What Is The Vision For Smart Wastewater & Sewage 2030/35?

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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infrastructure—pumping stations with no sensors, CSOs with intermittent monitoring, and treatment works running on outdated SCADA—and it’s clear that digital maturity must be built from the ground up in many cases.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.