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A new artificial intelligence model developed by researchers in South Korea could help improve access to clean drinking water worldwide. The model accurately predicts the concentrations of specific ions in water during electrochemical treatment processes, addressing a key limitation of existing water quality sensors.
Currently, over 2 billion people lack access to safely managed drinking water. Existing centralized water systems struggle to adapt quickly to changing demands. This has sparked interest in decentralized, electrochemical water treatment technologies like capacitive deionization. However, the sensors used in these systems can only roughly estimate overall water quality from electrical conductivity measurements, without tracking individual ion concentrations.
To overcome this, researchers from the Korea Institute of Science and Technology (KIST) and Yeongnam University developed a random forest machine learning model to predict ion concentrations in electrochemical water treatment. Their model accurately forecasted both the electrical conductivity of treated water and the concentrations of specific ions like sodium, potassium, calcium and chloride.
Key findings:
The model achieved high accuracy (R² of ~0.9) in predicting conductivity and individual ion concentrations
Predictions needed updating every 20-80 seconds for optimal accuracy
The random forest approach requires 100x less computing power than complex deep learning models
"With this technology, the concentration of individual ions can be monitored more precisely, contributing to the improvement of social water welfare," said lead researcher Dr. Moon Son of KIST.
The model could potentially be integrated into national water quality monitoring networks to enable more precise tracking of specific ions. This would require water quality measurements at least once per minute initially to train the model.
By enabling more accurate and efficient monitoring of water quality at the ion level, this AI-powered approach could help expand access to clean drinking water in water-scarce regions globally. The research demonstrates how machine learning can enhance emerging water treatment technologies to address critical sustainability challenges.
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