• Enhanced Absorption Refrigeration Cycle (ARC) performance through integration with a VCR system via a cascade heat exchanger, enabling lower evaporator temperatures and improved efficiency.

• Applied a recompression technique to eliminate the bulky condenser by reutilizing condensation heat in the generator, reducing thermal load.

• Incorporated injection and ejector technologies to recover isenthalpic expansion losses and achieve lower evaporator pressures for improved cycle performance.

• Utilized Artificial Neural Network (ANN)-based predictive models for comprehensive optimization and evaluation.

Advisor: Prof. Dr. Mohammad Monjurul Ehsan (PhD, University of Queensland, Australia)

Model development: 

• Python and EES were used for the mathematical modeling of the systems.

• MATLAB was utilized to develop the ANN-based machine learning model and perform GA-based multi-objective optimization.

• Adobe Illustrator was employed to design and illustrate the schematic diagrams.

• Origin was used to plot and format the graphs and data visualizations.

Publication: 3 Q1 journals papers in reputed Elsevier Energy Conversion management and Thermal Science and energy progress.

[1] Yasin Khan, Md Walid Faruque, Mahdi Hafiz Nabil, M Monjurul Ehsan. “Ejector and Vapor Injection Enhanced Novel Compression-Absorption Cascade Refrigeration Systems: A Thermodynamic Parametric and Refrigerant Analysis”, Energy Conversion and Management 289 (August 2023): 117190. Impact factor-9.9, Cite Score-19, SJR rank-Q-1 https://doi.org/10.1016/j.enconman.2023.117190

[2] Yasin Khan, S M Naqib-Ul-Islam, Md Walid Faruque, M Monjurul Ehsan, “Advanced Cascaded Recompression Absorption System Equipped with Ejector and Vapor-Injection Enhanced Vapor Compression Refrigeration System: ANN Based Multi-Objective Optimization” Thermal Science and Engineering Progress (March 2024): 102485. Impact factor-5.1, Cite Score-7.2, SJR rank-Q-1,  https://doi.org/10.1016/j.tsep.2024.102485

[3] Yasin Khan, S. M. Naqib-Ul-Islam, M Monjurul Ehsan. "Performance Evaluation of Novel Advanced Recompression Absorption Refrigeration Systems Equipped with Ejector and Vapor Injection Technology." Thermal Science and Engineering Progress (August 2024): Impact factor-5.1, Cite Score-7.2, SJR rank-Q-1,https://doi.org/10.1016/j.tsep.2024.102773

• The study investigates the thermal and hydraulic performance of nano-liquid metal fluids—nanofluids using five liquid metals (Ga, GaIn, EGaIn, GaSn, EGaInSn) with four types of nanoparticles (CNT, Al₂O₃, Cu, diamond)—in microchannel heat sinks, comparing them to conventional water-based nanofluids using validated single-phase and two-phase simulations.

• Results show that GaIn–CNT nanofluid delivers the highest heat transfer coefficient, outperforming other metal–nanoparticle combinations, with up to 341% improvement over water-based nanofluids. The performance evaluation criterion (PEC) highlights Ga-based nanofluids as hydraulically superior, and a particle concentration analysis supports enhanced cooling effectiveness.

• This research highlights nano-liquid metal fluids as advanced coolants for compact, high-performance electronics cooling, offering a promising pathway for next-generation mini/microchannel heat sink designs.

Advisor: Professor Md. Hamidur Rahman (M.Sc., Concordia University, Canada)

Model Development:

• SolidWorks is used for designing

• ANSYS Fluent is used for meshing, simulation, and post-processing

• Adobe Illustrator was employed to design and illustrate the schematic diagrams

• Origin was used to plot and format the graphs and data visualizations

Publication: Published in reputed ASME, Journal of Thermal Science and Energy Progress

[4] Yasin Khan, Md Tanbir Sarowar, Mahir Mobarrat, Md. Hamidur Rahman, “Performance Comparison of a Microchannel Heat Sink using different Nano-Liquid-Metal-Fluid Coolant: A numerical study”, ASME, J. Thermal Sci. Eng. Appl. September 2022; 14(9): 091004. Impact Factor: 2.1, Cite Score: 3, SJR Rank: Q2 https://doi.org/10.1115/1.4054007

• The project involves the thermodynamic modeling of a wide range of advanced power cycles—including Steam Cycle, Kalina Cycle, Organic Rankine Cycle, Supercritical CO₂ Cycle, and Gas Turbine Power Plants—using the principles of energy and exergy analysis based on the first and second laws of thermodynamics.

• Models are developed using computational tools such as Python, EES (Engineering Equation Solver), MATLAB, and EBSILON to assess and compare the performance of each cycle under varying operating conditions. Exergoeconomic evaluations are integrated to account for energy efficiency and cost effectiveness.

• Advanced multi-objective optimization techniques are implemented, often integrated with machine learning (ML) algorithms, to identify optimal operating parameters that balance performance, cost, and sustainability.

• ML techniques such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Genetic Algorithms (GA), and Deep Learning are applied for prediction, optimization, and decision-making. These data-driven approaches significantly enhance modeling speed, accuracy, and flexibility, especially for systems with complex thermodynamic interactions.

