Salomon Dominique Edimo Kingue | Engineering | Research Excellence Award

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Mr. Salomon Dominique Edimo Kingue | Engineering | Research Excellence Award

State University of Campinas | Brazil

Mr. Salomon Dominique Edimo Kingue is a reservoir engineer and researcher specializing in enhanced oil recovery (EOR), reservoir simulation, and sustainable subsurface energy strategies. His expertise centers on FAWAG/WAG processes, CO₂ storage modeling, and integrated reservoir–production optimization for complex carbonate systems, particularly within Brazilian pre-salt environments. He is highly skilled in using CMG (IMEX, STARS, GEM, CMOST), Petrel, Python, and advanced analytical tools to investigate flow behavior, improve recovery efficiency, and reduce greenhouse gas emissions. His research spans numerical simulation of EOR mechanisms, uncertainty analysis, carbon capture and storage (CCS), fractured-vuggy reservoir upscaling, and evaluation of production potential in hydrocarbon basins. He has co-authored studies on underground LPG storage and reservoir performance prediction, and contributed to interdisciplinary projects involving major industry partners. His work also extends to geological interpretation, multidisciplinary collaboration, and scientific communication through symposiums, poster sessions, and peer-reviewed publications. Salomon combines strong analytical reasoning with leadership, teamwork, and effective communication, reflecting his commitment to innovation-driven reservoir management and the advancement of low-carbon energy solutions.

Profile: Orcid

Featured Publications

Kingue, S. D. E., Akinmuda, O. B., Kuiekem, D., & Djitchouang, G. L. (2025). Assessing the production potential of Niger Delta reservoirs under uncertainty using numerical simulation tools. Petroleum Science and Technology.

Kuiekem, D., Kingue, S. D. E., Boroh, W., Noupa, R. K., Matateyou, J., & Ngounouno, I. (2025). Simulation study of underground LPG storage in a depleted conceptual oil reservoir. Petro Chem Indus Intern, 8(2), 1–14.

Siyao Li | Chemical Engineering | Best Researcher Award

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Ms. Siyao Li | Chemical Engineering | Best Researcher Award

Professor at East China University of Science and Technology, China

Siyao Li is a distinguished chemical engineer with a focus on membrane technology, materials science, and liquid/gas separations. Holding a PhD from Imperial College London (2022) under the mentorship of Andrew Livingston and Suzana P. Nunes, he is currently an Associate Professor at East China University of Science and Technology. His research spans ultrathin polyamide membranes, polymeric materials for separation processes, and the development of sustainable technologies for environmental and industrial applications. His work has been published in top journals such as Science and Nature. 🌱🧪

Publication Profile : 

Scopus

Orcid

Educational Background 🎓

🎓 PhD in Chemical Engineering | Imperial College London, UK (2018-2022)
Advisor: Prof. Andrew Livingston | Mentor: Prof. Suzana P. Nunes
Project: Investigation of Ultrathin Polyamide Nanofilms Incorporating Novel Amines for Liquid Separations

🎓 MSc in Advanced Chemical Engineering | Imperial College London, UK (2017-2018)
Advisor: Prof. Bradley Ladewig
Project: Covalent Organic Frameworks as Low-Energy CO2 Adsorbents for Mixed Matrix Membranes

🎓 BEng in Chemical Engineering and Technology | Tianjin University, China (2013-2017)
GPA: 3.82/4 | Ranking: Top 4%

Professional Experience 💼

🔬 East China University of Science and Technology — Professor (2024-Present)
🔬 East China University of Science and Technology — Associate Professor (2023-2024)
🔬 Exponent Science and Technology Consulting Co. Ltd — Associate (2022-2023)
🧑‍🏫 Imperial College London — Teaching Assistant (2019-2021)
💼 Exxon Mobil Corporation — Summer Intern (2017)
💨 Vestas Wind Systems A/S — Intern (2017)

Research Interests 🔬

🔬 Polymeric Membranes for Separations: Development of hydrophobic polyamide membranes for crude oil separation and novel molecular sieving techniques.
💡 Nanotechnology & Material Science: Synthesis of advanced materials like covalent organic frameworks (COFs) and integration into mixed matrix membranes for enhanced gas and liquid separations.
🌍 Sustainable Separation Technologies: Focus on low-energy, high-efficiency methods for CO2 capture and energy-efficient separation processes.

