.linkedin.com/company/encora-energy-limited/
