Abdul Gani Abdul Jameel, Ph.D.


Postdoctoral Fellow

Research Interests

My research expertise lies in the areas of oil and fuel characterization using a number of analytical techniques like TGA/DSC, Elemental Analysis (CHNSO), Calorimetry, TGA-FTIR, GC, ICP-OES, FT-ICR MS, 1H and 13C NMR spectroscopy. My research methodology focuses mainly towards understanding fuel molecular chemistry and its effect on global combustion and emission characteristics to design better fuels that increase efficiency and reduce emissions.  I am also interested in using machine leaning based techniques to predict fuel properties like octane number, cetane number, sooting propensity etc.

Presently I am working on heavy fuel oil (HFO) which is used as a fuel for electricity generation. I am looking into multiple aspects of HFO research including desulfurization using ODS, asphaltene characterization, swirling flame combustion and PM characterization. I am also interested in computational fluid dynamics of solid fuel combustion.

Selected Publications


  • PhD, Chemical Engineering, KAUST, Saudi Arabia (2014-2019)
  • M.Tech, Chemical Engineering, Anna University, India (2010 -2012)
  • B.Tech, Chemical Engineering, Anna University, India (2005 -2009)

Professional Profile

  • 2019- Present, Postdoctoral Researcher, KAUST, Saudi Arabia
  • 2012-2014: Research Project Officer, IIT Madras, India 
  • 2009-2010: Technical Support Officer, HCL Technologies, India

Scientific and Professional Membership

  • Member of Combustion Institute
  • Member of Saudi Arabian section of Combustion Institute


  • Best Poster award “ KAUST Research Conference – The future of Fuel”, KAUST 2019
  • Winner "Shell Carbon Challenge, IIT Madras, 2010.
  • Best Paper Award, Shaastra'11, IIT Madras.
  • Winner " Dr. A L Mudaliar Oratorical Contest" 2010, conducted by CLRI, Chennai.

KAUST Affiliations

  • Clean Combustion Research Center (CCRC)
  • Physical Sciences and Engineering (PSE)

Research Interests Keywords

combustion chemistry Combustion modeling computational fluid dynamics