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Intelligent Water Systems

Wednesday, April 11, 2018  
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Resources abound for understanding intelligent water systems

 

Corey Williams and Lisa McFadden

 

Intelligent water systems (IWS) are built to link together sensors, control systems, information management, and communications systems. They emphasize the water sector’s opportunity to take advantage of advanced technologies and dramatically shift management decision making.

While there are varying ideas of what an IWS may be, there’s not one singular definition. Some see the concept as a small piece to help in analyze and process data both historical and real-time data; others see this integration as an opportunity to overhaul their entire decision making or performance management approach.

How far each utility or facility chooses to take the IWS concept will vary, but many water sector organizations have produced resources to help guide these choices.

 

Key mechanisms

The Water Science & Engineering Center within the Water Environment Federation issued a technical report that identifies the key mechanisms needed for utilities to start and run a successful intelligent water systems program. Titled, Intelligent Water Systems: The Path to a Smart Utility, the report explores the following 10 topics.

·         Data prioritization — First and foremost, utilities must decide what data is needed and how the data collected will fit into the ultimate strategy and goal of the utility. Data should not be collected for the sake of collection; collecting data takes time, staff, and money. The right data, at the right time, needs to be captured. This critical data must be accurate, complete, and aligned with business and operational management requirements.

·         Data governance — Prior to data capture, system managers need to formulate a data governance approach. This includes identifying data stewardship, storage and access rights, and archiving and deletion protocols. For example, by deciding these responsibilities ahead of time, data processing issues can be ironed out. Developing a data management and governance plan also can help reveal gaps in the system.

·         Data capture — This aspect is probably the most notable component of the process. With all the new and emerging technologies, utilities have vast options for how to capture data and how much to capture. With many new technologies promoting real-time data capture, it is important to note the difference between real-time data and data frequency. While real-time data deals with how quickly the user receives measured data, data frequency refers to how often the data is gathered.

·         Data validation — With speed and an abundance of tools choices, data validation becomes an important component. While collecting data is easy, the goal is to be confident in the data being receiving.

·         Data processing, storage, and access — Organize your data! Historically, data organization is sometimes forgotten. With newer platforms and easier accessibility, the storage, query, and transfer of data is now more manageable than ever. Data organization includes the formulation and upkeep of database table structures that fit the needs for analytics (as distinct from the database table structures for transaction processing).

·         Data integration — By prioritizing and organizing data, users can integrate easier this data into existing systems and processes easier throughout the utility and networks. Remembering the prioritization and overall purpose of the data can help ensure they are being applied in a useful way.

·         Data analytics — With Big Data come big opportunities. By incorporating data analytics, utilities can transform what’s been collected into information. Utilities can choose many types of data analytics tools to use. The ultimate performance goal or outcome helps choose the right platform or tools to perform the analytics.

·         Business intelligence and decision support — With the information provided, utility personnel can make operational and business decisions. By incorporating the information provided from the data analytics into modeling, optimization, and even predictive analysis tools, utilities can look at many different scenarios and find the best solution. By utilizing IWS, water sector agencies can get a big picture view, with the goal of making an informed decision. Theses decision support tools are not just for big capital improvement projects (CIP), but also can be applied to real-time situations and scenarios, through dashboards and cloud-based operations.

·         Knowledge sharing — Once useful information has been attained, it can be integrated throughout the utility’s system and utilized in cloud based systems, allowing the information to be centralized and used across all utility functional groups. By sharing information throughout a utility, data silos fall away. This enables all stakeholders to incorporate the same information into their decision-making processes. Further, data sharing can encourage its use for beneficial purposes that might not have been intended originally.

