Mr. Yang Wu | Chemical Process Design | Best Academic Researcher Award

Published on by Superior Awards

Mr. Yang Wu | Chemical Process Design | Best Academic Researcher Award

Mr. Yang Wu | Zhejiang University | China

Mr. Yang Wu is a dedicated researcher in power and energy engineering whose work centers on hydrogen storage and cryogenic adsorption for enhancing the safety and performance of liquid hydrogen (LH) systems. His research investigates how residual gases compromise high-vacuum multilayer insulation in LH tanks and explores advanced adsorbent materials, including metal oxides, zeolites, and cryogenic compounds, to improve gas removal efficiency. With several peer-reviewed publications, including a comprehensive review on materials, methods, and integrated systems for LH storage, Yang Wu’s studies establish new perspectives on adsorbent selection and system integration. His ongoing projects focus on experimental evaluation of cryogenic adsorbents and the conceptual development of a residual-gas scavenging module to optimize LH tank reliability. By bridging materials science, thermodynamics, and engineering design, his work contributes significantly to defining standardized testing methods and advancing next-generation hydrogen storage technologies. Through his innovative approach, Yang Wu provides a foundation for safer, more efficient, and sustainable cryogenic storage systems that support the global transition to clean hydrogen energy.

Profile: Orcid

Featured Publications

Yu, M., Wu, Y., Wu, J., Zhu, Y., Yu, X., & Jiang, L. (2025). Hydrogen adsorbents in the vacuum layer of liquid hydrogen containers: Materials and applications. Hydrogen, 6(4), 89.

Dr. Kun-Ying Li | Sustainable Engineering | Best Researcher Award

Published on by Superior Awards

Dr. Kun-Ying Li | Sustainable Engineering | Best Researcher Award

Dr. Kun-Ying Li | National Chin-Yi University of Technology | Taiwan

Dr. Kun-Ying Li is an accomplished researcher in sustainable engineering and intelligent manufacturing, focusing on low-carbon technologies and energy-efficient machine tools. His research encompasses ISO14064-1:2018 greenhouse gas inventory, industrial energy-saving methods, intelligent thermal error analysis, cooling optimization, multi-objective optimization, and precision machinery design. His work emphasizes the integration of reliability engineering, applied mathematics, smart manufacturing systems, and computer-assisted engineering within the framework of Industry 4.0. He has contributed to several industry-academic collaboration projects aimed at optimizing cooling systems for multi-axis machine tools and implementing carbon inventory strategies in manufacturing transformation programs. These projects have involved partnerships with precision machinery and metal manufacturing companies, supported by national funding agencies. His expertise extends to developing innovative approaches for reducing energy consumption, enhancing process reliability, and improving the performance of advanced machine tools. Through a combination of technical insight and practical application, his research supports the transition toward intelligent, sustainable, and high-efficiency production systems that align with global goals for carbon reduction and green industry innovation. He has 220 citations by 151 documents, 29 published documents, and an h-index of 9, reflecting his consistent research contributions and growing academic influence in the fields of precision engineering and green manufacturing.

Profiles: Scopus | Google Scholar | ORCID

Featured Publications

  • Liu, Y. C., Li, K. Y., & Tsai, Y. C. (2021). Spindle thermal error prediction based on LSTM deep learning for a CNC machine tool. Applied Sciences, 11(12), 5444.

  • Li, K. Y., Luo, W. J., & Wei, S. J. (2020). Machining accuracy enhancement of a machine tool by a cooling channel design for a built-in spindle. Applied Sciences, 10(11), 3991.

  • Hsieh, M. C., Maurya, S. N., Luo, W. J., Li, K. Y., Hao, L., & Bhuyar, P. (2022). Coolant volume prediction for spindle cooler with adaptive neuro-fuzzy inference system control method. Sensors & Materials, 34, 28.

  • Li, K. Y., Luo, W. J., Hong, X. H., Wei, S. J., & Tsai, P. H. (2020). Enhancement of machining accuracy utilizing varied cooling oil volume for machine tool spindle. IEEE Access, 8, 28988–29003.

  • Li, K. Y., Maurya, S. N., Lee, Y. H., Luo, W. J., Chen, C. N., & Wellid, I. (2023). Thermal deformation and economic analysis of a multi-object cooling system for spindles with varied coolant volume control. The International Journal of Advanced Manufacturing Technology, 126(3), 1807–1821.

  • Maurya, S. N., Li, K. Y., Luo, W. J., & Kao, S. Y. (2022). Effect of coolant temperature on the thermal compensation of a machine tool. Machines, 10(12), 1201.