Inside the K-Water Pilot: The Chemical Challenge
In our last newsletter, we shared the origins of NAIAD—how an early need for precision and stability in water purification led to the development of an intelligent support system for operators and infrastructure.
Today, we look deeper into the first technical breakthrough that proved this concept: The application of AI to one of the most sensitive processes in water treatment—chemical dosing.
The Problem: Water Quality Variability and the Limits of Manual Control
Chemical dosing in water purification is never static. It changes based on raw water conditions—factors like turbidity, temperature, and seasonal impacts such as rainfall.
Even experienced operators rely on real-time observations and experience-based adjustments, but as variability increases, so does the potential for over- or under-dosing, leading to:
- Excess chemical consumption
- Unnecessary operational costs
- Risk of compromising water quality
The goal of the pilot at K-Water was to see if AI could support this process by predicting optimal coagulant injection rates using continuous sensor data and learning algorithms.
The Approach: AI-Assisted Decision Support
Rather than automating blindly, the system at K-Water was designed to complement operator expertise:
- Continuous data ingestion from water quality sensors
- Real-time algorithmic analysis to predict coagulant dosing needs
- Automated adjustments based on algorithmic outputs, while keeping human operators informed and engaged in decision loops
This approach meant that even with rapid shifts in water quality, the AI could:
- Maintain optimal water quality
- Stabilize the dosing process
- Reduce unnecessary chemical use
The Result: Reliable, Uninterrupted Operation
Over the course of the pilot:
- The AI-assisted system operated continuously for three weeks
- It achieved over 98% accuracy compared to historical dosing decisions
- Operators observed increased consistency and reduced manual intervention requirements
Importantly, the system proved that AI could sustain operational stability, even under variable conditions, without replacing human oversight.
This was the first demonstration of what a learning water management system could deliver when integrated into live operations.
Looking Ahead: Scaling Lessons from Chemical Control to Full Plant Optimization
The K-Water pilot was only the beginning. The success of AI-assisted chemical dosing opened the door to broader applications across the entire water treatment ecosystem, from energy optimization to reservoir flow management.
In our next issue, we’ll examine how these learnings were expanded to:
- Predictive pump control to reduce energy consumption
- Smart scheduling for reservoir valve operations
- Integrated monitoring for plant-wide efficiency
These are not future concepts—they’re operational realities.
Stay tuned.
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