·         Performance reporting and visualization — IWS is not always just for predictive and decision-making tools, it also can show how efficiently a water sector agency is operating. Coupling tool for performance data and visualization  — such as interactive mapping or GIS, dashboards, or chart pop-ups — can provide useful insight into areas of need and improvement. Once performance gaps are identified via these visualization methods, water sector agencies can use optimization tools to improve operations, reduce energy usage, lower costs, or develop adaptive master planning and CIP. IWS provides the data and information that utilities need to take a step back and look at where improvements may be needed.

 

IWS drivers

Similar to the concepts identified by WEF, the National Association of Clean Water Agencies (NACWA; Washington, D.C.) identified several IWS drivers. NACWA published these findings in the white paper, Envisioning The Digital Utility Of The Future. The paper lists eight drivers for utilities, which include

·         reduce operational costs,

·         manage and mitigate risks,

·         enhance the customer experience,

·         improve financial execution,

·         optimize asset performance and uncover hidden value,

·         leverage existing communications and computing platforms,

·         maximize the engagement and efficiency of employees, and

·         integrate water quality, policy, and performance.

 

Wanted results and simple framework

At the 2018 AWWA/WEF Utility Management Conference (UMC), participants in the workshop, Demystifying the “SMART” Utility, shared their opinions on where IWS can help most. Fully two-thirds of the attendees believed cost reduction and asset optimization to be the most important result of IWS implementation. Figure 1 (p. xx) shows the full results of their voting.

The Smart Water Networks Forum (SWAN) is a non-profit organization that seeks to be the leading global hub for the smart water sector. This group, a WEF partner, seeks to accelerate the awareness and adoption of data-driven technologies in water and wastewater networks worldwide. To help communicate the critical components of IWS, SWAN has developed a five-level framework to clearly define the components.

·         The Physical level comes first. This includes components such as its pipes, pumps, valves, reservoirs, and tanks. As physical water infrastructure only, without data collection or analysis, this layer is often not considered “smart.”

·         The Sensing and Control level includes the initial components enabling IWS. These include sensors, meters, pressure-reducing valves (PRV), and automatic meter reading (AMR) and advanced metering infrastructure (AMI).

·         The Collection and Communication level are technologies that enable storage and transmission of data. Examples include fixed cable network, radio, cellular, and Wi-Fi.

·         Supervisory control and data acquisition (SCADA) system, cybersecurity, and customer information systems (CIS) and geographic information system (GIS) are prime examples of the Data Management and Display level.

·         Data Fusion and Analysis is the ultimate IWS level. These technologies perform data analytics and modelling to help operators by assessing effects of changes, responding to them in real-time, optimizing operations, and planning for enhanced decision-making.

 

Based on these five levels, the same UMC workshop participants who identified cost savings and asset optimization as primary drivers, claimed that the largest resource gap existed at the Data Fusion and Analysis and Collection and Communications levels. The implications are that, in general, water and wastewater utilities appear to have SCADA (level 4) for data management and display and instrumentation and sensors (level 2) in place. However, the need to communicate the data from the sensors to management platforms and the lack of ability to perform analysis for enhanced decision-making are the areas of greatest needs to take full advantage of IWS. Figure 2 (p. xx) shows the workshops participants full opinions on the needs for IWS implementation.

 

Changing workforce and skills

With the implementation of IWS, utilities will start to see a rise in the need for some new skillsets, including data science and data engineering. While current utility personnel may hone some of these skills, these are things that the utility engineer of the future will need to possess. It is important to make students aware of resources that exist outside the “typical” water engineering realm, and that is evident is the large mix of water personnel we are starting to see today.

 

Figure 1. Most desired benefits of intelligent water systems

 

Figure 2. Largest resource needs for intelligent water system implementation

 

About the Authors

 

Corey Williams is CEO of Optimatics (Overland Park, Kan.) and chair of the Interoperability Task Force for WEF’s Intelligent Water Technology Committee,

 

 

 

 

 

 

 

Lisa McFadden is director of Integrated Technical Programs and associate director of the Water Science & Engineering Center  at the Water Environment Federation (Alexandria, Va.).


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