Wastewater operations intelligence
Earlier visibility into nutrient risk.
Clearer operator decisions.
Waste Water Care is developing an explainable nutrient early-warning and operator decision-support platform for biological wastewater treatment.
The product program is designed to combine data-quality intelligence, nutrient-risk visibility, operator workflows, source-grounded AI assistance, and evidence-ready reporting.
Current environment: demonstration in development. Predictive outputs shown in the preview use synthetic demonstration data.
Influent Flow
412 m³/h
Dissolved O₂
1.4 mg/L
Temperature
14.2 °C
NH₄-N trajectory · +1h / +2h / +4h
Curated Demo OutputThe operational problem
Operators need context before a process issue becomes an operational surprise.
Wastewater treatment is a dynamic biological process. Changing flow, oxygen conditions, temperature, loading and sensor quality can make nutrient behaviour difficult to interpret from isolated measurements alone.
Waste Water Care is being designed to organize these signals into a clearer investigation workflow while keeping the operator in control.
Data to action
From plant data to an auditable response.
- 01
Understand the plant
- 02
Check data quality
- 03
Review nutrient-risk preview
- 04
Investigate confidence and drivers
- 05
Record operator response
- 06
Generate evidence and reports
The planned workflow combines implemented software functions and clearly labelled demonstration outputs. Production predictive validation is a later phase.
Product capabilities
Six capabilities, each with a stated release state.
Plant & Data Workspace
A bounded workspace that describes one plant, its process units and its measured signals.
Data Quality & Sensor Health
Deterministic checks for gaps, flatlines, stale data and out-of-range values before anything else is shown.
Nutrient Risk Preview
Short-horizon ammonia-risk visualizations shown on synthetic data to illustrate the intended interface.
Alert & Action Workflow
Acknowledge, assign, investigate and record operator actions with a clear audit trail.
Evidence Copilot
A read-only assistant designed to answer only from an approved corpus, with citations and stated gaps.
Evidence Reporting
Draft incident summaries assembled from recorded evidence, always requiring human review.
Human in the loop
Decision support, not autonomous control.
Waste Water Care is designed as a read-only decision-support environment. The operator remains responsible for investigation and operational action.
Explain
Show what the signals indicate and how confident the view is.
Investigate
Give operators drivers, data quality and evidence to check.
Document
Record what was reviewed and what action the operator chose.
No equipment control, setpoint changes, PLC/SCADA write-back or autonomous process action is included in the current demonstration scope.
Initial use case
Starting with ammonia-risk visibility.
The initial product direction focuses on biological/nitrifying wastewater operations where earlier visibility into ammonium/ammonia conditions may help operators investigate process changes before they become larger operational issues.
Nitrate is retained as a secondary demonstration signal. Real predictive performance requires plant-specific data and validation.
Discuss Data ReadinessNH₄-N · NO₃-N
Synthetic Demonstration DataTransparency
Product truth should be visible.
Waste Water Care separates working software, experimental AI, simulated demonstration outputs and future technology so that users can understand exactly what they are viewing.
- Working Prototype
Functional software behaviour. In Stage 1 shown as a release target, not yet implemented.
- Experimental AI POC
An experimental AI function with bounded scope and human review.
- Curated Demo Output
Simulated or prepared output on synthetic data — not generated by a validated model.
- Future Roadmap
Planned future technology. Not part of the current program.
- Not Included
Explicitly out of scope for the demonstration.
Interested in the next validation stage?
The next commercial development step is to understand plant data readiness, operating context and the requirements for a future pilot.