Wentao Shang | Green Technology | Best Researcher Award

Published on by Technology Scientists

Best Researcher Award

Wentao Shang
Affiliation Jinan University
Country China
Scopus ID 57604364900
Documents 34
Citations 812
h-index 15
Subject Area Green Technology
Event Technology Scientists Awards
ORCID 0000-0002-5168-7696

Wentao Shang is affiliated with Jinan University, China, and works across membrane science, separation technologies, computational prediction, imaging, and advanced materials. His recent scholarly record includes research on membrane distillation, nanofiltration fouling prediction, and supramolecular materials, providing a multidisciplinary basis for consideration within the field of green technology. [1] [2] [3]

Abstract

Wentao Shang is a researcher at Jinan University whose documented work connects membrane science, green technology, computational modeling, imaging, and advanced materials. His recent publications examine surface patterning for membrane distillation, multimodal convolutional neural networks for dynamic nanofiltration fouling prediction, and solution-sheared supramolecular oligomers with improved thermal-resistant adhesion. These studies demonstrate an interdisciplinary approach combining materials engineering, separation processes, experimental characterization, and data-driven analysis. With 34 documented publications, 812 citations, and an h-index of 15, his profile indicates sustained scholarly activity and measurable research visibility. The breadth and environmental relevance of these themes support consideration for a Best Researcher Award.

Keywords

Keywords: Green Technology, Membrane Distillation, Nanofiltration, Membrane Fouling, Optical Coherence Tomography, Convolutional Neural Networks, Surface Patterning, Advanced Materials, Supramolecular Oligomers, Sustainable Engineering.

Introduction

Wentao Shang’s research profile at Jinan University reflects an interdisciplinary focus connecting membrane processes, nanofiltration, imaging-based analysis, advanced materials, and sustainable engineering. His recent publications address membrane distillation, fouling prediction, and thermally resistant supramolecular materials, indicating a research trajectory relevant to emerging green technology and resource-efficient engineering. [1] [2] [3]

Research Profile

Shang is associated with research spanning membrane science, separation technologies, computational prediction, and functional materials. His publication record includes studies using surface patterning to improve membrane distillation and multimodal convolutional neural networks to model nanofiltration fouling. These themes connect experimental characterization, materials engineering, and data-driven methods for environmental applications. [1] [2]

Research Contributions

Shang’s contributions can be viewed through three complementary areas: engineering membrane surfaces for improved separation performance, applying in-situ optical coherence tomography and multimodal neural networks to characterize fouling dynamics, and investigating supramolecular materials with enhanced thermal and adhesive properties. Together, these studies demonstrate integration of experimental methods, computational analysis, and materials design. [1] [2] [3]

Publications

The documented publications associated with Shang include a 2026 review of surface patterning in membrane distillation, a 2026 Desalination article on multimodal convolutional neural networks for nanofiltration fouling prediction, and a Nature Communications study on solution-sheared supramolecular oligomers. The works collectively cover membrane engineering, machine learning, imaging, adhesion, and advanced materials. [1] [2] [3]

Research Impact

The research has potential relevance to green technology through improved membrane efficiency, fouling management, and durable functional materials. Surface-engineered membranes may support cleaner separation processes, while predictive imaging models can improve understanding of fouling development. Work on thermally resistant adhesives further broadens the profile toward resource-conscious and performance-oriented materials engineering. [1] [2] [3]

Award Suitability

The Best Researcher Award profile is supported by a combination of publication activity, citation indicators, interdisciplinary research themes, and alignment with green technology. The reported record of 34 documents, 812 citations, and an h-index of 15 provides quantitative evidence of scholarly visibility, while recent publications demonstrate continuing research activity. [1] [2] [3]

Conclusion

Wentao Shang presents a research profile combining membrane technology, computational modeling, imaging, and advanced materials. His recent work addresses practical challenges in separation efficiency, fouling prediction, and material durability. The combination of documented scholarly output and green-technology relevance provides a reasonable academic basis for consideration under the Best Researcher Award. [1] [2] [3]

