PI Profiles

PI- Dr. Syed Maaz Hasan Assistant Professor at NUST School of Mechanical and Manufacturing Engineering (SMME). PI of the Artificial Intelligence for Mechanical Systems (AIMS) Lab in SINES. Dr. Hasan specializes in manufacturing systems, particularly focusing on scalable systems and process planning. He holds a PhD in Mechanical Engineering from NUST and has a strong background in system modeling, optimization, and the integration of AI in mechanical systems.

PI- Dr. Yasar Ayaz Professor at NUST School of Mechanical and Manufacturing Engineering (SMME). PI of the National Center of Artificial Intelligence (NCAI) at NUST. Dr. Ayaz’s research interests include renewable energy, energy systems, and artificial intelligence in energy applications. He has contributed significantly to sustainable energy technologies and their application in mechanical systems.

Lab Equipment/Resources

1.       Solar Simulator
2.       HVAC Test Bench
3.       Heat Exchanger Test Bench
4.       Envy Tech Devices
5.       Air Quality Monitoring Devices
6.       Power Quality Monitoring Devices

Lab Capabilities

• Advanced thermal performance evaluation of heat exchangers and HVAC systems.
• AI-based optimization and control for mechanical systems.
• Development of energy-efficient and sustainable systems.
• Experimental testing and simulation of renewable energy technologies (solar, wind).
• Modeling and simulation of fluid dynamics in complex systems.

Projects Undertaken

1.     HEC NRPU 10462
2.    AIMS funded by NCAI
3.    IOSE Funded by NCBC

MS Students

Phd Students

Research Publications

[1–8]

[1]         A. Javaid, M. Sajid, E. Uddin, A. Waqas, Y. Ayaz, Sustainable urban energy solutions: Forecasting energy production for hybrid solar-wind systems, Energy Convers Manag 302 (2024) 118120. https://doi.org/10.1016/j.enconman.2024.118120.

[2]         S. Iqbal, S.N. Khan, M. Sajid, J. Khan, Y. Ayaz, A. Waqas, Impact and performance efficiency analysis of grid-tied solar photovoltaic system based on installation site environmental factors, Energy & Environment 34 (2023) 2343–2363. https://doi.org/10.1177/0958305X221106618.

[3]         H. Idrees, S. Ali, M. Sajid, M. Rashid, F.I. Khawaja, Z. Ali, M.N. Anwar, Techno-Economic Analysis of Vacuum Membrane Distillation for Seawater Desalination, Membranes (Basel) 13 (2023) 339. https://doi.org/10.3390/membranes13030339.

[4]         M.F. Faiz, M. Sajid, S. Ali, K. Javed, Y. Ayaz, Energy modeling and predictive control of environmental quality for building energy management using machine learning, Energy for Sustainable Development 74 (2023) 381–395. https://doi.org/10.1016/j.esd.2023.04.017.

[5]         B. Rasheed, A. Safdar, M. Sajid, S. Ali, Y. Ayaz, Assessment of solar load models for bifacial PV panels, Front Energy Res 10 (2022). https://doi.org/10.3389/fenrg.2022.1019595.

[6]         S.A. Haider, M. Sajid, H. Sajid, E. Uddin, Y. Ayaz, Deep learning and statistical methods for short- and long-term solar irradiance forecasting for Islamabad, Renew Energy 198 (2022) 51–60. https://doi.org/10.1016/j.renene.2022.07.136.

[7]         A. Javaid, U. Javaid, M. Sajid, M. Rashid, E. Uddin, Y. Ayaz, A. Waqas, Forecasting Hydrogen Production from Wind Energy in a Suburban Environment Using Machine Learning, Energies (Basel) 15 (2022) 8901. https://doi.org/10.3390/en15238901.

[8]         S.A. Haider, M. Sajid, S. Iqbal, Forecasting hydrogen production potential in islamabad from solar energy using water electrolysis, Int J Hydrogen Energy 46 (2021) 1671–1681. https://doi.org/10.1016/j.ijhydene.2020.10.059.