Mr. Abdulhalim Musa Abubakar | Process Engineering | Chemical Engineering Award

Modibbo Adama University (MAU) ,Nigeria

Abdulhalim Musa Abubakar is a Nigerian Chemical Engineer dedicated to innovation in renewable energy, chemical reaction engineering, and water treatment. Born and raised in Adamawa State, he has developed a solid foundation in both theoretical knowledge and practical application of chemical engineering principles. With academic qualifications from the University of Maiduguri and a diverse professional portfolio, he brings experience from academic, industrial, and development sectors. His work spans teaching, research, water quality analysis, and biogas technology. Abdulhalim currently serves as an Assistant Lecturer at Modibbo Adama University (MAU), where he integrates research, student mentorship, and curriculum advancement. Known for being proactive, detail-oriented, and results-driven, he is committed to using his skills for environmental sustainability and energy transformation in Nigeria and beyond. His vision is to contribute meaningfully to solving global energy and environmental challenges through cutting-edge research and innovative engineering practices.

Professional Profile

Orcid

🎓 Education

Abdulhalim Musa Abubakar holds both Bachelor’s and Master’s degrees in Chemical Engineering from the University of Maiduguri, where he graduated with distinctions (B.Eng: 4.55 CGPA, M.Eng: 4.85 CGPA). His academic journey began at University Primary School, followed by Imam Malik Secondary School, where he earned his WAEC certificate in 2013. He pursued higher education with a clear focus on energy, environmental remediation, and reaction engineering. In addition to formal academic achievements, he has undertaken numerous professional training programs and certifications, including diplomas in Oil & Gas Management and Control Engineering, and certifications in AutoCAD, data science, project management, and programming. These multi-disciplinary skills strengthen his engineering knowledge and his capacity to tackle complex industrial challenges. His educational path reflects a strong commitment to academic excellence and lifelong learning, enabling him to contribute both in research and practical problem-solving within the chemical engineering domain.

💼 Experience

Abdulhalim Musa Abubakar has gained diverse experience across academic, industrial, and community-based projects. He began his practical journey as a Plant Operator Intern at Maiduguri Water Treatment Plant in 2017. During his NYSC service year, he served at Mada Water Works, where he performed water quality analysis. He briefly taught at Bulumkutu Islamic Science School before joining Modibbo Adama University (MAU) in 2019 as a Graduate Assistant, and subsequently, as an Assistant Lecturer in 2023. He has participated in data gathering and fieldwork as an Enumerator with Borno Women Development Initiative. His career showcases a balance of academic responsibilities and field engagement. He also has notable experience with environmental modeling and simulation software, and his teaching and research focus on sustainable engineering practices. These roles reflect his multidisciplinary capabilities and his commitment to using engineering tools for real-world impact, especially in energy and environmental sectors.

🏆 Awards and Honors

Abdulhalim Musa Abubakar has been recognized for his service, academic excellence, and professional dedication. Notable among his accolades is the Certificate of Service awarded for his voluntary role as Tutorial Coordinator by the Nigerian Society of Chemical Engineers (NSChE), UNIMAID Student Chapter (2018). He also received recognition from the Muslim Students’ Society of Nigeria (MSSN), Faculty of Engineering Branch, for his voluntary academic support in 2017/2018. He has earned certificates of participation and achievement in over a dozen international workshops, seminars, and webinars, including those hosted by prestigious institutions such as the Royal Society of Chemistry, Polytechnic University of the Philippines, and Siirt University in Türkiye. His proactive participation in global conferences and research congresses underscores his commitment to continuous learning and professional engagement. These honors reflect both academic leadership and a deep-seated drive to contribute to scholarly and societal advancement in engineering and beyond.

🔍 Research Focus

Abdulhalim Musa Abubakar’s research centers around renewable energy systems, biogas production, microbial kinetics, environmental remediation, chemical reaction engineering, and waste-to-energy technologies. He has a particular interest in transforming organic waste materials, such as chicken manure and medical waste, into biogas through anaerobic digestion processes. His master’s research explored microbial growth modeling and digester performance, contributing insights into sustainable energy generation from biodegradable waste. His research also addresses pharmaceutical waste management, modeling and simulation using ASPEN Plus, and water treatment processes using eco-friendly techniques. Additionally, he has presented studies on energy access in underserved areas like refugee camps, reflecting his interest in humanitarian engineering. Abdulhalim is dedicated to applying data science, programming, and simulation tools to solve energy and environmental challenges. His goal is to develop scalable, cost-effective technologies that bridge the gap between clean energy supply and waste reduction, particularly in Africa and other developing regions.

