oHYSEM is a Python library designed to provide optimal planning, operation, and management strategies for hybrid renewable electricity-hydrogen systems. It enables both stand-alone and grid-connected systems to participate in energy and hydrogen markets, ensuring the integration of new assets and efficient system scheduling.
The LARGE LANGUAGE MODEL FOR HYDROGEN STORAGE project uses advanced natural language processing to improve research efficiency.
Python simulation framework for techno-economic optimisation of PEM and Alkaline electrolysers under variable renewable energy — 15-year horizon, degradation modelling, LCOH/NPV analysis
Energy, Machine Learning & CFD Analyst
Ph.D. in Theoretical and Computational Chemistry at IIT Kharagpur, PDRA-I at SNBNCBS, PDF-I at IIT Bombay, PDF-II at uOttawa, Visiting at UW
Hydraulic effects of grid-scale hydrogen storage
Loch-E is a research tool for modelling pressure–composition–isotherm (PCI) behaviour of FeTiHₓ hydrides using ab initio energetics and explicit entropy contributions.
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