Hooman Chamani graduated with a BASc in ChemicalEngineering (2013) and MASc in Chemical Engineering-Environmental Engineering(2015) from Ferdowsi University of Mashhad and Sharif University of Technology, respectively, and received his Ph.D. in Chemical Engineering from University ofOttawa (2021). During his PhD program, he tackled the main challenge of membrane distillation process, i.e. pore wetting. As part of his research on membrane separation processes, he joined the summer program at the University of Edinburgh, UK, in 2019.
Hooman was ranked first in his class during both his BASc and MASc programs (based on CGPA) and has won several awards and scholarships during the past four years, including Mitacs Globalink ResearchAward for visiting MIT, Ontario Trillium Scholarship, International ExperienceScholarship, Richard Stessel Memorial Scholarship, Paul G Complin Scholarship, etc.
Hooman is a Postdoctoral Researcher in the Department of Chemical Engineering and Applied Chemistry, University of Toronto, working with Prof. Jay Werber and Prof. Jason Hattrick-Simpers. His focus is on the application of data science in accelerating the characterization and discovery of membranes.
Water scarcity is a widespread and increasingly pressing global challenge. Even Canada, blessed with abundant freshwater resources, is not immune to this threat and all three prairie provinces, stretching from the Rocky Mountains to the Hudson Bay shore, are at risk from drought, while management of water in natural resource industries can be highly complex and costly. Membrane-based desalination processes, especially reverse osmosis (RO), are the most energy-efficient and (often) cost-efficient processes for treatment of saline waters. The membrane material is the enabling component of all membrane processes. However, current membrane fabrication methods rely on art rather than science, with limited control and knowledge of the membrane structure, which has constrained progress in membrane science.
In this research project, artificial intelligence (AI) will be first employed to develop an open-source application with the aim of generating statistically equivalent latent space (LS) representations of 3D structure of membranes using 2D scanning electron microscopy (SEM) images. The application would allow for rapid, accessible characterization of membrane structure, speeding up materials optimization, and could find application in essentially all membrane applications. Second, we will use this tool and regression models to guide the experimental membrane fabrication conditions to prepare high-permeability, highly compression-resistant porous membranes for high-pressure RO (HPRO) processes. HPRO would expand the operating range of RO to higher pressures (>150 bar) and salinities, which would enable massive energy savings in industrial water management. Introducing image-based AI into membrane science has the potential of revolutionizing the design and characterization of membrane materials.