Advisor: Professor Md. Hamidur Rahman (M.Sc., Concordia University, Canada)

My Role: Co-supervising, thermodynamic modeling, and machine learning implementation 

Model Development:

• Python and EES were used for thermodynamic modeling and analysis

• MATLAB was used for ANN-based ML modeling and GA-based multi-objective optimization

• EBSILON was employed to develop realistic power cycle simulation models

• Adobe Illustrator was used to design and illustrate the schematic diagrams

• Origin was used to plot and format the graphs and data visualizations

Publication: Published in reputed Elsevier Journals

[8] Sifat Abdul Bari, Mohtasim Fuad, Kazi Fahad Labib, M Monjurul Ehsan, Yasin Khan, Muhammad Mahmood Hasan, “Enhancement of thermal power plant performance through solar-assisted feed water heaters: An innovative repowering approach” Energy Conversion and Management: X (100550) Impact factor-7.1, Cite Score-8.8, SJR rank-Q-1
https://www.sciencedirect.com/science/article/pii/S259017452400028X

[4] Asif Iqbal Turja, Md Mahmudul Hasan, M Monjurul Ehsan, Yasin Khan. “Multi-Objective Performance Optimization & Thermodynamic Analysis of Solar Powered Supercritical CO2 Power Cycles Using Machine Learning Methods & Genetic Algorithm” Energy and AI 15 (2024) 100327 Impact factor-9.6, Cite Score-16.5, SJR rank-Q-1
https://www.sciencedirect.com/science/article/pii/S266654682300099X

• This project focuses on integrating various power cycles, refrigeration systems and desalination systems to develop advanced cogeneration and multigeneration systems capable of simultaneously producing electricity, heating, cooling, and optionally, desalinated water.

• The modeling goes beyond conventional thermodynamic evaluation, incorporating exergoeconomic analysis to assess both energy efficiency and economic feasibility under different configurations.

• Machine learning-integrated multi-objective optimization techniques are employed to determine the ideal balance between performance, cost, and environmental sustainability, enabling the development of high-efficiency, low-impact systems.

• Waste Heat Recovery (WHR) is another critical component of this research. Industrial and power plant waste heat is strategically reclaimed and reused to improve energy utilization, reduce fuel consumption, and minimize emissions. These systems play a pivotal role in modern energy management for sustainable industries.

Advisor: Prof. Dr. Mohammad Monjurul Ehsan (PhD, University of Queensland, Australia)

My Role: Co-supervising, system modeling, optimization, and machine learning implementation 

Model development: 

• Python and EES were used for the mathematical modeling of the systems.

• Genetic Algorithms (GA) and ANN-based ML tools were employed for performance and economic optimization

• Adobe Illustrator was employed to design and illustrate the schematic diagrams.

• Origin was used to plot and format the graphs and data visualizations.

Publication: 3 Q1 journals papers in reputed Elsevier Energy Conversion management and Thermal Science and energy progress.

[6] Fairooz Nanzeeba, Tajwar A Baigh, Afrida Kabir, Yasin Khan, M. Monjurul Ehsan. "Genetic Algorithm-based Optimization of Combined Supercritical CO₂ Power and Flash-Tank Enhanced Transcritical CO₂ Refrigeration Cycle for Shipboard Waste Heat Recuperation" Energy Reports. Impact Factor: 4.7, Cite Score: 8.2, SJR Rank: Q2 https://www.sciencedirect.com/science/article/pii/S2352484724004840

[7] Tajwar A Baigh, Mostofa J Saif, Ashraf Mustakim, Fairooz Nanzeeba, Yasin Khan, M. Monjurul Ehsan. "Thermodynamic Evaluation and Optimization of a Novel Combined Cooling and Power (CCP) System with Integrated Cold Energy Utilization" Heliyon, CellPress, Elsevier. Impact Factor: 3.4, SJR Rank: Q1 https://www.sciencedirect.com/science/article/pii/S2405844024117793

[10] Azmain Rashid Raiyan, Samiuzzaman, Yasin Khan, M Monjurul Ehsan, Md Rezwanul Karim, Sefat Mahmud Siddique, “Exergoeconomic Analysis and Multi Objective Optimization of a Nuclear Driven Integrated Cooling and Power Cycle using Response Surface Regression Modeling Coupled with Genetic Algorithm”, Energy Conversion and Management 337 (2025) 119836 Impact factor-11.53, Cite Score-19.1, SJR rank-Q-1 https://www.sciencedirect.com/science/article/pii/S0196890425003590

• This research focuses on the design and performance evaluation of advanced cascade vapor compression refrigeration systems (CRS), aiming to achieve ultra-low cooling temperatures suitable for cryogenic and biomedical applications.

• Various novel configurations have been proposed, including Triple Cascade Refrigeration Systems (TCRS) integrated with flash tanks, suction line heat exchangers, and ejector-injection subsystems to enhance thermodynamic efficiency and cooling capacity.

