In an interview with World Water-Tech, Thouheed Abdul Gaffoor, CEO of EMAGIN shares his expertise on the efficiency of intelligence platforms in the water-tech industry.

Tell us a bit about EMAGIN and the key partners you work with?

EMAGIN provides water and wastewater utilities with an operational intelligence platform to enable smarter management of their critical processes in real-time. The platform leverages artificial intelligence to create real-time actionable insights and recommendations with the objective of reducing energy costs and resource wastages, as well as enhancing public safety and emergency preparedness. In terms of partners, we work with EPC firms, real-time sensor companies, SCADA providers and system integrators.

How is EMAGIN different to other intelligent platforms?

There are three main differentiators for us in the smart water landscape: Firstly, our platform is system agnostic and thereby capable of supporting the entire built water cycle which includes drinking water treatment, water distribution networks, wastewater treatment and collection systems. The reason we’re able to work across all these systems is because EMAGIN uses AI driven models based on readily measured data streams as opposed to physically based numerical models. The advantage with this approach is that these models are based on parameters that are actually measured in the system. As processes change and/or instrumentation is increased, machine learning models are able to continuously learn from the system and adapt to these changes. Lastly, our platform delivers optimized future recommendations on critical process setpoints that are specifically designed to drive down costs and enhance reliability. While the majority of analytics platforms are great at reporting to operators and engineers on what has already happened or what is happening in real time, they stop short on telling operators what will happen in the future or what actions to take based on future predictions.

What is the challenge facing the water industry and how are you solving it?

The water sector is faced by a number of challenges such as the influence of climate change, deteriorating assets, increased infrastructure intensity and a retiring workforce. Dynamic weather patterns can influence the quality and quantity of plant inputs. With EMAGIN’ real-time optimization, we’re able to understand how these external factors influence process performance and dynamically adapt to them ahead of time. For example, with some of the work we’ve done with distribution networks, we’ve seen that temperature fluctuations and even local events like a Champions League match can have a sizable impact on water demand and thereby supply schedules. Our industry is starting to accumulate an abundance of data. With more sensors being installed, the challenge is knowing about what we can we do with the large volumes of data coming in continuously from so many sources. At EMAGIN, we have the scalability to ingest large amounts of data and provide simple recommendations that are easy to follow. As operational workforces retire in the next decade, we are at risk of losing institutional knowledge. By working with EMAGIN, utilities are able to keep that knowledge in house through our AI platform. The platform is able to make recommendations based on how particular assets have been operated in the past. That way you don’t have to wake up a recently retired member of your team to see what used to be done when a specific issue arose at 3 in the morning.

How does artificial intelligence have the power to progress the industry? Are there any pitfalls?

Artificial intelligence has the power to drive down costs, enhance emergency preparedness, and improve workforce productivity for utilities. It can also ensure that institutional knowledge is kept within the institution. By using the wealth of data we’ve accumulated over years of operations, utilities can leverage AI to learn from both past successes and mistakes. In the same manner that we’ve grown accustomed to Google Maps or Waze for navigation, AI represents the natural progression of technologies that our next generation of engineers and operators will soon be reliant on. However, with any ‘new’ technology, there will always be limitations. Since AI models are reliant on the data they consume, their quality is necessarily a function of the data quality. Of course, data quality control is undoubtedly a tractable problem.

Which projects are in the pipeline for EMAGIN?

We’ve deployed to nearly 10 locations in North America last year and we’re quickly approaching 15 or 20 right now. The upcoming projects are a mix of scaling applications that have already proven successful and working on new application areas through co-innovation with end users. We’re looking forward to expanding into the UK and then Europe with potential to drive into the Middle East and Asia.

What will the audience expect to hear from your Masterclass in Deep Learning session at the World Water-Tech Innovation Summit?

AI in the water sector is still in its relative infancy, which means we are working with our partners to educate the market. In this talk specifically, we’ll start off by separating AI from public perception by defining its foundations. I’ll then walk the audience through specific examples of how AI can be compared with traditional engineering methods, and then describe how AI models are built and applied to water applications.

Don’t miss out on Thouheed Abdul Gaffoor, CEO of EMAGIN speaking at the World Water-Tech Innovation Summit in London on Feb 20 at 2.45pm.