By Oliver Grievson
The water industry’s greatest technology trend is also one with scattershot levels of adoption, but that can change with proper understanding of the purpose for and pathway toward Digital Water.
Digital Water, Water 4.0, or smart water — whatever you want to call it — has been a concept that has been around for many years. While speaking on smart wastewater networks at a conference around six or seven years ago, an audience member challenged, “But we’ve been talking about smart wastewater networks for 10 years now, and we still haven’t delivered many.”
This is, of course, the truth about Digital Water. It is something that we’ve struggled with, to grasp its concept and tangibility, because the benefits of Digital Water are not very well known, and the opinion of most is that “Digital Water” is a buzzword for the latest technology. In some regards, people are right in saying that and there are a lot of water companies that will pick up the latest fad in technological innovations, run with it for a few years, and then get bored of it or discover an even newer technology that gets adopted instead. It’s the attraction of pretty, shiny things that look good but deliver only some of what they promise.
For the past few years, within the water industry, I have heard artificial intelligence (AI) being hailed as the future, with proclamations that we can use AI to solve this problem or that problem without much of a clue as to what AI can actually do. This is half the problem with Digital Water — most people don’t realize that it’s a collection of tools utilized to get the best out of a dataset to glean insight on what’s happening within a real-world scenario.
What Is Digital Water?
If you have a room with a hundred people in it, then you will probably get a hundred different answers. To me, Digital Water is using the many forms of data that all water companies collect to gain insight into and situational awareness of a system’s performance. The system can be a wastewater collection network or an entire wastewater treatment system, or it can be the entire anthropogenic water cycle (although the scale of this is somewhat daunting).
The water industry has always suffered from the DRIP (data rich, information poor) phenomenon. It can be argued that there is data poverty as well, depending on the data quality and how a company looks after its data sources (mainly its instruments). What companies usually fail to do — as they simply don’t have time — is to use the many forms of data and bring them together to gain insight. For example, I’ve conducted investigations into why a treatment works is receiving too much flow for the number of people that it’s thought to serve. In these investigations, you tend to look at your data sources to see if they are right, usually finding that one of them is wrong or out of date. The conclusion of the investigation typically results in instrument error due to incorrect setup, a steady increase in population served without anyone knowing, or some sort of infiltration into the network. By looking at the flow data alone and understanding the system, you can understand what’s happening, whether attributable to a cracked pipe, a faulty instrument, or lack of communication with planning authorities. All these investigations were manual tasks with costly resolutions. But what if all that could be done automatically? Wouldn’t this be a value case of actually using the data that is already collected?
What Are The Steps To Achieving Digital Water?
I’ve heard that it doesn’t matter where you start with Digital Water — the important thing is that you start. But I disagree.
Realistically, Digital Water is a mixture of policies, people, and — yes — technology. For me, the start of any Digital Water process is understanding all the informational requirements you may need within the business, as opposed to the standard thinking of “we want everything.” The organizations that say this are somewhat immature in their approaches and aren’t ready. For those who truly are ready, the first step in any Digital Water journey is about the people — it’s about stakeholder engagement, from the CEO of the water company to the operator on the ground. It’s about understanding the operational and engineering needs of the organization.
To illustrate, the CEO of the organization will want relatively high-level data about how the company is performing. Is it doing what it was meant to, or will there be a knock on the door by a regulator with the potential for a big fine? The CEO also wants to know how the business is performing financially and whether the board and shareholders are going to be content. If we trickle this down to the manager of a water treatment plant, they’re going to want to know what asset might fail that could prevent them from producing enough water to keep their customers happy. If we trickle down even further to the operators on the front line of the business, they’re going to want to know how an individual treatment plant is performing and what the next priority is on their list to manage or fire-fight.
And, of course, the information that is given must be absolutely correct — and thus, the source of data has to be correct, too. There is a world full of efficiencies available to water operators, so mining the data they collect has huge value to it. This value is largely untapped.
Once the stakeholder engagement piece is done, a water operator can get more advanced and use the digital tools that are available. An example is the “digital twin” adopted in Valencia, Spain, which is arguably one of the most advanced digital twins for water in the world. This was built up by ensuring that the model it was based upon was correct and that the monitoring was correct as well. Once it was put into practice, problems were exposed related to a lack of depth of data or a lack of calibration in the model. This resulted in a fine-tuning state, going back and forth between instrumentation and model to get things right for the first uses of the digital twin. As more functionality was added, it also added to the complexity. In the end, real-time insight into the performance of the water distribution network became possible, with the ability to forecast what would happen in the future if certain steps were taken. This is Digital Water.
We have, however, only started on this journey as an industry, and there will be many practicalities to iron out. It will take a lot more discussion, so do join me at the IWA Digital Water Summit in Bilbao (https://digitalwatersummit.org), from November 30 to December 2, 2022, to expand on Digital Water in much greater detail.
About The Author
Oliver Grievson is the technical lead at Z-Tech Control Systems, executive director of Water Industry Process Automation & Control, and chairman of the Sensors for Water Interest Group. He is a fellow of the International Water Association as well as the chair of their Digital Water Programme. He has 25 years of experience within the water industry in both the U.K. and abroad, working in everything from municipal operations to engineering design, acting as a technical and operational specialist. His specialties include wastewater operations, instrumentation, and Digital Water.