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MODELLING AND ARTIFICIAL INTELLIGENCE (AI)
Remote Modelling & AI-enabled Process Control
Research in Modelling and Artificial Intelligence (AI) Machine Learning delves deeper into the development of innovative advancements in technologies (such as environmental modelling and simulations tools, smart sensors) for urban planning, water / wastewater management, environmental modelling and climate impact. The use of mathematical models and digital visualization help find new modern solutions for challenges in climate change and continued climate resilience. With the advanced development of environmental modelling and simulations tools for analysis, identification of potential issues, and offering of improved solutions to Industry needs for future growth,
Previously, Environmental Process Modelling Centre (repositioned from DHI-NTU Centre at NTU), was an initiative in 2007 to support the development of the local environmental and water industry in Singapore. It was among the first batch of Centres of Excellence, supported by the Environment & Water Industry Programme Office (EWI) was set up by the Ministry of the Environment and Water Resources (MEWR) to spearhead the development of the environment and water industry.
Areas of research
UAV Remote Sensing of turbidity in coastal waters
- Remote sensing for the protection of water quality
Modelling & Hydrodynamics
- Contaminant Fate
- Transport in Water, Ocean Outfalls & Intakes
Artificial Intelligence & Machine Learning
- Industrial and Municipal Water System Simulation & Process Controls,
- Molecular Dynamics
DRFM hybrid model to optimize energy performance of pre-treatment depth filters in desalination facilities
Numerical simulation of air pressurization within dropshafts
Mixing behaviors of inclined dense jets in flowing currents
Molecular Dynamics (MD) simulation
Molecular dynamics simulations in membrane material design for desalination
MOF-Based nanoporous membranes for seawater desalination
Study on multi‐port jets in shallow water
Lab safety enquires: Ms Li Min (Lab Manager) [email protected] / (65) 6790 5077