{"id":639275,"date":"2024-09-23T18:00:10","date_gmt":"2024-09-23T15:00:10","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/artificial-intelligence-model-helps-produce-clean-water\/"},"modified":"2024-09-23T18:00:10","modified_gmt":"2024-09-23T15:00:10","slug":"artificial-intelligence-model-helps-produce-clean-water","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/artificial-intelligence-model-helps-produce-clean-water\/","title":{"rendered":"#Artificial intelligence model helps produce clean water"},"content":{"rendered":"
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\n Overview of conductivity-based water ion concentration prediction using machine learning (random forest) techniques. Credit: Korea Institute of Science<\/a> and Technology<\/a>
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About 2.2 billion people, more than a quarter of the world’s population, lack access to safe, managed drinking water, and about half of the world’s population experiences severe water scarcity at some point during the year. To overcome these shortages, huge socioeconomic costs are being spent on sewer irrigation and alternative water sources such as rainwater reuse and seawater desalination.<\/p>\n

Furthermore, these centralized water distribution systems have the disadvantage of not being able to respond immedia<\/a>tely to changes in water demand. Therefore, there is a growing interest in decentralized water production technologies, which are electrochemical-based technologies that are easy to adopt, such as capacitive deionization and battery electrode deionization (also known as faradaic deionization).<\/p>\n

However, the existing water quality measurement sensors used in electrochemical-based technologies do not measure and track individual ions in water, and have the limitation of roughly inferring water quality conditions from electrical conductivity.<\/p>\n

Dr. Son Moon’s research team at the Korea Institute of Science and Technology (KIST) Water Resource Cycle Research Center, in collaboration with Professor Baek Sang-Soo’s team at Yeongnam University, has developed a technology that uses data-driven artificial intelligence to accurately predict the concentration of ions in water during electrochemical water treatment processes.<\/p>\n

Their paper is published<\/a> in the journal Water Research<\/i>.<\/p>\n

The researchers first built a random forest model, a tree-based machine learning technique utilized for regression problems, and then app<\/a>lied it to predict ion concentrations in electrochemical water treatment technologies.<\/p>\n