Profile Summary
Engr. Dr. Muhammad Subhan Azeem is a Chemical Engineer and academician currently serving as a HOD in the Department of Food Science and Technology at NFC Institute of Engineering & Technology (NFC IET), Multan. He has been associated with NFC IET since 2014 and is actively involved in teaching, research, and academic administration.
Dr. Azeem holds a Ph.D. in Chemical Engineering, along with an M.Sc. and a B.Sc. in Chemical Engineering from reputable Pakistani institutions. His academic and research interests lie primarily in catalysis, dry reforming of methane, hydrogen production, process simulation, and data-driven optimization techniques. His recent work integrates machine learning approaches with chemical process optimization, particularly in catalytic performance enhancement.
He has published extensively in national and international peer-reviewed journals, including Process Safety and Environmental Protection, Adsorption Science & Technology, Frontiers in Chemical Sciences, and other recognized journals. His research focuses on the development of sustainable nano catalysts, catalyst stability, coke resistance, and simulation of complex separation processes using tools such as ASPEN HYSYS.
Dr. Azeem teaches core undergraduate courses, including Petroleum Refinery Engineering, Environmental Health and Safety, Unit Operations, Applied Thermodynamics, and Separation Processes, and supervises laboratory work in Fuel & Energy and Thermodynamics. In addition to teaching and research, he has held several administrative roles, including Laboratory In-Charge, Deputy Hostel Warden, and Member of the Board of Studies for both Chemical Engineering and Food Science & Technology departments.
Through his academic contributions, research publications, and institutional service, Dr. Subhan Azeem continues to contribute actively to engineering education and applied chemical research in Pakistan.
Research Interests
- Dry Reforming of Methane (DRM)
- Nano catalysts and Catalyst Stability
- Hydrogen Production Technologies
- Process Simulation and Optimization
- Machine Learning Applications in Chemical Engineering
- Separation Processes and Distillation