Awards & Honors 🏆

🏆 Full PhD Scholarship (2018-2022) | Excellent Graduate of Imperial College London (2017) | Excellent Student Awards (Top 5%) for 3 years (2014-2016)

Publications 📚

  1. S. Li, R. Dong, V. E. Musteata, J. Kim, N. D. Rangnekar, J. R. Johnson, S. Chisca, B. A. McCool, S. P. Nunes, Z. Jiang*, A. G. Livingston*, “Hydrophobic Polyamide Nanofilms Provide Rapid Transport for Crude Oil Separation,” Science, 2022, 377, 1555-1561.
  2. S. Li, N. Prasetya, B. Ladewig*, “Investigation of Azo-COP-2 as a Photoresponsive Low-Energy CO2 Adsorbent and Porous Filler in Mixed Matrix Membranes for CO2/N2 Separation,” Ind. Eng. Chem. Res., 2019, 58(23), 9959-9969.
  3. Z. Jiang, R. Dong, A. M. Evans, N. Biere, M. A. Ebrahim, S. Li, D. Anselmetti, W. R. Dichtel, A. G. Livingston*, “Aligned Macrocycle Pores in Ultrathin Films for Accurate Molecular Sieving,” Nature, 2022, 609, 58-64.
  4. W. Xu, Y. Wang*, Y. Wu, F. Xu, K. Qu, L. Dai, J. Wang, J. Wu, L. Lei, S. Li, Z. Xu*, “Sub-2-nm Channels within Covalent Triazine Framework Enable Fast Proton-Selective Transport in Flow Battery Membrane,” Adv. Func. Mater., 2023, 2300138.
  5. L. Dai, S. Pang, S. Li, Z. Yi, K. Qu, Y. Wang, Y. Wu, S. Li, L. Lei, K. Huang, X. Guo, Z. Xu*, “Freestanding Two-Dimensional Nanofluidic Membranes Modulated by Zwitterionic Polyelectrolyte for Mono-/Di-Valent Ion Selectivity Transport,” J. Membr. Sci., 2023, 677: 121321.

 

 

 

Manas Ranjan Sethi | ECE | Best Researcher Award

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Mr. Manas Ranjan Sethi | ECE | Best Researcher Award

Research Scholar at NIT Silchar, India

Manas Ranjan Sethi is a dedicated academic professional currently pursuing a Ph.D. in Electronics and Instrumentation Engineering at NIT Silchar, with a strong focus on machine learning applications in fault diagnosis and energy systems. He holds an M.Tech in Electronics & Telecommunication from BPUT, Odisha and has over 12 years of teaching experience, having worked as an Assistant Professor at Gandhi Institute for Technology (GIFT) and as a Lecturer at Koustuv Institute of Self Domain, Bhubaneswar. His research interests include machine learning, signal processing, and sustainable energy systems, particularly in wind turbine diagnostics and emotion recognition using EEG signals. Manas has contributed to numerous journals, conferences, and book chapters, and he has earned distinctions such as qualifying CBSE-UGC NET and GATE. He is also skilled in technical tools, enjoys singing, and has a passion for reading.

Publication Profile : 

Scopus

 

🎓 Educational Background :

Manas Ranjan Sethi is currently pursuing a Ph.D. in Electronics and Instrumentation Engineering at NIT Silchar (since March 2020). He holds an M.Tech in Electronics & Telecommunication (Specialization in Communication Engineering) from BPUT, Odisha (2012), with a CGPA of 8.20. He completed his B.E. in Electronics & Telecommunication from BPUT, Odisha, in 2006, securing a 62.19%. Additionally, he completed his +2 Science and Matriculation from MP Board, Madhya Pradesh.

