Principal Investigator

about-me

Dr. Pyarimohan Dehury

Assistant Professor
Energy Engineering, ICT-IOC Bhubaneswar

Post-Doctoral: IIT Madras, India (2021 - 2023)

Doctor of Philosophy: IIT Guwahati, India (2021)

Master of Chemical Engineering: IIT Guwahati, India (2015)

Bachelor of Chemical Engineering: Biju Pattnaik University of Technology, India (2012)

Email ID: p.dehury@iocb.ictmumbai.edu.in

Contact No.: +91- 8280008066


Ms. Gangotri Bhata (Co-Goude: Dr. Sushma Chakraborty)

Minor Specialization: Energy Engineering
Email ID: che22g.bhata@stuiocb.ictmumbai.edu.in
Research Area: Rheological Behaviour of Nanofluids
Brief about research: ""


Mr. Lalitesh Tushar Khairnar (Co-Goude: Dr. Sushma Chakraborty)

Minor Specialization: Energy Engineering
Email ID: che22l.khairnar@stuiocb.ictmumbai.edu.in
Research Area: Extraction of REE from spent Automobile Catalytic Converter
Brief about research: This study explores how rare earth elements, essential in current technology, may be recovered from garbage rather than utilizing environmentally harmful mining processes. It uses some standard techniques for recovering rare earth elements from garbage. We are also researching on new ways for collecting rare earth elements from waste products that have a lower environmental effect than current ones.

Miss. Asmi Ayojini Ray (Co-Goude: Dr. Sushma Chakraborty)

Minor Specialization: Pharmaceutical Technology
Email ID: che22a.ray@stuiocb.ictmumbai.edu.in
Research Area: Development of Heat Transfer Fluids For EV Batteries
Brief about research: This research focuses on the development of heat transfer fluids for EV batteries. This study mainly examines the evolution of coolants, their options, their thermophysical needs, and engineering challenges that are associated while developing HTFs specifically designed for EV battery applications.

Mr. Aniruddha Moreshwar Dudhakuwar (Co-Goude: Dr. Abhay kotkonwar)

Minor Specialization: Energy Engineering
Email ID: che22a.dudhakuwar@stuiocb.ictmumbai.edu.in
Research Area: Rheological Behaviour of Engine Nano Coolant
Brief about research:



Mr. Priyanshu Kumar (Co-Goude: Dr. Ramesh Devarapalli)

Minor Specialization: Energy Engineering
Email ID: che22p.kumar@stuiocb.ictmumbai.edu.in
Research Area: Modeling and Optimization of Fuel Cell Systems for Performance Enhancement
Brief about research: This project focuses on the computational modeling and optimization of fuel cell systems to improve their performance, efficiency, and operational reliability. Using COMSOL Multiphysics, a 3D model is developed by coupling fluid flow, species transport, electrochemical reactions, and current distribution. The study investigates the influence of key operating parameters and design variables on hydrogen consumption, reactant distribution, current density, and overall cell performance.

Mr. Thiyagarajan Thittani (Co-Goude: Dr. Ashish Adak)

Minor Specialization: Textile Engineering
Email ID: che22t.thiyagarajan@stuiocb.ictmumbai.edu.in
Research Area: Numerical Modelling and Experimental Study of Fuel Cell
Brief about research: The research includes numerical and experimental investigation of Proton Exchange Membrane (PEM) fuel cell performance using COMSOL Multiphysics. The study aims to systematically evaluate the influence of GDL/membrane properties, operating temperature, and reactant flow rate on cell behavior. Cell performance is characterized through polarization and Electrochemical Impedance Spectroscopy (EIS) curves, offering valuable insights for PEMFC design and optimization.

Mr. Rohith Kumar V (Co-Goude: Dr. Ashis Adak)

Minor Specialization: Pharmaceuticals
Email ID: che22v.rohith@stuiocb.ictmumbai.edu.in
Research Area: Predicting Solvents for Rare Earth Element Extraction By using Machine Learning
Brief about research: This research focuses on use of artificial intelligence to identify suitable solvents for extracting metals such as rare-earth elements from aqueous solutions. Experimental data such as extraction efficiency, pH, temperature, solvent properties, metal concentration, and distribution coefficient can be collected and used to train machine-learning models. Models such as Random Forest, XGBoost, and Neural Networks can predict extraction performance.
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