References

  1. Zhang, C., Lin, Y., Lu, G., Yuan, B., Chen, P., Farid, M. U., Lee, V. P. H., Shang, W., Li, W., & An, A. K. (2026). Surface patterning in membrane distillation: Fabrication, mechanism, and performance enhancement. Separation and Purification Technology, 394(Part 3), Article 137561.
    https://www.sciencedirect.com/science/article/abs/pii/S1383586626008270
  2. Shang, W., Zeng, Y., Xiao, F., Wu, M., Wang, Y., Yang, Z., He, J., & Sun, F. (2026). A multimodal convolutional neural network trained by in-situ OCT characterization for dynamic structural prediction of nanofiltration fouling. Desalination, 639, Article 120676.
    https://www.sciencedirect.com/science/article/pii/S0011916426008325
  3. Lu, G., Ma, R., Zhao, Y., Wang, D., Shang, W., Chen, H., Khan, S. A., Li, M., & Saiz, E. (2025). Solution-sheared supramolecular oligomers with enhanced thermal resistance in interfacial adhesion and bulk cohesion. Nature Communications, 16, 7754.
    https://www.nature.com/articles/s41467-025-63123-9
  4. Elsevier. (n.d.). Scopus author details: Wentao Shang, Author ID 57604364900. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57604364900
  5. ORCID. (n.d.). Wentao Shang, ORCID 0000-0002-5168-7696. ORCID.
    https://orcid.org/0000-0002-5168-7696

Yucen Yuan | New energy | Research Excellence Award

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Mr. Yucen Yuan | New energy | Research Excellence Award

Lanzhou Jiaotong University | China

Mr. Yucen Yuan is an early-career researcher affiliated with Lanzhou Jiaotong University, China, with a focused research profile in intelligent fault diagnosis and data-driven condition monitoring of renewable energy systems. His work lies at the intersection of machine learning, optimization algorithms, and mechanical fault detection, with particular emphasis on wind turbine bearing health assessment. Yuan has authored 2 peer-reviewed publications, accumulating 1 citation to date and 1 h-index, reflecting emerging scholarly visibility. His 2025 article in Engineering Research Express introduces an improved dung beetle optimizer–enhanced LSTM framework, demonstrating methodological innovation in time-series fault diagnosis. This contribution highlights his expertise in deep learning optimization, signal analysis, and industrial predictive maintenance. Yuan has engaged in collaborative research, contributing as part of a small co-author network, and his work supports the reliability and sustainability of wind energy infrastructure. The societal impact of his research aligns with global clean energy goals by advancing intelligent monitoring technologies that reduce equipment failure, maintenance costs, and operational risks in renewable power systems.

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Featured Publications

Nuttapat Jittratorn | Renewable Energy | Best Researcher Award

Published on by Technology Scientists

Mr. Nuttapat Jittratorn | Renewable Energy | Best Researcher Award

Ph.D. Candidate in Electrical Engineering, National Cheng Kung University, Taiwan.

Nuttapat Jittratorn is a passionate Ph.D. candidate in Electrical Engineering at National Cheng Kung University, Taiwan. With a deep-rooted commitment to renewable energy innovation, he has led over 10 collaborative projects across Taiwan and Japan, applying AI to enhance energy forecasting systems. His academic and industrial experience spans solar PV, wind power, and hybrid energy systems. Nuttapat’s interdisciplinary expertise merges machine learning with real-time deployment, helping industries such as TSMC and Delta Electronics optimize energy use. Recognized with the Best Oral Presentation Award at the 2025 IEEE IAS Annual Meeting, he also contributes to academic leadership as a session chair and student mentor. A forward-thinking researcher fluent in English and Thai, he continues to bridge research with sustainable industrial solutions.

🧾Author Profile

🎓 Education

Nuttapat Jittratorn began his academic journey at Kasetsart University, Thailand, earning a Bachelor of Engineering in Electrical Engineering (2014–2018). He then pursued his Master’s degree at National Chung Cheng University in Taiwan, where he deepened his focus on renewable energy systems and intelligent computation (2018–2021). Currently, he is a Ph.D. candidate in Electrical Engineering at National Cheng Kung University, Taiwan (2021–present). His doctoral research centers on enhancing the reliability and accuracy of energy forecasting using artificial intelligence. Throughout his studies, Nuttapat has maintained a strong interdisciplinary approach, integrating engineering principles with emerging technologies like deep learning and hybrid modeling. His academic path reflects a consistent commitment to solving global energy challenges through intelligent system design and applied machine learning in energy grids.

💼 Experience 

Since 2021, Nuttapat has played pivotal roles as Team Leader, Project Advisor, and Researcher across Taiwan and Japan. He has collaborated with leading institutions and corporations such as TSMC, Delta Electronics, FarEasTone Telecom, and the National Science and Technology Council. His work involves real-time AI-powered forecasting systems for solar, wind, and multi-load applications in power and steam. Nuttapat has led the development and deployment of models in real-world industrial settings, optimizing power generation and usage. As a Thesis Advisor at Ton Duc Thang University (2022–2023), he mentored students in AI-energy research and thesis defense preparation. His projects span Changhua, Hsinchu, Tainan, Taoyuan, and Kagoshima, showcasing his ability to drive innovation in dynamic, multinational environments.

