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We’ve designed this conference with long-term value in mind—not just starting from today’s technologies, but also from where this sector might be heading. Instead of asking utilities simply what they want to learn in 2025, we asked a more fundamental question: What would success look like for a smart wastewater system say by 2030/35? What kind of operational capability might a wastewater utility need to build—not only to meet compliance, but to reshape how wastewater utilities manage risk, engage the public, and optimise infrastructure?
Here’s what you told us—organised by theme. But first, a perspective from one of the UK’s leading wastewater asset managers.
“Despite the clarity of your 2030/35 vision question, the path to smart wastewater transformation is anything but straightforward. Utilities like ours face deep-rooted legacy challenges—fragmented data systems, ageing infrastructure, and chronic underinvestment in digital capability. Many networks still lack even basic, consistent telemetry across critical assets, making it difficult to build the real-time visibility that intelligent monitoring demands. Add to that the sheer volume of analogue infrastructure—pumping stations with no sensors, CSOs with intermittent monitoring, and treatment works running on outdated SCADA—and it’s clear that digital maturity must be built from the ground up in many cases.
The organisational and operational hurdles are equally formidable. Deploying a unified smart system requires more than sensors and dashboards—it requires cultural change, data governance frameworks, cybersecurity maturity, and workforce upskilling at scale. Utilities must break down silos between IT, operations, and compliance teams to enable meaningful integration. Regulatory pressure adds urgency but also risk: with expectations rising faster than budgets, there’s a real danger of fragmented solutions that solve today’s reporting problems but fail to build the operational intelligence needed for tomorrow. The smart wastewater utility of 2030/35 is within reach—but only if we treat it as a whole-system transformation, not a tech retrofit”.
This conference is designed to help the industry address key challenges like these as well as navigate what’s next. So let’s return to the question at hand: what could the future vision circa 2030 actually look like?
Real-Time Event Detection and Response
Achievable Goal: Utilities will have near real-time visibility of storm overflows, blockages, and asset performance across their networks.
What It Looks Like:
• A dense network of sewer level, flow, and pressure sensors is installed at key network pinch
points (e.g. pumping stations, CSO chambers, rising mains).
• Machine learning algorithms detect anomalous trends in flow levels or pump runtimes,
indicating a blockage or partial collapse.
• Operators receive automated alerts with location, severity level, and confidence score—direct
to handhelds or control rooms.
• Automated actuators trigger increased pumping capacity or diversion where
infrastructure allows.
Integration Example:
• Sensor data is fed into a cloud-based digital twin that models the hydraulic behaviour of the
network in real time.
• These twins integrate rainfall radar forecasts to simulate probable overflow sites up to 6 hours
in advance.
• This enables preemptive tank drawdown, optimized routing, and early contractor dispatch.
Accurate Event Duration Monitoring (EDM) and Regulatory Reporting
Achievable Goal: Provide 95–100% accurate duration and volume reporting for all permitted storm overflows and unpermitted spill events.
What It Looks Like:
• Each storm overflow is fitted with a smart EDM logger capable of measuring start/stop times
and flow volumes—not just presence/absence.
• AI-based signal interpretation removes false positives (e.g. sensor fouling, telemetry dropouts)
and flags anomalous durations.
• Daily automated reporting to regulators, with real-time dashboards shared with internal ops
and external stakeholders.
Public Transparency Example:
• An open access online map shows real-time status of CSOs (green = inactive, red =
discharging), with timestamped spill history, spill volumes, and cause (e.g. rainfall-triggered
vs. blockage).
• Utilities publish monthly summary data, with benchmarking against targets and environmental
impact statements.
End-to-End Network Visibility and Root Cause Analytics
Achievable Goal: Gain full visibility of asset performance and interactions, allowing root cause diagnosis across treatment works, pumping stations, rising mains, and sewer lines.
What It Looks Like:
• Smart sensors at pumping stations log flow rates, pump health, and energy usage, integrated
with SCADA and AI to forecast pump failure risk.
• Treatment works automatically receive alerts about incoming storm surge based on upstream
network sensors, allowing proactive process adjustment.
• Data lake aggregates maintenance logs, asset age, flow anomalies, and rainfall patterns to
build predictive maintenance schedules.
System Integration Example:
• GIS-integrated dashboards display real-time flow paths and asset performance across the
full catchment.
• Operators can trace a specific overflow incident back through the network to find the root
cause—e.g. pump failure at Station X, causing surcharging at Manhole Y, leading to CSO
activation at Site Z.
Proactive Compliance and Environmental Protection
Achievable Goal: Wastewater utilities move from reactive compliance to proactive environmental stewardship, with systems geared towards real-time pollution prevention.
What It Looks Like:
• High-risk catchments with sensitive water bodies (e.g. bathing waters, SSSIs) have additional
layers of monitoring, including water quality sensors at discharge points.
• AI models forecast pollution risk days ahead based on network conditions and
weather predictions.
• Spill prevention teams are dispatched to priority sites before spills occur, supported by
autonomous tankers or local retention strategies.