📚 Publication Top Notes

1. Modeling Anaerobic Decomposition: JMP Application with Biomass Data

Authors: Abubakar, A. M.; Elboughdiri, N.; Chibani, A.; Nneka, E. C.; Yunus, M. U.; Ghernaout, D.
Journal: Portugaliae Electrochimica Acta (2025)
Summary: This paper models anaerobic digestion using JMP software based on experimental data from two biomass combinations in Nigeria. Neural networks and response surface methodology were applied to optimize biogas production. Monod kinetic parameters were also estimated, showing excellent prediction accuracy and insight into biomass-substrate interactions.

2. Review on Municipal Solid Waste, Challenges and Management Policy in Pakistan

Authors: Asif, M.; Laghari, M.; Abubakar, A. M.; Suri, S. K.; Wakeel, A.; Siddique, M.
Journal: Portugaliae Electrochimica Acta (2025)
Summary: A critical review highlighting Pakistan’s challenges in managing municipal solid waste, including rapid urbanization, insufficient infrastructure, and lack of effective policy enforcement. It recommends comprehensive reforms, sustainable waste processing, and public-private collaborations for improved waste governance.

3. Development of Low-Cost Adsorbents from Coconut Shell for Energy-Efficient Dye Removal from Laboratory Effluent Discharge

Authors: Abdulhalim Musa Abubakar; Naeema Nazar; Abdulghaffaar Assayyidi Yusuf; Enyomeji Ademu Idama; Moses NyoTonglo Arowo; Aisha Maina Ma’aji; Irnis Azura Zakarya
Journal: Measurement: Energy (June 2025)
Summary: This research focuses on developing coconut shell-based adsorbents for removing dyes from laboratory wastewater. The material showed over 90% dye removal efficiency under optimal conditions and was confirmed as a cost-effective and energy-efficient method for effluent treatment.

4. Characterizing the Reducing Properties of Biofuels in Activating Metal Catalyst of Refinery Process

Authors: Mohammed Abdulrahim; Usman Habu Taura; Abdulhalim Musa Abubakar; Marwea Al-Hedrewy
Journal: Sustainable Processes Connect (May 2025)
Summary: Examines the effectiveness of biofuels in enhancing metal catalyst performance in refinery processes. The study found that biofuels provided a reducing atmosphere that facilitated catalyst activation but also noted challenges such as catalyst deactivation and thermal instability.

5. Impact of Furfural Raffinate Oil as a Filling Agent on the Vulcanization and Mechanical Properties of Rubber

Authors: Suleiman A. Wali; Abubakar Mohammed; Abdulhalim Musa Abubakar; Abdulmuhsin Usman; Kamran Khan
Journal: Current Engineering Letters and Reviews (January 2025)
Summary: Investigates the use of furfural raffinate oil as a rubber additive. Findings show improvements in rubber strength and flexibility up to a certain concentration, indicating potential for sustainable and cost-effective rubber production using industrial by-products.

Conclusion

Abdulhalim Musa Abubakar stands out as a dynamic and forward-thinking Chemical Engineer whose academic achievements, hands-on industrial experiences, and proactive engagement in research and professional development reflect a deep commitment to sustainable innovation. His work spans critical sectors including renewable energy, biogas production, water treatment, and environmental remediation—key areas that align with global sustainability goals. Through a strong foundation in chemical engineering, supported by advanced software and data science skills, he has consistently demonstrated his ability to bridge theoretical knowledge with practical applications. Abdulhalim’s numerous certifications, conference contributions, and teaching roles further underscore his dedication to lifelong learning and capacity building. As he continues to evolve as a researcher and educator, his efforts are poised to contribute significantly to solving pressing energy and environmental challenges both within Nigeria and internationally. His trajectory reflects not only technical competence but also a clear vision for engineering as a tool for societal transformation.

