Feature sponsored by Test & measurement


One primary advantage is the ability to make informed decisions based on data that reflects real-world, near real-time operating conditions. Near- or actual real-time data gathering is highly sought after as water supplies and their management comes under increasing pressure from a combination of population growth, rising urbanisation and expanding industrialisation. Each consumes large amounts of water and generates effluent waste that has to be treated to the highest quality before being returned to its natural origins.


WHAT MAKES PREDICTIVE MAINTENANCE COMPELLING? First and most important, it is imperative to distinguish between predictive maintenance and legacy “run-to-fail”, and preventive maintenance approaches. Run-to-fail literally means, “Let it run until it goes wrong; then fix it”. This is the least attractive option when water systems are concerned because human and animal safety can potentially be compromised, or valuable crops placed under threat. Preventive maintenance, on the other hand, is the most common approach and is often a core tenet of industrial operations. However, this approach can be unnecessarily costly because it can mandate


Instrumentation Monthly June 2023


refurbishment or replacement of devices and systems that are in robust working order with have long lives ahead of them while allowing less robust devices or systems to slowly deteriorate and eventually fail. The problem is that you do not always know what is “good” versus what is going bad, so by default, you replace everything within a given time scale. However, modern maintenance programs are increasingly taking advantage of new sensing, data gathering and analysis algorithms that continuously examine the state of every instrument or device being used to ensure safe water supplies. The ability to accurately measure current data and compare it to historical trends provide highly reliable models of what to expect in the future, not only enabling the operator to predict the with a high degree of certainty the likelihood of supply and service issues, but to identify precisely where and when those problems are likely to happen and initiate plans for their resolution before they happen. That means that predictive


maintenance can save operators large sums of money by only scheduling maintenance where it is expected to be required rather than apply maintenance as an incontrovertible policy.


THE IMPORTANT NEW ROLES OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE


The implementation of machine learning (ML) and artificial intelligence (AI) are rapidly accelerating to optimise the efficiency, performance and management of potable and wastewater treatment plants and networks worldwide.


The ability of machines to autonomously learn about how anomalies that negatively affect water treatment and distribution issues and predict when and where they will occur is now enabling operators to plan and carry


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