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Mar 13
Sand Technologies
For water plant managers looking to leverage AI without significant interruptions, three quick AI projects are explicitly designed for rapid results and minimal disruption. From predicting equipment maintenance needs to enhancing water quality to demand forecasting, these solutions showcase the power of AI in water treatment with quick deployment.
These projects ensure meaningful predictive capabilities to operations without overhauling existing processes. Moreover, they provide a stepping stone to more extensive AI integrations in the future.
Nearly all water utilities use SCADA data to monitor their assets and operations. AI goes a step further by adding predictive capability. Imagine knowing when a problem is likely to occur, rather than just determining if a problem exists and then addressing the issue. Predictive maintenance powered by AI transforms water utility maintenance practices, helping plant managers practice proactive vs reactive maintenance.
Using historical SCADA data for reference, AI monitors equipment performance in real time, analyzing sensor data to detect anomalies that match the activity before past failures, alerting management that a potential failure is likely. Traditional maintenance often relies on fixed schedules or simply responding to breakdowns. AI in water treatment enables early identification of issues — such as pump malfunctions or filter inefficiencies — before they escalate.
This proactive approach significantly reduces unplanned downtime, increases operational efficiency, reduces maintenance costs and extends equipment lifespan. ABB surveyed heavy asset industries across all sectors and discovered that the average cost per hour for unplanned downtime was $125,000. Predictive maintenance is a game-changer for cutting these expenses using smarter, faster solutions.
An example of how it works is AI systems analyzing pump vibration data or monitoring filter pressure and comparing it to the historical SCADA data to predict maintenance needs. Imagine receiving an alert that a pump will likely fail within 48 hours. AI makes this possible by identifying subtle patterns and anomalies humans might miss.
With quick deployment options, this technology can be integrated seamlessly into existing systems, ensuring water plant managers can harness the benefits of AI without significant disruptions to operations. For water plant managers, this means fewer surprises, optimized efficiency and a smarter approach to equipment maintenance.
Water quality is the most critical aspect of water management. Most water utilities use SCADA data to monitor water quality. AI uses the SCADA data but upgrades water quality management.
It leverages advanced machine learning algorithms to revolutionize the detection of minute anomalies in quality metrics such as pH, turbidity and dissolved oxygen levels. AI automation can immediately adjust chemical dosage and other process parameters to maintain optimal water quality. In addition, AI can learn the optimal chemical dose for different water sources and treatment conditions. This capability minimizes chemical waste and reduces operational costs. A notable quick-win example is Thames Water. The utility quickly reduced chemical costs by 20% while improving water quality by 15%.
AI systems can seamlessly integrate into existing operations, providing an efficient and modern solution to maintaining water quality standards. With the integration of AI in water treatment, water plant managers can expect actionable insights and tangible benefits within weeks. This can in turn accelerate regulatory reporting as AI systems streamline data gathering and analysis, eliminating manual bottlenecks.
One of the main goals of water utilities is a steady and reliable water supply. Historically, utilities operated in a reactive mode — fixing a problem when it arises. This approach can leave customers without water at inconvenient times and longer than necessary.
AI can improve demand forecasting by analyzing historical water use data, weather patterns and population growth trends to accurately forecast water demand. It can also detect trends and patterns in data that might be missed by traditional methods, allowing for more accurate predictions, optimal water allocation and conservation measures to mitigate water scarcity.
Similarly, AI enables real-time monitoring of water use and system conditions, allowing for dynamic adjustments to distribution based on actual demand. It can also be used with smart sensors to detect leaks in water infrastructure, allowing for prompt repairs that prevent water loss. With this, utilities can optimize water supply, reduce waste and ensure efficient water distribution by accurately forecasting demand.
Water treatment is a critical service that requires precision and timely action. It is also a highly regulated industry. The AI solutions above can provide quick wins that immediately impact water treatment efficiency, streamline regulation reporting and upgrade equipment maintenance to a proactive approach. And, these projects are an accessible and scalable introduction to leveraging AI in water treatment.
Companies that have successfully navigated AI journeys began with small projects that delivered fast results. This strategy helps a company learn existing knowledge gaps, build confidence in the technology and determine how to scale the technology across the organization for further gains.
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