Dr. Seyed Reza Nabavi | Nueral Network Awards | Best Researcher Award

Dr. Seyed Reza Nabavi | Nueral Network Awards | Best Researcher AwardΒ 

Dr. Seyed Reza Nabavi, University of Mazandaran, Iran

Dr. Seyed Reza Nabavi is an Associate Professor of Applied Chemistry in the Department of Applied Chemistry at the University of Mazandaran, Babolsar, Iran. he earned his Ph.D. in Applied Chemistry from the University of Tabriz in 2009, focusing on hybrid modeling and artificial intelligence applications for olefin process optimization. As a visiting scholar at the National University of Singapore in 2008, Dr. Nabavi further honed his expertise in chemical and biomolecular engineering. His teaching repertoire spans diverse topics, including transport phenomena, chemical reactor design, and chemical process modeling at both undergraduate and postgraduate levels. A prolific researcher, his interests lie in polymer nanotechnology, catalytic processes, machine learning in chemical process optimization, and pyrolysis. Notably, he has collaborated on significant projects, such as studying coke formation and inhibitors in naphtha thermal cracking at the bench scale, bridging academia and industry. Married and based in Iran, Dr. Nabavi has received recognition for his academic excellence, including being the top-ranked B.Sc. graduate.

Professional Profile:

ORCID

Suitability for Best Researcher Award: Seyed Reza Nabavi

Based on the provided curriculum vitae, Dr. Seyed Reza Nabavi demonstrates exceptional qualifications that make him a strong candidate for the Best Researcher Award. Below is a summary of his key accomplishments and attributes supporting his suitability for this recognition

πŸŽ“ Educational Background

  • Ph.D. in Applied Chemistry (2009), University of Tabriz 🧬
    Thesis: Application of Hybrid Modeling and Artificial Intelligence in Modeling and Optimization of Olefin Processes.

    • Visiting Scholar: National University of Singapore (Apr-Dec 2008).
  • M.Sc. in Applied Chemistry (2003), University of Tabriz 🧡
    Thesis: Preparation and Characterization of Conducting Polyaniline/Nylon-6 Composite Fibers.
  • B.Sc. in Applied Chemistry (2000), University of Sistan and Baluchestan πŸ›οΈ
    • πŸŽ–οΈ First Rank among graduate students.

πŸ‘¨β€πŸ« Teaching Experience

  • Expertise in teaching at M.Sc. and B.Sc. levels πŸ“š, including advanced courses:
    • Transport Phenomena, Design of Experiments (DOE), Chemical Reactors, Process Control, and Petrochemical Processes.
    • Proficient in Modeling and Simulation and Unit Operation Laboratories.

πŸ”¬ Research Interests

  • Nanotechnology of Polymers: Electrospinning and Nanofiber Membranes 🧡.
  • Catalytic Processes: Ozonation, Photocatalysts, and Reaction Engineering βš—οΈ.
  • Modeling and Optimization: Applying Machine Learning and Evolutionary Algorithms πŸ€–.
  • Thermal Cracking & Pyrolysis: Exploring Coke Formation and Mitigation πŸ”₯.

πŸ… Academic Positions

  • Associate Professor: 2022 – Present, University of Mazandaran 🏫.
  • Assistant Professor: 2012 – 2022, University of Mazandaran.

πŸ§ͺ Research Highlights

  • Lead researcher in projects like Coke Formation and Inhibitors in thermal cracking of naphtha (collaboration with Tabriz Petrochemical Company) πŸ›’οΈ.
  • Published impactful research on polymers, reaction engineering, and optimization using cutting-edge AI techniques.

Publication top Notes:

Multi-Criteria Decision Making in Chemical and Process Engineering: Methods, Progress, and Potential

A liter scale synthesis of hierarchically mesoporous UiO-66 for removal of large antibiotics from wastewater

Data-Based Modeling, Multi-Objective Optimization and Multi-Criteria Decision Making of a Catalytic Ozonation Process for Degradation of a Colored Effluent

A bacterial cellulose-based LiSrVO4:Eu3+ nanosensor platform for smartphone sensing of levodopa and dopamine: point-of-care diagnosis of Parkinson’s disease

Parametric optimization of poly(ether sulfone) electrospun membrane for effective oil/water separation

Deep Learning Aided Multi-Objective Optimization and Multi-Criteria Decision Making in Thermal Cracking Process for Olefines Production