AI Fouling Identification
AI assistanted intelligent early warning control system for membrane fouling
AI Fouling Identification is an innovative membrane fouling prediction and control system that leverages machine learning and visual non-destructive monitoring to deliver real-time insights. It accurately predicts membrane fouling dynamics and identifies primary fouling substances, enabling targeted treatment. The significant improvement in accuracy and effciency of membrane fouling prediction helps to reduce maintenance costs and operational downtime, whereas the rich data it generates supports the optimization of water treatment systems, contributing to more sustainable water management.
Team member(s)
Dr Shang Wentao* (Alumnus, School of Energy and Environment, City University of Hong Kong)
Mr Lee Po-hong Vincent (PhD, School of Energy and Environment, City University of Hong Kong)
Miss Liu Xinwen (Xi'an Jiaotong University)
Miss Wang Yuchen (Jinan University)
Mr Wu Maogang (Jinan University)
Mr Tang Zhenzhou (Jinan University)
* Person-in-charge
(Info based on the team's application form)
Mr Lee Po-hong Vincent (PhD, School of Energy and Environment, City University of Hong Kong)
Miss Liu Xinwen (Xi'an Jiaotong University)
Miss Wang Yuchen (Jinan University)
Mr Wu Maogang (Jinan University)
Mr Tang Zhenzhou (Jinan University)
* Person-in-charge
(Info based on the team's application form)
Achievement(s)
- CityU HK Tech 300 Seed Fund (2025)
- Third Prize, The 17th National University Student Social Practice and Science Contest on Energy Saving and Emission Reduction (2024)
- Silver Award, Finals of Jinan University Competition, China International College Students’ Innovation Competition 2024