💼 Professional Experience :

Manas has a rich academic career spanning over 12 years. He served as an Assistant Professor in the Electronics & Communication Engineering Department at Gandhi Institute for Technology (GIFT), Bhubaneswar, from November 2013 to March 2020. Prior to that, he worked as a Lecturer in the Electronics & Telecommunication Engineering Department at Koustuv Institute of Self Domain, Bhubaneswar, from July 2007 to November 2013. He has a strong foundation in teaching and mentoring students in the field of Electronics and Communication Engineering.

📚 Research Interests : 

Manas’s research interests lie in the domains of Machine Learning, Signal Processing, and Fault Diagnosis. His work focuses on vibration signal-based diagnostics and energy extraction using wind turbines. He is passionate about leveraging machine learning techniques for predictive maintenance and condition monitoring. His recent research includes the application of meta-classifiers for diagnosing wind turbine blade faults and exploring emotion recognition through EEG signals.

🏆Achievements & Certifications :

Manas has earned several academic distinctions, including qualifying the CBSE-UGC NET (Electronic Science) in July 2018, and securing GATE scores of 260 (2016) and 218 (2011) in Electronics and Communication. He has also attended and contributed to various seminars, workshops, and short-term courses in fields such as VLSI Design, Microwave Filters, and Adaptive Signal Processing.

📝 Publication Top Notes :

  • Sethi, M. R., Subba, A. B., Faisal, M., Sahoo, S., & Koteswara Raju, D. (2024). Fault diagnosis of wind turbine blades with continuous wavelet transform based deep learning model using vibration signal. Engineering Applications of Artificial Intelligence, 138, 109372.
  • Sethi, M. R., Sahoo, S., Dhanraj, J. A., & Sugumaran, V. (2023). Vibration Signal-Based Diagnosis of Wind Turbine Blade Conditions for Improving Energy Extraction Using Machine Learning Approach. Smart and Sustainable Manufacturing Systems, 7(1), 14–40.
  • Chatterjee, S., Sethi, M. R., & Asad, M. W. A. (2016). Production phase and ultimate pit limit design under commodity price uncertainty. European Journal of Operational Research, 248(2), 658–667.
  • Sethi, M. R., Parhi, S. S., Sahoo, S., Sugumaran, V., & Mohanty, S. R. (2023). Fault Diagnosis of Wind Turbine Blades Through Vibration Signal Using Filtered Cultivation Data: A Comparative Study. Proceedings of the 2023 IEEE Region 10 Symposium, TENSYMP 2023.
  • Kar, P., Hazarika, J., & Sethi, M. R. (2023). A Comparative Study between Supervised and Unsupervised Techniques for Two Class Emotion Recognition using EEG. Proceedings of the 2023 IEEE 8th International Conference for Convergence in Technology, I2CT 2023.
  • Banala, H. S., Sahoo, S., Sethi, M. R., & Sharma, A. K. (2023). Fault Diagnosis in Wind Turbine Blades Using Machine Learning Techniques. In R. Doriya, B. Soni, A. Shukla, & X. Z. Gao (Eds.), Machine Learning, Image Processing, Network Security and Data Sciences (Lecture Notes in Electrical Engineering, Vol. 946), 401–411. Springer, Singapore.
  • Sethi, M. R., Sahoo, S., Kanoongo, S., & Hemasudheer, B. (2022). A Comparative Study on Diagnosing Wind Turbine Blade Fault Conditions using Rule Classifier. Proceedings of the 2022 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET 2022), 1–6. doi: 10.1109/ICEFEET51821.2022.9848401.
  • Sethi, M. R., Hemasudheer, B., Sahoo, S., & Kanoongo, S. (2022). A Comparative Study on Diagnosing Wind Turbine Blade Fault Conditions using Vibration Data through META Classifiers. Proceedings of the 2022 4th International Conference on Energy, Power, and Environment (ICEPE 2022), 1–5. doi: 10.1109/ICEPE55035.2022.9798026.