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Actively involved in Teaching Research and Academic Publishing
Specialized in Renewable Energy Systems and Biofuel Engineering
Experienced in Waste-to-Energy Technologies
Experienced in Scientific Data Analysis and Modelling
Proficient in Engineering Design and Analysis
I am Dr.Omojola Awogbemi,a globally recognised Mechanical Engineering academic and energy systems researcher focused on advancing sustainable and low-carbon technologies.My work centres on renewable energy,biofuel innovation,and waste-to-energy systems,addressing critical challenges in energy security,environmental sustainability,and industrial decarbonisation.
Based at Ekiti State University,Nigeria, I contribute to teaching, research,and mentorship while driving impactful innovation.Recognised among the Top 2% of Global Scientists (2025) by Stanford University and Elsevier,my research integrates engineering innovation,circular economy principles,and intelligent systems to develop scalable,efficient, and environmentally responsible energy solutions.I actively engage in international collaborations,industry partnerships, and funded initiatives,with a strong focus on delivering future-ready energy systems.
My expertise spans renewable energy, biofuel engineering, and waste-to-energy systems, with strengths in thermochemical processes, catalyst development, and machine learning applications in energy systems. I focus on efficient, data-driven engineering solutions supported by advanced modelling, design, and research.
“Recent Advancements in Biochar Functionalization from Crop Residues for a Green Future”, Journal of Renewable Materials, 13(13), 2191.
View PublicationAchieving Energy Sustainability in Nigeria’s Telecommunications Industry through Renewable Propane”, NIPES NIPES-Journal of Science and Technology Research, Special Issue, 7(2), 3320-3325.
View PublicationHydrogen Infrastructure for a Sustainable Future: Challenges, Innovations, and Global Opportunities”, NIPES NIPES-Journal of Science and Technology Research, Special Issue, 7(2), 3302-3308.
View PublicationPredictive Maintenance of Industrial Milling Machines Using Machine Learning: Enhancing Reliability and Efficiency”, NIPES NIPES-Journal of Science and Technology Research, Special Issue, 7(1), 1630-1637
View PublicationThere are many variations of passages of Lorem Ipsum available, but the majority have suffered alter...
There are many variations of passages of Lorem Ipsum available, but the majority have suffered alter...
There are many variations of passages of Lorem Ipsum available, but the majority have suffered alter...