AI USE CASE
AI Water Treatment Chemical Dosing Optimizer
Automatically optimize chemical dosing in water treatment plants using real-time sensor data and ML.
What it is
Machine learning models continuously analyze water quality sensor readings—turbidity, pH, conductivity, flow rate—to recommend or automatically adjust chemical dosing in real time. Utilities typically report 15–30% reductions in chemical consumption and a 10–20% decrease in energy costs associated with treatment processes. Tighter process control also reduces the risk of regulatory non-compliance due to dosing errors. Integration with existing SCADA or IoT sensor networks allows rapid deployment with minimal disruption to operations.
Data you need
Historical and real-time water quality sensor data (pH, turbidity, conductivity, flow rate) along with corresponding chemical dosing records and treatment outcomes.
Required systems
- erp
- data warehouse
Why it works
- Establish a robust sensor maintenance and calibration schedule before training any ML model.
- Involve plant operators early in the project to build trust in automated recommendations through transparent explainability.
- Implement a continuous retraining pipeline that incorporates new seasonal and operational data regularly.
- Start with a decision-support (advisory) mode before moving to closed-loop automated control.
How this goes wrong
- Sensor data quality is poor or inconsistently calibrated, leading to unreliable model inputs and unsafe dosing recommendations.
- Operations staff distrust automated recommendations and override them systematically, negating efficiency gains.
- Model performance degrades seasonally as raw water composition changes and the model is not retrained.
- Integration with legacy SCADA systems proves more complex than anticipated, significantly delaying deployment.
When NOT to do this
Do not deploy closed-loop automated dosing control without first running the model in advisory mode for at least one full seasonal cycle, as raw water variability can cause dangerous under- or over-dosing.
Vendors to consider
- Suez Water Technologies (Aquadvanced)www.suez.com/en/our-offering/our-products-and-services/water-technologies-and-solutions/digital-solutions/aquadvanced →
- Xylem Vue (formerly Xylem Analytics)www.xylem.com/en-us/products--services/digital-solutions/xylem-vue/ →
- Veolia Hubgradewww.veolia.com/en/our-offer/solutions/hubgrade →
- Idrica GoAiguawww.idrica.com/goaigua/ →
Sources
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