Public Impact Example:
• Live data feeds into public signage at beaches and rivers to indicate current water quality risk
status (like air quality indexes).
• Community alerts via apps notify local users if a spill has occurred or is likely to occur, building
transparency and trust.
Cross-Utility and Stakeholder Data Collaboration
Achievable Goal: Integrated data sharing between wastewater operators, regulators, environmental groups, highways authorities, and flood management bodies.
What It Looks Like:
• Shared cloud platforms offer API access to near-real-time flow data, event notifications, and
asset conditions.
• Flood prevention efforts integrate sewer system data with surface water and river catchment
models to predict compound risks.
• Environmental regulators have direct read-only access to CSO telemetry, removing the need
for manual data uploads or report chasing.
Collaborative Outcome Example:
• Joint pollution investigations are faster and more accurate because all stakeholders are
working off the same real-time datasets and visualisation tools.
• Utilities can demonstrate to regulators that a given event was unavoidable due to exceptional
rainfall or was mitigated by early intervention.
Smart, Dynamic Investment Planning
Achievable Goal: Use data from smart systems to drive targeted investment that reduces spills,
lowers energy use, and extends asset life.
What It Looks Like:
• Dynamic risk-based prioritisation models continuously update based on asset health,
performance trends, and environmental sensitivity.
• Capex planning tools integrate sensor and event data to justify upgrades or rehab work, with
clear environmental return on investment forecasts.
• Digital twins simulate the impact of proposed interventions (e.g. upsizing pipes, adding storage
tanks) on long-term spill reduction.
Many utilities are now ALSO looking beyond static performance models toward systems that are dynamic, data-rich, and responsive in real time. This isn’t a universal requirement—but it reflects a growing recognition that traditional, reactive approaches may no longer be fit for purpose in a world of climate pressure, regulatory scrutiny, and rising public expectations.
Multiple Technical Pathways, One Strategic Direction
How individual organisations move toward this vision will vary. Some are exploring AI-enabled digital twins. Others are investing in distributed sensors and localised analytics. There’s no single blueprint, but there is a common direction: toward systems that provide visibility, enable early intervention, and support increasingly automated, data-informed decision-making.
In the words of one CEO of a wastewater utility we spoke to during the consultation phase
“We’re moving from a paradigm of response to one of monitoring and of prevention. The focus is shifting from reacting to incidents, to engineering out their likelihood entirely”.
Resilience Built on Anticipation
For some utilities, that might mean using rainfall forecasting to activate upstream storage ahead of peak loads. For others, it could involve optimising pump cycles based on real-time ammonia and conductivity signals.
Real-World Pilots, Real-Time Results
These shifts are already producing results. Pilot projects are beginning to show that predictive controls, deployed at the right locations, can reduce discharge events, defer capital upgrades, and improve asset efficiency. In parallel, a more data-transparent culture is emerging—where customer-facing dashboards, real-time alerts, and historic performance metrics become not just tools for compliance, but mechanisms for building trust.
Strategic Phasing Enables Strategic Delivery
Of course, transformation on this scale isn’t delivered all at once. In many cases, the most effective strategy is a phased one: starting in high-risk areas, layering in capability gradually, and aligning each stage with operational maturity and confidence. Utilities are learning that intelligent sequencing—not just ambition—determines the success of these programmes.
Building Whole-System Awareness, Not Fragmented Tools
And the challenge isn’t simply deploying more sensors or running more analytics. It’s integrating data across systems—bringing telemetry, EDM, rainfall, GIS, and SCADA into a single operational picture. For some, that integration is still in progress. For others, it’s already shaping how field teams respond, how risk is prioritised, and how interventions are automated.
Introducing Predictive Tools Safely
The same applies to predictive tools. AI models offer compelling potential, but they still need to be deployed carefully. In most cases, these tools are best introduced gradually—tested in specific environments, validated against live conditions, and refined over time. Full autonomy may not be the immediate goal; confidence and clarity are.
Progress Starts with an Honest Baseline
All of this builds on a realistic baseline. Many wastewater networks still rely on manual monitoring, siloed data, and reactive workflows. That’s not a failure—it’s the starting point. Recognising those operational constraints is key to designing systems that are both intelligent andimplementable.
Compliance Is the Baseline, Not the Vision
What’s also becoming clear is that a compliance-only mindset—while necessary—is no longer sufficient. Future investment decisions will likely need to deliver multiple outcomes simultaneously: reduced environmental risk, improved operational efficiency, enhanced reputational standing, and better service transparency. Regulation may still define the floor—but utilities increasingly want to define the ceiling themselves.
Translating Ideas into Leadership Moves
This conference is designed to help you think through that journey. Whether you’re just beginning to build network visibility, experimenting with real-time control, exploring digital twins, or trialling AI-based forecasting, the agenda reflects a broad spectrum of practical experience. It’s not about prescribing one future—but offering a space to explore the range of viable, forward-facing strategies that can support long-term transformation.
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