SCADA & AUTOMATION RESOURCES
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The water sector, facing escalating demands and aging infrastructure, cannot afford to be left behind in the adoption of artificial intelligence (AI). Embracing AI is not just about efficiency; it's about ensuring future resilience and continued service delivery in a world increasingly reliant on intelligent systems.
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Water utilities are under mounting pressure to modernize aging infrastructure while keeping budgets under control, forcing owners and contractors to deliver reliable projects with leaner teams, tighter windows, and greater scrutiny. Hyper-detailed modeling is emerging as a critical solution for these challenges.
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While far from prolific, applications of artificial intelligence (AI) in the water and wastewater industry are nothing new. AI and machine learning have been used for data analytics for years. However, for small utilities and those with an aging workforce, these tools seem too high tech and costly to be practical. This doesn’t have to be the case, though. AI tools — particularly generative AI (Gen AI) and large language models (LLMs) — are able to address critical workforce shortages and resource constraints within the water and wastewater industry.
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The full potential of smart water infrastructure is within reach — if our digital systems work together and share critical data.
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Pumps are power-hungry and thus expensive to run, but San Jose Water shows how data-driven technologies and strategies can bring the cost down for utilities.
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Traditional analog devices are increasingly being replaced by digital solutions, and communication protocols like CANopen are playing a key role in this transition. This shift calls for engineers to assess whether digital pressure transmitters are the best fit for their specific applications.
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The state of America’s crumbling infrastructure continues to be a perennial concern as the scale of the problem continually outpaces both the funding and the human resources needed to solve it. Engineers have the solution — AI systems that offer unprecedented speed and potential cost savings — but to leverage its full potential, engineers need to take on a new role — and potentially a new business model.
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Non-revenue water is a global problem. Around 30% of drinking water is lost on its way to the consumer, imposing a huge economic loss that increases the overall cost of water treatment. The good news: By combining smart metering, hydraulic modeling, and AI, utilities can effectively increase their operational efficiency, reduce water losses, and optimize the utilization of increasingly scarce resources.
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When people talk about artificial intelligence, they tend to sort into four camps — doomers, gloomers, bloomers, and zoomers. The doomers fear the robot apocalypse. The gloomers worry AI will automate away all the meaningful jobs. The bloomers are optimistic about AI innovation but want to take a measured approach with guardrails as the technology develops. And the zoomers? They're already deep into it and want minimal regulation to accelerate progress. But I'd like to add a fifth mindset to the mix: the loomers.
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As climate change continues to intensify, utilities face a growing list of challenges from unpredictable storm events and aging infrastructure to rising energy demands and water scarcity. These evolving external pressures are forcing utility leaders to reimagine infrastructure and operations, adopt resilient systems, and pursue sustainable practices grounded in data.