• Detailed energy and exergy analyses are conducted to identify losses, inefficiencies, and improvement scopes across the system stages. Advanced exergy analysis, including endogenous, exogenous, avoidable, and unavoidable components, provides deeper insight into performance degradation and component interaction.

• Thermoeconomic analysis is carried out to assess the system’s cost-effectiveness under different operating scenarios, incorporating both investment and operational expenditures.

• A robust multi-objective optimization framework is implemented, combining Artificial Neural Networks (ANN) and Genetic Algorithms (GA) to identify optimal design and operating conditions for maximizing COP, minimizing exergy destruction, and reducing overall cost.

Advisor: Prof. Dr. Mohammad Monjurul Ehsan (PhD, University of Queensland, Australia)

My Role: Co-supervising, system modeling, optimization, and machine learning implementation 

Model development: 

• Python and EES were used for the mathematical modeling and analysis of the systems.

• Genetic Algorithms (GA) and ANN-based ML tools were employed for performance and economic optimization

• Adobe Illustrator was employed to design and illustrate the schematic diagrams.

• Origin was used to plot and format the graphs and data visualizations.

Publication: 3 Q1 journals papers in reputed Elsevier Energy Conversion management and Thermal Science and energy progress.

[1] Faruque, Md Walid, Yasin Khan, Mahdi Hafiz Nabil, M. Monjurul Ehsan, and Azharul Karim. "Thermal Performance Evaluation of a Novel Ejector-Injection Cascade Refrigeration System." Thermal Science and Engineering Progress (2023): 101745. Impact Factor: 5.1, Cite Score: 7.2, SJR Rank: Q1
https://www.sciencedirect.com/science/article/abs/pii/S2451904923000987

[3] Mahdi Hafiz Nabil, Yasin Khan, Md Walid Faruque, M Monjurul Ehsan. “Thermo-Economic Assessment of Advanced Triple Cascade Refrigeration System Incorporating a Flash Tank and Suction Line Heat Exchanger”, Energy Conversion and Management 295 (2023): 117630.
Impact Factor: 9.9, Cite Score: 19, SJR Rank: Q1
https://www.sciencedirect.com/science/article/abs/pii/S0196890423009767

[9] Imrul Kayes, Mahdi Hafiz Nabil, M. Monjurul Ehsan, M. Ahsan Habib, Yasin Khan. “Advanced exergy analysis and machine learning based multi-objective optimization of a modified triple cascade refrigeration system for enhanced performance”, Applied Thermal Engineering (2025) 126661.
Impact Factor: 6.1, Cite Score: 11.3, SJR Rank: Q1
https://www.sciencedirect.com/science/article/pii/S1359431125012530

• This research explores advanced configurations of absorption refrigeration systems (ARS), with a primary focus on improving performance under low generator temperature and partial load conditions, making them more compatible with solar thermal and low-grade waste heat sources.

• Novel system architectures have been developed by cascading absorption systems with vapor compression cycles, and incorporating ejector technology, flash tanks, and reheaters. These modifications enhance refrigerant flow characteristics, reduce exergy loss, and extend the operational range of traditional ARS.

• Detailed energy and exergy analyses have been conducted to evaluate thermodynamic performance under varying operating conditions. Emphasis has been placed on maximizing Coefficient of Performance (COP) and minimizing generator energy demand, especially in configurations suited for remote and off-grid cooling applications.

• Multi-objective optimization and parametric analysis are employed to fine-tune the influence of key design and operational variables such as absorber temperature, generator temperature, reheater effectiveness, and flash tank pressure, targeting an ideal trade-off between efficiency, complexity, and cost.

Advisor: Prof. Dr. Mohammad Monjurul Ehsan (PhD, University of Queensland, Australia)

My Role: Co-supervising, system modeling, optimization, and machine learning implementation 

Model development: 

• Python and EES were used for the mathematical modeling and analysis of the systems.

• Genetic Algorithms (GA) and ANN-based ML tools were employed for performance and economic optimization

• Adobe Illustrator was employed to design and illustrate the schematic diagrams.

• Origin was used to plot and format the graphs and data visualizations.

Publication: 3 Q1 journals papers in reputed Elsevier Energy Conversion management and Thermal Science and energy progress.

[8] Sefat Mahmud Siddique, M. Muhtasim Uzzaman, M. Monjurul Ehsan, Yasin Khan. “Thermal analysis of a novel configuration of double effect absorption system cascaded with ejector and injection enhanced compression refrigeration cycle”, Energy Conversion and Management, Volume 326 (2025).
Impact Factor: 9.9, Cite Score: 19, SJR Rank: Q1
https://www.sciencedirect.com/science/article/pii/S0196890424013761

[2] Md Walid Faruque, Yasin Khan, Mahdi Hafiz Nabil, M Monjurul Ehsan. “Parametric Analysis and Optimization of a Novel Cascade Compression-Absorption Refrigeration System Integrated with a Flash Tank and a Reheater”, Results in Engineering (2023).
Impact Factor: 6.0, Cite Score: 5.8, SJR Rank: Q1
https://www.sciencedirect.com/science/article/pii/S2590123023001354