Civil and Environmental Engineering
In this project, digital twins (incorporating both machine learning and deterministic models) of selected water and wastewater treatment technologies will be developed and applied for the purposes of optimising design and performance of these technologies.
School
Research Areas
Computer science and engineering | Chemical engineering | Environmental engineering
- Research Environment
- Expected Outcomes
- Supervisory Team
- Reference Material/Links
The candidate will work with a team of engineers and research students skilled in water and wastewater treatment and will draw on strengths in machine learning and digital twins from colleagues in computer science, CSIRO and Art & Design.
The appointed ToR student will assist in development of digital twins incorporating both machine learning-based algorithms and deterministic models to optimise design and performance of selected water and wastewater treatment technologies.
- Machine learning modelling of a membrane capacitive deionization (MCDI) system for prediction of long-term system performance and optimization of process control parameters in remote brackish water desalination
- Application of digital twins for remote operation of membrane capacitive deionization (mCDI) systems