When you look at the water treatment facilities, assets are very critical to their operations. These assets can be pumps, pipes, evaporators, chlorinators, and so on. Most of the inefficiencies like water leakage, monetary losses, or compliance related fines can be directly attributed to assets' performance. So why don't water facilities just replace the assets when they go down in efficiency? One of the biggest problems here is that assets are very expensive. Replacing them is not an option until it completely dies down. Given this situation, what can the water facilities do to solve their problems?

What are the main problems?

Water and wastewater treatment facilities face enormous challenges when it comes to managing their operations. These challenges represent significant expenses to operators. Some of the highest ranking problems include asset health prediction, anomaly detection, performance forecasting, combined sewer overflow avoidance, and many more. Understanding the asset health and learning how to predict it can open up a lot of doors, especially when we can't replace them frequently.

Understanding the definition

Before we dig into asset health prediction, we need to understand asset management. What exactly is asset management anyway? Sounds like it's just managing the assets, right? Well, there's a lot more to it than that. When it comes to wastewater asset management, we need to be aware of all the variables that impact a particular asset's health. It includes the operation, maintenance, and replacement of assets on the critical path. For example, the critical path for a water utility will be retrieving, purifying, and dispersing clean water. This path will include water pumps, water transportation pipes, stormwater sites, and many other components.

What exactly is the problem?

One of the first and foremost questions that comes to mind is -- What's the big deal here? Why can't we just use simple thresholds to get alerted about assets? The problem is that the data is very unstructured. This data is usually a combination of numbers, free form text, SCADA, ERP, event logs, and more. It's usually referred to as a "data lake". Extracting meaningful insights from this data lake takes several areas of expertise like:

  • Automatic data processing engine to parse the data
  • Natural Language Processing to understand text
  • Time-series modeling to analyze sensor data
  • Predictive analytics for event prediction
  • In reference to the title of the post, these are the four prongs we need to build anything meaningful. Modern water facilities are gathering data using many different sources, so we need to make sure we use all that data to drive efficiency upwards.
Okay I understand the problem, but what's the solution here?

We need a solution that can extract wisdom from this data lake consisting of large amounts of unstructured data. More importantly, we need wisdom that's specific to water. We don't need some generic "Artificial Intelligence platform" that uses the same model for many verticals like healthcare, energy, mining, and so on. Artificial Intelligence is an amazing tool that can solve really difficult problems, but only if we use it in the right way. Water is a very unique vertical that has a lot of nuances associated with it. An Artificial Intelligence solution that takes this into account when extracting wisdom will totally outperform a generic Artificial Intelligence platform. Artificial Intelligence deserves to be used in the right (and slightly constrained) way so that it can have a meaningful impact.

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