Mr Qi Liang | Pattern Recognition | Excellence in Research

Master in Tongji University at China

Qi Liang is a dedicated researcher and master’s student at Tongji University, PR China, specializing in mechanical engineering. With a strong foundation in industrial engineering from Jiangsu University of Science and Technology, Qi has a keen interest in advancing technology through innovative research. Recognized for introducing self-supervised learning methods in semiconductor applications, Qi’s work aims to solve complex challenges in pattern recognition. Their publication in Engineering Applications of Artificial Intelligence reflects a commitment to high-impact research. With multiple ongoing projects and a focus on practical applications, Qi is paving the way for efficient solutions in the semiconductor industry.

Strengths for the Award

Innovative Research: Qi Liang has introduced a self-supervised learning method for few-shot learning in semiconductor applications, demonstrating originality and a significant contribution to the field.

Publication Record: The recent publication in Engineering Applications of Artificial Intelligence showcases a commitment to high-quality research, adding to the credibility of the work.

Diverse Research Interests: With a focus on computer vision, multi-modal learning, and fault diagnosis, Qi’s work spans multiple cutting-edge areas, which increases the potential impact of the research.

Practical Applications: The research addresses real-world challenges in the semiconductor industry, offering low-cost, efficient methods that have immediate applicability.

Academic Engagement: Qi’s active involvement in ongoing projects and industry collaborations indicates a robust engagement with both academic and practical aspects of research.

Areas for Improvement

Broader Collaboration: Expanding collaborations with international researchers could enhance the research’s visibility and applicability on a global scale.

Increased Publication Volume: While the current publication is commendable, a more extensive publication record could further establish Qi’s expertise and leadership in the field.

Outreach and Communication: Engaging in more outreach activities, such as conferences and seminars, could help disseminate findings and foster connections within the research community.

Education

Qi Liang graduated with a Bachelor’s degree in Industrial Engineering from Jiangsu University of Science and Technology, where foundational principles of engineering and technology were mastered. Currently, Qi is pursuing a Master’s degree in Mechanical Engineering at Tongji University, one of China’s prestigious institutions, now in their third year of the program. This advanced education has allowed Qi to engage deeply with cutting-edge topics, particularly in computer vision and machine learning. Through rigorous coursework and research, Qi has developed expertise in areas such as pattern recognition, self-supervised learning, and fault diagnosis, equipping them with the skills necessary to tackle complex engineering problems and contribute significantly to both academic and industrial advancements.

Experience

Qi Liang has gained substantial experience through multiple research projects, totaling five completed or ongoing initiatives that emphasize practical applications of machine learning in semiconductor manufacturing. In addition to academic research, Qi has participated in three consultancy and industry-sponsored projects, bridging the gap between theoretical knowledge and real-world applications. Their collaborative efforts in research have led to valuable partnerships and a broader understanding of the industry’s challenges and needs. As the first to implement self-supervised learning techniques in few-shot learning tasks related to wafer map pattern recognition, Qi has showcased exceptional innovation. This unique approach has opened new avenues for cost-effective and efficient solutions within the semiconductor sector, positioning Qi as an emerging leader in their field.

Research Focus

Qi Liang’s research focuses on the intersection of computer vision and machine learning, with a strong emphasis on pattern recognition, keypoint detection, and image retrieval. Specializing in self-supervised and multi-modal learning, Qi aims to develop innovative methodologies that minimize the reliance on labeled data while maximizing efficiency and applicability in industrial contexts. Current research projects explore dynamic adaptation mechanisms for few-shot learning, specifically tailored for wafer map pattern recognition in the semiconductor industry. Qi is also interested in signal processing and fault diagnosis, seeking to improve reliability and performance in manufacturing processes. This research direction not only contributes to the academic community but also addresses pressing industry challenges, promoting advancements in automation and smart manufacturing.

Conclusion

Qi Liang’s innovative contributions to the field of mechanical engineering and computer vision make a strong case for the Excellence in Research award. The unique approach to self-supervised learning in few-shot learning for wafer map pattern recognition signifies both a breakthrough in methodology and practical application in the semiconductor industry. With a few strategic improvements, Qi has the potential to further amplify the impact of their research and cement their status as a leading researcher in their field.

Abdulhalim Musa Abubakar | Process Engineering | Chemical Engineering Award