Artificial Intelligence in water and wastewater utilities is still in its early stages, but it is having a huge impact where it is used, and most organizations already have the data they need to enhance their local water conditions.
A brief introduction to AI in the field of water
Collecting and analyzing past performance data to generate operational insights has long been a source of efficiency and innovation for many firms, both in the water industry and beyond.
However, the full extent of this approach’s potential benefits has yet to be realized.
Vast volumes of data, ranging from the current state of subsurface assets to consumer opinion on social media posts, go mostly unrecorded and unanalyzed.
The information gathered may be erroneous, partial, or otherwise unfit for purpose, and the methods employed to analyze it may not be very enlightening.
Data gathering and analysis used to be time-consuming, labor-intensive and frequently fruitless, but technological advancements have opened up new and exciting possibilities.
Automated data collecting, such as through the use of sensors and artificial intelligence, and the capacity to analyze massive amounts of data through machine learning constitute a significant advancement in this method of gaining insights to improve efficiency.
Artificial intelligence has acquired a lot of interest as a potent tool for solving real-world issues, thanks to its numerous uses.
Artificial Intelligence approaches have been used in water treatment and desalination in recent years to optimize the process and provide practical answers to water contamination and scarcity.
Applications of Artificial Intelligence are also predicted to minimize water treatment process operational costs by lowering costs and optimizing chemical utilization.
Several Artificial Intelligence models have accurately predicted the effectiveness of various adsorbents in the cleanup of a variety of contaminants from water.
This digital technology revolution can help innovative water utilities increase their efficiency.
Water utilities may leverage the knowledge and data available to make better decisions while improving service delivery and lowering costs by leveraging the power of artificial intelligence algorithms and big data analytics.
For water services beginning on this digital transition to enhance their water distribution operations in general, and to solve unaccounted-for-water concerns in particular.
AI improving the water industry
AI will drive a decade of technological investment in water and wastewater operations
Artificial intelligence is being used in water and wastewater systems.
According to a recent industry study, $6.3 billion will be invested in Artificial Intelligence solutions by 2030.
This investment is part of a rising trend in the water sector to use intelligent different infrastructure technologies to “get digital.”
AI in water and wastewater operations will result in huge OPEX
When US utilities spend $300 per customer per year on water and wastewater operations, there is a lot of room for savings.
By decreasing energy expenses, improving chemicals used for treatment and allowing proactive asset maintenance, AI can save 20-30% on operating expenditures (OPEX).
AI will be able to foresee and learn from critical situations at a faster rate
Water main breaches are both financially and socially harmful to utilities.
AI and machine learning can “fingerprint” data patterns that suggest an impending break event and learn from them over time, making alarms more effective.
AI will help operators with superior decision-making intelligence
Operators no longer need to assess complicated factors on their own to make key decisions.
Artificial Intelligence empowers Operator 2.0 – empowered by intelligent suggestions driven by machine learning – whether it’s turning pumps on or off, determining chemical doses, or choosing when to repair assets.
AI will help water and wastewater systems save energy
Energy usage accounts for 25-30% of total operating and maintenance (O&M) expenses, according to the USEPA.
Artificial Intelligence can optimize pump runtimes so that energy is only used when it is required.
For many early Artificial Intelligence users, this represents an immediate cost-cutting victory.
AI will keep the water clean at a low cost
Many enterprises, both public and commercial, are required to meet effluent compliance regulations.
To guarantee that effluent regulations are satisfied and compliance fines are minimized, Artificial Intelligence learns from the unique characteristics of your site.
AI will make data integrity easier
The growth of data accessible to water operations managers has created a data analysis dilemma.
SCADA technologies, CMMS, and social media may help optimize your operations.
Artificial Intelligence can clean, utilize and protect this diverse data, allowing it to be used to provide extremely high recommendations.
AI will maintain institutional knowledge
How can you make sure that a seasoned operator’s essential expertise is passed down when they retire?
Institutional knowledge will be documented and standardized thanks to Artificial Intelligence-powered dashboards.
AI will hasten the transition to value-based asset maintenance.
Early Artificial Intelligence adopters are rapidly abandoning reactive asset maintenance.
Time-based management is simple to administer, but it causes needless downtime and degradation.
Allow Artificial Intelligence to tell your team when and what assets need to be maintained.
AI will power genuinely smart water
The route to Artificial Intelligence adoption allows businesses to seek data-driven, intelligent water system administration.
The result is long-term water management that is robust, sustainable and cost-effective.
Piloting artificial intelligence in water
To assist water utilities in this digital transition, national water sector policies must be revised.
Any water utility with digital data can benefit from current breakthroughs in artificial intelligence and big data.
Piloting the concept of Artificial Intelligence and Hydraulic Modeling for UFW will show how advanced network analysis algorithms improve operational efficiency and service delivery, as well as give the water utility a competitive edge by combining the power of Artificial Intelligence with big data obtained from the SCADA system, as well as data fed from different sensors on the water distribution system.
On the primary water distribution system, an Artificial Intelligence pilot examines numerical UFW and pipe burst assessment, as well as sensor failure methods (pipe diameter over 200 millimeters).
The Artificial Intelligence algorithms are put to the test on a single water distribution network sector or a small water distribution system chosen in collaboration with the water utility based on the following criteria:
1. The water distribution system should always be up to 800 kilometers long, with linear pipe length and characteristics (pipe size, location, and material) provided in digital format (GIS or Hydraulic Modelling 1.0) with adequate precision;
2. The water distribution system shall contain a minimum density of sensors (pressure gauges, micrometers and customer meters) with at least two years of historical pressure and flow data, as well as the digitized status of pumps and valves.
3. The availability of a traditional hydraulic model, which would be advantageous but not essential.
References
[1] Jenny, H., Alonso, E. G., Wang, Y., & Minguez, R. (2020). Using Artificial Intelligence for Smart Water Management Systems. (online) available at: https://www.adb.org/sites/default/files/publication/614891/artificial-intelligence-smart-water-management-systems.pdf
[2]10 Ways AI is Changing the Water Industry, 27 April (online) available at: https://www.innovyze.com/en-us/blog/ai-in-water-10-ways-ai-is-changing-the-water-industry
[3] Alam, G., Ihsanullah, I., Naushad, M., & Sillanpää, M. (2022). Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects. Chemical Engineering Journal, 427, 130011. (online) available at: https://www.sciencedirect.com/science/article/abs/pii/S1385894721015965
