Juan Santamaria Sancho | Photovoltaic Systems | Research Excellence Award

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Marc Ayoub | Renewable Energy | Research Excellence Award

Mr. Marc Ayoub | Renewable Energy | Research Excellence Award

PhD Researcher in University of Galway, Ireland

Mr. Marc Ayoub is a PhD researcher at the University of Galway, working on tidal energy systems with a focus on environmental impact, regulatory challenges, and community engagement frameworks. He also serves as an Associate Fellow at the Issam Fares Institute for Public Policy and International Affairs at American University of Beirut, contributing to energy policy research in the Middle East, and was a Nonresident Fellow at The Tahrir Institute for Middle East Policy focusing on climate and energy issues. He completed a Master’s by Research at the University of Limerick, where he worked on energy policy related to grid-scale battery deployment, supported by the Sustainable Energy Authority of Ireland. Prior to this, he worked extensively at the Issam Fares Institute as an Energy Researcher and Program Coordinator, producing policy reports, conducting techno-economic assessments, and contributing to regional energy dialogues. He also has academic experience as a Chemical Engineering Instructor at Holy Spirit University of Kaslik and professional industry experience as a Project Engineer at Petroserv SAL, where he managed large-scale geophysical survey projects in collaboration with NEOS GeoSolutions for Lebanon’s energy sector. His educational background includes advanced studies in chemical engineering and chemistry from University of Balamand and Lebanese University, reflecting a strong interdisciplinary foundation in energy, engineering, and policy.

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Featured Publications


From dysfunctional to functional corruption: The politics of reform in Lebanon’s electricity sector

– ACE Research Consortium Working Paper No. 30, 2020 | Cited by: 14


Unbundling Lebanon’s Electricity Sector

– Issam Fares Institute for Public Policy and International Affairs, 2021 | Cited by: 6

 

Lijia Fang | Ammonia Combustion | Best Researcher Award

Mr. Lijia Fang | Ammonia Combustion | Best Researcher Award

Mr. Lijia Fang | Sophia University | Japan

Mr. Lijia Fang is a Ph.D. student in the Department of Science and Engineering at Sophia University, Japan, specializing in AI-driven combustion control and sustainable fuel systems. His research integrates artificial intelligence with combustion engineering to optimize fuel efficiency and minimize emissions. Currently, his work focuses on exploring the synergies between ammonia and ethanol fuels to enhance clean combustion performance and significantly reduce nitrogen oxide emissions. He has completed a major research project titled “Influence of Pre-Chamber Nozzle and Main Chamber Geometry on Ammonia Combustion: A Combined Experimental and Predictive Study” and has published two research papers, including one under review in Applied Thermal Engineering and another published in Energies (2024). His studies employ machine learning algorithms to predict in-cylinder combustion pressure and validate ammonia–oxygen combustion models in constant-volume chambers. Mr. Fang has contributed to seven patents, demonstrating his strong involvement in practical innovation, particularly in electronic systems and control circuits. His research aims to accelerate the transition toward low-carbon, high-efficiency combustion systems by integrating AI-based optimization methods with experimental validation. With 2 publications indexed and cited by 6 documents, he currently holds an h-index of 2, reflecting his emerging impact in the field of sustainable combustion and energy technologies. Through his interdisciplinary expertise in artificial intelligence, mechanical design, and environmental sustainability, Mr. Fang continues to advance cutting-edge research that supports the global pursuit of cleaner and more efficient energy solutions.

Profiles: Scopus | Orcid

Featured Publications

Fang, L., Singh, H., Ohashi, T., Sanno, M., Lin, G., Yilmaz, E., Ichiyanagi, M., & Suzuki, T. (2024). Effect of machine learning algorithms on prediction of in-cylinder combustion pressure of ammonia–oxygen in a constant-volume combustion chamber. Energies, 17(3), 746. https://doi.org/10.3390/en17030746