🏅 Honors & Awards 

Nuttapat Jittratorn was awarded the Best Oral Presentation Award in the Renewable and Sustainable Energy Conversion track at the 2025 IEEE IAS Annual Meeting, recognizing his research impact in intelligent PV and wind power forecasting. Additionally, he served as the Session Chair at the same Award, a testament to his leadership and recognition in the energy research community. His collaborative research and advisory roles in academia and industry have positioned him as a standout researcher in applied energy systems. These achievements underscore his ability to produce not just high-quality publications, but also real-world, industry-transforming outcomes that align with global sustainability goals.

🔬 Research Focus 

Nuttapat’s research is centered on AI-based renewable energy forecasting. He develops intelligent models for very short-term and short-term prediction of solar PV and wind power generation. His focus includes hybrid techniques that combine LSTM, Markov models, and probabilistic correction based on environmental data like wind speed. He also explores energy storage integration, such as BESS (Battery Energy Storage Systems), to enhance operational efficiency. His work bridges data science and engineering, ensuring models are not only accurate in labs but also viable for real-world deployment in industrial energy management. His interdisciplinary projects support Taiwan and Japan’s energy industries in transitioning toward smarter and more reliable grid systems. His research is forward-looking, contributing directly to the goals of a low-carbon economy and sustainable industrial operations.

Publication Top Notes

1. A Hybrid Method for Hour-Ahead PV Output Forecast with Historical Data Clustering

Authors: N. Jittratorn, G.W. Chang, G.Y. Li
Conference: 2022 IET International Conference on Engineering Technologies
Citations: 4
Summary: This paper proposes a clustering-based hybrid model for predicting hour-ahead PV output. Historical meteorological data are clustered to create more accurate baseline patterns, improving forecast accuracy. The model has industrial applications for solar plant operation scheduling.

2. Very Short-Term Wind Power Forecasting Using a Hybrid LSTM-Markov Model Based on Corrected Wind Speed

Authors: A.N. Jittratorn, B.C.M. Huang, C.H.T. Yang
Journal: Renewable Energy and Power Quality Journal, Vol. 21, pp. 433–438
Year: 2023 | Citations: 2
Summary: A hybrid forecasting framework combining LSTM and a Markov decision structure, this study corrects input wind speed for improving wind power forecasts within minutes to hours. Effective for wind turbine operational control and energy market participation.

3. A Deterministic and Probabilistic Framework Based on Corrected Wind Speed to Improve Short-Term Wind Power Forecasting Accuracy

Authors: N. Jittratorn, C.M. Huang, H.T. Yang
Journal: International Journal of Electrical Power & Energy Systems, Vol. 170, 110859
Year: 2025
Summary: This journal article presents an advanced dual-framework model integrating deterministic forecasts with probabilistic corrections, improving reliability in fluctuating wind environments. It’s particularly useful for risk-aware grid management and dispatch.

4. Short-Term Forecasting of Wind Power Plant Generation Based on Machine Learning Models

Authors: M.N. Phan, K.P. Nguyen, V. Van Huynh, C.M. Huang, H.T. Yang, N. Jittratorn, et al.
Conference: 2025 IEEE 1st International Conference on Smart and Sustainable Developments
Year: 2025
Summary: Collaborative paper exploring various machine learning models for short-term wind forecasting. Nuttapat contributed to model selection, tuning, and integration with real-time plant data.

5. PV Power Forecasting for Operation of BESS Integrated with a PV Generation Plant

Authors: N. Jittratorn, C.S. Liu, C.M. Huang, H.T. Yang
Conference: 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA)
Year: 2024
Summary: Proposes a new forecasting model to manage PV+BESS operation, ensuring optimal battery use while minimizing forecast error. Critical for smart energy storage deployment in renewable infrastructure.

🏅 Conclusion

Nuttapat Jittratorn is a highly promising early-career researcher with solid technical, academic, and leadership credentials. His contributions to AI-driven energy forecasting and integration with industrial applications stand out. While still in the Ph.D. phase, his research maturity, real-world impact, and academic service position him as a strong candidate for the Best Researcher Award, particularly in the applied energy systems or smart grid technologies domain.