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Tel Aviv University’s scientists have designed an AI system that predicts contaminants in wastewater with around 90% accuracy.
By Shula Rosen
Scientists at Tel Aviv University have discovered a way to use artificial intelligence for more efficient wastewater management.
This marks a sea change in traditional processes, which require costly and time-consuming transfers of samples to labs for tests.
Conventional waste water tests are error-prone, and the delay before receiving the results means that contaminants remain in the system for longer.
Tel Aviv University’s Hydrochemistry Laboratory scientists have designed an AI system that predicts contaminants in wastewater with around 90% accuracy.
Wastewater contaminated by domestic, agricultural, or industrial waste is sent to water treatment plants, where it is purified and returned to the water sources in a form that can be safely used.
A significant issue wastewater treatment facilities face is detecting fluctuations in phosphorus concentration. The scientists developed algorithms to calculate variables such as temperature, precipitation, wastewater characteristics, and chemical and biological properties.
Instead of testing the water and waiting for results, the algorithms can predict and detect the presence of contaminates more quickly and accurately, increasing the efficiency of wastewater treatment.
In addition, with AI tools, researchers created a database to recognize microorganisms like protozoa, filaments, and flocs or clusters of particles. This “library” can be used to detect contaminants more efficiently than traditional methods.
The research, led by PhD student Ofir Inbar and Prof. Dror Avisar from the Hydrochemistry Laboratory at the Porter School of Environmental and Earth Sciences at Tel Aviv University, was conducted in collaboration with Dr. Mony Shahar, Yaakov Gidron and Ido Cohen from the Center for Artificial Intelligence and the School of Computer Science at Tel Aviv University, and Dr. Ofir Menashe from the Kinneret Academic College.
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