Many digital solutions which are powered by digital twins are assisting communities in lowering water prices, reducing the impact of climate events, lowering the costs of safely reclaiming wastewater, and lowering emissions connected with water and wastewater management.

However, every breakthrough comes with a learning curve, and digital twins are no exception.

Traditional infrastructure simulation models were time-consuming and expensive to create.

Today, advanced machine learning technologies improve infrastructure representation by automatically calibrating to match.

When combined with the correct skills, advanced digital twin frameworks may connect digital twins to comprehensive decision intelligence.

Focus on the Hydroinformatics

The transdisciplinary application of information and decision support systems to the equitable and efficient management and use of water for a variety of purposes is known as hydroinformatics.

Hydroinformatics engineers are multidisciplinary professionals who use hydraulic modeling, engineering, and a complete understanding of the water cycle to solve long-standing water problems in novel ways.

A hydroinformatics engineer uses the digital twin outputs to create strong algorithms that generate utility recommendations.

These could include real-time operational advice delivered via a real-time decision support system, as well as off-line recommendations for assets and planning projects.

Achieving impressive results

When paired with deep domain knowledge and hydroinformatics, the digital twin can be used to improve outcomes.

Reducing OPEX and CAPEX

The digital twin provides continuous, real-time optimization and extremely accurate predictions to increase the efficiency and resilience of an asset, process, or system by leveraging prior data and automatically calibrating it to better represent the infrastructure.

Reducing downtime and maintenance expenses

Operators are given decision intelligence to help them detect and diagnose operational problems and initiate proactive maintenance or asset replacement.

Providing utility leaders with the tools they need to stay ahead of workforce concerns.

Rather than starting from zero, data from the digital twin assist new operators in picking up where their predecessors left off, driving incremental improvements when staff churn occurs.

Providing insight into the interdependence of assets

For example, how does a failing pump affect a drinking water network, and what strategy is required to compensate in the short term and enable optimization?

Three Steps to Boost Your Digital Twin

Examine your existing status

Examining all areas where predicted or more detailed data and information can assist your utility in making more timely or efficient operational or planning choices.

Ask these questions:

What real-time data are we now able to access?

Are we making use of the information to make improvements?

Do we have reliable forecasts?

Do we act on our data and use it to make sound operational or commercial decisions?

Where could we use tools to attain our goal?

How may we use the current infrastructure to offer more cost-effective capital improvement programs?

Determine the value that a digital twin can provide to your utility.

This is a low-cost way to learn about the obstacles and data requirements for creating a digital twin, as well as the justification for deployment, which could include a return-on-investment study.

Ask these questions:

Is the digital twin capable of providing real-time decision assistance, making recommendations to operators based on existing or projected conditions?

Can the digital twin automatically optimize and control network assets, as well as offer alarms when system anomalies occur?

Are there any restrictions on the types of data that the digital twin can use?

How can our utility effectively and economically analyze the ROI of adopting a digital twin in our specific context?

Set priorities plans based on your specific needs.

Make these questions clear:

Is the digital twin comprehensive enough to allow you to fully use the technology’s potential?

Will the provider collaborate with you to optimize the digital twin as operating conditions change?

Is the provider well-equipped to serve your utility and provide continuing support?

Sharing successful results

Using digital twin technology to optimize processes

EWE WASSER GmbH (EWE) in Germany desired a system that would optimize energy consumption associated with aeration while also improving safety through better system control of chemical usage at the Cuxhaven treatment plant.

EWE collaborated with Xylem to construct and operate the Xylem Treatment System Optimization, which employs machine learning to create models of the carbon, nitrogen, and phosphorus elimination processes using data from the plant’s SCADA system.

The final results have shown a 30% reduction in aeration energy usage, equating to 1.2 million kWh per year, while guaranteeing that all plant effluent concentrations remain in regulatory compliance.

Using the digital twin to optimize networks

To create a smart system capable of reacting reliably to abrupt rainy weather occurrences, the City of South Bend, Indiana constructed an operational digital twin that optimized its existing infrastructure network using artificial intelligence.

Xylem is now collaborating with South Bend on a revised design that includes a monitoring system comprised of more than 165 sensors and software agents strategically placed throughout the City’s urban watershed.

The smart sewer system will allow the City to meet the criteria of a wastewater consent agreement while investing 60% less capital than was initially envisaged.

“From a purely financial standpoint, we spent $400 million less than we had anticipated.” Kieran Fahey, Director, City of South Bend

When the City of Columbus, Ohio, sought innovative control strategies to help reduce sewer overflows and discover ways to cut operational and maintenance costs while maximizing asset utilization, the Department of Public Utilities collaborated with Xylem to build a solution.

At the asset level, digital twin technology is being used.

When a large wastewater utility in Europe faced cavitation difficulties at one of its treated sewage water pumping stations, the utility installed an asset-level digital twin application as part of its control approach.

The pumps’ acceptable operating parameters were defined using SAM PRO, Xylem’s Smart Asset Management Performance & Reliability Optimization system.

That increased total system efficiency, resulting in substantial OpEx and CapEx savings.

Xylem ebook can be reached through our Knowledge Hub

Reference

[1] Digital-twin-ebook_final_oct2022, Xylem Decision Intelligence [online] Available at: https://www.xylem.com/en-eg/brands/xylem-vue/unmatched-expertise/digital-twin/

[2] Hydroinformatics, limswiki org, [online] Available at: https://www.limswiki.org/index.php/Hydroinformatics

[3] From Digital Twins To Decision Intelligence, water online, [online] Available at: https://www.wateronline.com/doc/from-digital-twins-to-decision-intelligence-0001