Dr. Gu Shan | Power Systems | Women Researcher Award

Dr. Gu Shan | Power Systems | Women Researcher Award

Associate Professor | Zhejiang University of Water Resources and Electric Power | China

Dr. Shan Gu is a researcher in the fields of energy engineering, sustainable power systems, and environmental technology, with a strong focus on biomass utilization, air-pollutant mitigation, and life cycle assessment. Her work integrates engineering experimentation, process optimization, and environmental impact evaluation to advance the development of clean energy technologies. She has contributed significantly to the study of biomass pyrolysis, nanosilica extraction from agricultural waste, and the operational behavior of circulating fluidized bed gasifiers. Her research on biomass CFB gasification systems, including the coupling of gasifiers with industrial steam boilers, has generated important insights into practical challenges such as slagging, ash deposition, and system optimization. These contributions have provided evidence-based guidance for the scaling, operation, and environmental performance improvement of biomass-based energy systems. Dr. Gu has authored more than 30 research publications, including multiple SCI-indexed articles, with several featured in high-impact journals. Her scholarly work demonstrates strong visibility, with measurable academic influence across citation databases. According to Scopus, she has 14 indexed documents, 16 citations by 15 documents, and an h-index of 2. Her Google Scholar profile shows significantly higher engagement, with over 250 citations across her most influential works, including widely referenced studies on nanosilica production and biomass gasification, each exceeding 100 citations. Her publications continue to inform ongoing research in sustainable materials, renewable energy pathways, and the optimization of energy–environment systems, positioning her as an active contributor to advancing cleaner technologies and carbon-reduction strategies.

Publication Profile

Scopus

Featured Publications

Gu, S., Zhou, J., Luo, Z., Wang, Q., & Ni, M. (2013). A detailed study of the effects of pyrolysis temperature and feedstock particle size on the preparation of nanosilica from rice husk. Industrial Crops and Products. Citations: 121.

Gu, S., Zhou, J., Yu, C., & Shi, Z. (2015). A novel two-staged thermal synthesis method of generating nanosilica from rice husk via pre-pyrolysis combined with calcination. Industrial Crops and Products. Citations: 105.

Gu, S., Zhou, J., Luo, Z., & Shi, Z. (2015). Kinetic study on the preparation of silica from rice husk under various pretreatments. Journal of Thermal Analysis and Calorimetry. Citations: 25.

Gu, S., Zhou, J., Lin, B., & Luo, Z. (2015). Life cycle greenhouse gas impacts of biomass gasification-exhausted heat power generation technology in China. Journal of Biobased Materials and Bioenergy..

Li, R., Gu, S., Ye, Y., Li, Z., Zhou, L., & Xu, C. (2025). System optimization and primary electrical design of a 50 MW agrivoltaic power station: A case study in China.

Dr. Fei Long | Power Systems | Most Cited Article Award

Dr. Fei Long | Power Systems | Most Cited Article Award

Lecturer, China Three Gorges University, China

Dr. Fei Long is a dedicated Lecturer and Postdoctoral Researcher at the School of Electrical Engineering and New Energy, China Three Gorges University 🇨🇳. With a deep-rooted passion for systems control and stability, he has significantly contributed to the advancement of control theory, especially in time-delay and power systems. Known for his academic rigor and innovation, Dr. Long is actively engaged in top-tier research that addresses critical challenges in modern power systems. 📊🔋

Publication profile

Scopus

🎓 Education Background

Dr. Long earned his Ph.D. in Control Science and Engineering from China University of Geosciences (Wuhan) 🎓 between September 2016 and June 2022. Prior to this, he completed his undergraduate studies in Electrical Engineering and Automation at Hubei University of Technology from 2011 to 2015 ⚡📚.

💼 Professional Experience

Since June 2022, Dr. Fei Long has been serving as a Lecturer and Postdoctoral Researcher at China Three Gorges University. His role involves both teaching and conducting cutting-edge research in the field of electrical engineering and control systems. 🏫🔍

🏆 Awards and Honors

Dr. Long is the Principal Investigator of a National Natural Science Foundation of China (Youth Science Fund Project) 🧪. Notably, two of his research papers have been recognized as ESI Highly Cited Papers, placing them in the top 1% of citations worldwide 🌍📈—a testament to the global impact of his research.

🔬 Research Focus

Dr. Long’s primary research interests lie in the stability and robust control of time-delay systems and power systems ⏳⚙️. His work emphasizes innovative mathematical modeling and control strategies to enhance system resilience and performance, particularly in neural networks and energy systems integration.

Conclusion

With a robust academic background, impactful research contributions, and a strong focus on systems stability, Dr. Fei Long stands out as a prominent young researcher in the field of electrical and control engineering. His commitment to excellence and scholarly achievement continues to inspire and shape the future of sustainable energy systems. 🚀📘

📚 Top Publication Notes

IEEE Transactions on Cybernetics (2022) Highly cited paper, recognized for its innovation in delay systems control (Cited by 100+ articles).

IEEE Transactions on Neural Networks and Learning Systems (2023) – Advanced delay-product-type functionals (Cited by 80+ articles).

Automatica (2020) – A key contribution to relaxed matrix inequalities (Cited by 120+ articles).

IEEE Transactions on Systems, Man, and Cybernetics: Systems (2021) – Used in applied power systems and neural networks (Cited by 90+ articles).

Applied Mathematics and Computation (2019) –  Mathematical advancements in delay control theory (Cited by 85+ articles).

IEEE Transactions on Cybernetics (2024) –  Recent breakthrough in delayed neural networks stability.