Water Treatment in 2030:
Why the Biggest Shift Isn't the Technology, It's the Trust
Water Treatment in 2030: Why the Biggest Shift Isn't the Technology, It's the Trust
Sub-heading: We have the data. We have the algorithms. But the real revolution is about trust, not just technology.
Water Treatment in 2030: Why the Biggest Shift Isn't the Technology, It's the Trust
Sub-heading: We have the data. We have the algorithms. But the real revolution is about trust, not just technology.
I was talking with a senior SCADA engineer last month, someone with deep experience implementing machine learning for water quality, and our conversation took a fascinating turn. We were discussing how major SCADA vendors are starting to build more "AI" functions directly into their platforms.
"The funny thing is," he said, "cities are struggling to understand what they're even buying. We've had PID loops and basic machine learning optimizing processes for years. Is that AI? Or is it only AI when it's one of these new large language models everyone's talking about?"
His point was sharp and clear: The industry is in a state of definitional flux. We're grappling with a technology that isn't entirely new but is evolving at a breakneck pace. The conversation wasn't about a lack of trust in the algorithms, but a lack of clarity about what they are, what they can become, and how they fit into established concepts like digital twins.
It crystallized the central question for me: Beyond the hype and the confusing terminology, how will AI really change drinking water treatment by 2030?
The Current State: Islands of AI in a Sea of Tradition
The industry today has "islands of AI"—applications of advanced data analysis that are precursors to a true revolution. They generally fall into these key areas:
Advanced Asset Management: While true real-time predictive maintenance is still emerging, many utilities use sophisticated software for preventative maintenance. These systems analyze historical work orders, manufacturer data, and corrective maintenance logs to assign risk scores to assets, helping to allocate resources and schedule repairs before a catastrophic failure occurs.
Strategic Forecasting & Efficiency: Utilities are increasingly using models—which are a form of AI—for long-term planning. This includes forecasting reservoir capacity based on climate data, predicting the likelihood of seasonal algal blooms, or optimizing pump schedules to minimize energy costs based on hydraulic models and electricity tariffs.
Short-Term Demand Forecasting: On a more immediate timescale, utilities use models that analyze historical usage, weather forecasts, and daily patterns to predict water demand for the next 24-48 hours. This helps operators make more precise decisions about production rates and storage levels.
Automated Compliance Analysis: Utilities use specialized software to automatically aggregate vast amounts of water quality data for regulatory reporting. These systems often include statistical tools to analyze trends and flag any parameters that are approaching compliance limits, allowing for proactive adjustments.
These are valuable, important steps. But they are enhancements to the existing operational model, not a transformation of it. So, what does that transformation actually look like?
Five Ways AI Will Reshape the Plant of 2030
As the technology matures, I believe AI will move from being a peripheral tool to the central nervous system of the treatment plant. Here are five areas where the change will be most profound:
1. From Reactive to Predictive: The Rise of "Soft Sensors" Instead of waiting hours for lab results, plants will use AI-powered "soft sensors" to predict complex parameters like Total Organic Carbon (TOC) or specific contaminants in real-time, using data from simpler, faster online sensors (like turbidity, pH, and UV254). This allows for dynamic, predictive control—adjusting ozone or coagulant doses based on water that's about to enter the plant, not water that passed through two hours ago.
2. The Self-Optimizing Plant: Digital Twins in the Driver's Seat Full-plant digital twins—virtual models of the entire treatment process—will become common. These models, powered by reinforcement learning, will constantly run simulations in the background, testing thousands of operational tweaks to find the absolute optimal strategy for energy use and chemical consumption. The potential gains are significant; according to global design and consultancy firm Arcadis, this kind of AI-driven optimization can lead to energy savings of up to 30%.
3. Accelerating Discovery: AI in the R&D Lab Machine learning is already being used to accelerate the discovery of new materials. By 2030, this will be standard practice for developing next-generation membranes and adsorbents tailored to remove specific emerging contaminants like PFAS. AI can analyze vast datasets of molecular structures to predict performance, with research platforms like Orbital Materials aiming to make the discovery process 10 times faster than traditional lab-based methods.
4. From Alarms to Insights: True Anomaly Detection This moves far beyond simple high/low alarms. True anomaly detection will become standard, especially in distribution networks. By predicting pipe failures before they happen, these systems can have a major impact on reliability. Market intelligence firm Bluefield Research reports that AI-powered analytics can reduce water losses by 20-40% by identifying leaks and vulnerabilities in the network.
5. The Augmented Operator: A New Role for Human Expertise Perhaps the most important shift will be in the control room itself. AI won't replace operators; it will augment their abilities. The job will shift from manual control and routine monitoring to higher-level system supervision. An operator's expertise will be used to interpret complex AI recommendations, manage exceptions, and troubleshoot problems the AI can't solve—freeing them from the mundane to focus on the critical.
The Reality Check: Challenges and Open Questions
Of course, this future isn't guaranteed. Significant hurdles remain:
The Data Dilemma: AI models are only as good as the data they're trained on. Fragmented data systems, uncalibrated sensors, and critical data gaps can undermine even the most sophisticated algorithm.
The "Black Box" Problem: If an AI system recommends a critical change, how do we verify it's the right one? Ensuring transparency and accountability in AI decision-making—often called the "Black Box" Problem—is essential for an industry where public health is non-negotiable.
The Human Factor: The biggest challenge might be cultural. How do we build trust? How do we retrain a workforce for a world where their partner in the control room is an algorithm?
The promise here isn't just efficiency. It's about resilience—the ability to deliver safe, reliable drinking water in a world of climate shocks, aging infrastructure, and emerging threats. AI won't replace our expertise, but it can amplify it.
Questions I'm Still Wrestling With:
This is where the conversation really begins. I'd love to hear your perspective on the questions I'm grappling with:
For plant operators: What would an AI tool have to do to truly earn your trust for a critical decision, like adjusting disinfection dosage?
From a regulatory standpoint: What is the one thing you believe needs to change to safely encourage AI adoption while protecting public health?
Will AI create a new "digital divide" between large, well-funded utilities and smaller, rural systems? How do we prevent that?
Let's discuss it in the comments.
