Md Mahmudul Hasan

Md Mahmudul Hasan is a Ph.D. student in Civil Engineering at the University of Cincinnati. He is a lead researcher in the Building Energy Assessments, Solutions and Technologies (BEAST) Laboratory under the supervision of Dr. Nabil Nassif. His research focuses on energy assessment, PCM thermal storage design, interstage thermal management in industrial heat pumps and energy system optimization. He combines thermodynamic modeling, simulation, machine learning and AI-enabled analysis to investigate building energy demand, HVAC performance and forecasting reliability under changing operating conditions. He earned his Bachelor of Science in Mechanical Engineering from the Islamic University of Technology (IUT), Bangladesh, and previously worked at US-Bangla Airlines. His broader goal is to bridge research and industrial practice through AI-enabled innovation in HVAC and thermal energy systems, advancing energy efficiency, industrial decarbonization and a more sustainable energy future.

Bachelor of Science (B.Sc.): Islamic University of Technology (IUT) Bangladesh, 2023 (Mechanical Engineering)

Machine Learning, Energy Conversion, Optimization, Phase Change Materials, Applied Thermal Engineering, Industrial Heat Pump, HVAC.

Grant: #DE-EE0011194 Investigators:Dr. Nabil Nassif - Principal Investigator, 09-01-2024 -08-30-2027 U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Industrial Technologies Office Cost-Effective Integration of PCM Thermal Energy Storage and Wet Compression for High-Efficiency Industrial Heat Pumps. Role:Graduate Research Assistant (GRA), Active

Peer Reviewed Publications

i. Asif Iqbal Turja, Md Mahmudul Hasan, M Monjurul Ehsan, Yasin Khan  (2023). Multi-Objective Performance Optimization & Thermodynamic Analysis of Solar Powered Supercritical CO2 Power Cycles Using Machine Learning Methods & Genetic Algorithm. Energy & AI, 15More Information

ii. Asif Iqbal Turja , Khandekar Nazmus Sadat , Md. Mahmudul Hasan , Yasin Khan , Md Monjurul Ehsan  (2024). Waste Heat Recuperation in Advanced Supercritical CO2 Power Cycles with Organic Rankine Cycle Integration & Optimization Using Machine Learning Methods. International Journal of ThermofluidsMore Information

iii. Hasan MM, Hasan MJ, Rahman PB  (2024). Comparison of RNN-LSTM, TFDF and stacking model approach for weather forecasting in Bangladesh using historical data from 1963 to 2022. PloS ONEMore Information

iv. Hasan MM, Dharmasena P, Nassif N  (2026). Dynamic Multi-Output Stacked-Ensemble Model with Hyperparameter Optimization for Real-Time Forecasting of AHU Cooling-Coil Performance. EnergiesMore Information

v. Mohamed Elwardany, Asif Iqbal Turja, Md Mahmudul Hasan, Nabil Nassif  (2026). High-temperature heat pumps for industrial decarbonization: Technologies, integration strategies, and future perspectives. Chemical Engineering and Processing - Process Intensification.More Information

vi. Elwardany, Mohamed; Reddy, Y Siva Kumar, PhD; Hasan, Md Mahmudul; Nassif, Nabil  (2026). Recent Advances in Water-Based High-Temperature Heat Pumps for Industrial Decarbonization. ASHRAE TransactionsMore Information

vii. Md Mahmudul Hasan, Mohamed Elwardany, PE Nabil Nassif PhD  (2026). Comparative Modeling of Scheduled and Real-Time Building Energy Data Using Advanced Ensemble Machine Learning. ASHRAE TransactionsMore Information

viii. Md Mahmudul Hasan, Mohamed Elwardany and Nabil Nassif  (2027). A Unified Robustness Evaluation Framework for Machine Learning-Based Building Energy Forecasting Under Operational Variability and Schedule Perturbation. (Under Revision). Energy & Buildings