Hadi Belhaj | Energy | Best Faculty Award

Hadi Belhaj | Energy | Best Faculty Award

Prof Hadi Belhaj, Khalifa University, United Arab Emirates

Based on the provided information, Prof. Hadi Belhaj appears to be a highly suitable candidate for the Research for Best Faculty Award.

Publication profile

google scholar

  1. Educational Background: Prof. Belhaj holds a Ph.D. in Petroleum Engineering from Dalhousie University, which establishes his strong academic foundation in the field.
  2. Extensive Research Experience: His involvement in numerous funded research projects, including studies on enhanced oil recovery (EOR), CO2 sequestration, and unconventional reservoir engineering, demonstrates his active contribution to advancing petroleum engineering knowledge.
  3. Leadership in Research Projects: Prof. Belhaj has served as the principal investigator on several significant projects, with budgets reaching into the millions of dollars, indicating his ability to lead large-scale research initiatives successfully.
  4. Publications and Contributions: He has authored numerous journal papers and a book on tight oil reservoirs, showcasing his expertise and contributions to academic literature.
  5. Recognition and Awards: Prof. Belhaj has received several prestigious awards, including the SPE International Distinguished Service Award and the SPE Regional Distinguished Achievement for Petroleum Engineering Faculty Award, which recognize his excellence in research and service.
  6. Professional Service and Memberships: His active participation in professional societies like the Society of Petroleum Engineers (SPE) and his role in various committees underline his commitment to the broader engineering community.
  7. Teaching and Mentorship: His roles in mentoring students, advising SPE student chapters, and organizing training programs demonstrate his dedication to education and fostering the next generation of petroleum engineers.

Given these accomplishments and his significant impact on both research and education in petroleum engineering, Prof. Hadi Belhaj is indeed a strong contender for the Research for Best Faculty Award.

Publication top notes

Application of nanotechnology by means of nanoparticles and nanodispersions in oil recovery-A comprehensive review

Ionic liquids as alternatives of surfactants in enhanced oil recovery—A state-of-the-art review

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Sand-production prediction: a new set of criteria for modeling based on large-scale transient experiments and numerical investigation

Comprehensive transient modeling of sand production in horizontal wellbores

Rock properties evaluation for carbonate reservoir characterization with multi-scale digital rock images

Enhanced oil recovery by nonionic surfactants considering micellization, surface, and foaming properties

 

Irfan Ali Channa | Power System Control | Excellence in Research

Dr. Irfan Ali Channa | Power System Control | Excellence in Research

Ph.D Scholor, Institute of Automation, Beijing University of Chemical Technology, Beijing China

Irfan Ali Channa is a highly motivated PhD scholar at the Institute of Automation, Beijing University of Chemical Technology in China. He is passionate about innovation in the field of AI, particularly in power systems. Irfan has a strong background in experimental design, literature review, and scientific writing. His career includes significant experience as a lecturer at Bahria Engineering University in Karachi, Pakistan, where he contributed to both academic and engineering solutions.

Education 🎓

Irfan Ali Channa is currently pursuing his PhD at the Institute of Automation, Beijing University of Chemical Technology in Beijing, China, a program he began in 2019 and is expected to complete in 2024. His advanced studies focus on leveraging artificial intelligence to enhance power systems. Prior to this, he developed a solid foundation in engineering education and research during his tenure at Bahria Engineering University in Karachi, Pakistan.

Experience 🏫

From 2014 to 2017, Irfan Ali Channa served as a lecturer at Bahria Engineering University in Karachi, Pakistan. During this period, he was responsible for providing academic services to students, developing engineering solutions, and facilitating research innovations. His role involved extensive interaction with both students and faculty, promoting a collaborative and progressive educational environment.

Research Interests 🔬

Irfan’s research interests lie primarily in the application of artificial intelligence in power systems. He is particularly focused on developing innovative methodologies for the classification and detection of power quality disturbances. His work frequently involves the use of advanced techniques such as deep learning, evolutionary algorithms, and swarm-based optimization. Irfan is also interested in renewable energy sources, as evidenced by his studies on wind potential and photovoltaic modules.

Awards 🏆

Throughout his academic and professional career, Irfan Ali Channa has received recognition for his contributions to the field of electrical engineering. His innovative research and dedication to advancing power system technology have earned him accolades within the academic community, particularly for his work on power quality disturbances and renewable energy analysis.

Publications Top Notes 📚

  1. A new deep learning method for classification of power quality disturbances using DWT-MRA in utility smart grid
    • Published in: Computers and Electrical Engineering, Elsevier (2024)
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    • Cited by: Articles focused on power quality and smart grid technology.
  2. Evolutionary and Swarm Based Optimization of Fit k-Nearest Neighbor Classifier for Classification of Power Quality Disturbances
    • Published in: Electric Power Components and Systems (2023)
    • Cited by: Articles related to optimization algorithms in electrical systems.
  3. Detection and classification of power quality disturbances using STFT and deep neural Network
    • Published in: Proceedings of the 2023 7th International Conference on Computer Science and Artificial Intelligence (2023)
    • Cited by: Research on deep learning applications in power quality analysis.
  4. A comparative study to analyze wind potential of different wind corridors
    • Published in: Energy Reports, Volume 9 (2023)
    • Cited by: Studies on renewable energy and wind power assessment.
  5. Temperature and irradiance based analysis of specific variation of PV module
    • Published in: Jurnal Teknologi, Volume 83(6) (2021)
    • Cited by: Research on photovoltaic module performance under varying conditions.