Dr. Maria Zednikova | Chemical Engineering | Best Researcher Award

Institute of Chemical Process Fundamentals of the CAS | Czech Republic

Profiles

Early Academic Pursuits

Dr. Mária Zedníková began her academic journey in chemical engineering with a master’s degree followed by a doctoral degree, both from the Institute of Chemical Technology in Prague. Her strong foundational training laid the groundwork for her subsequent focus in multiphase systems, fluid mechanics, and process engineering. These formative years were marked by deep engagement with core engineering principles and practical laboratory experience, which shaped her research orientation toward hydrodynamics and gas-liquid interactions.

Professional Endeavors

Dr. Zedníková’s professional career is deeply rooted in the Institute of Chemical Process Fundamentals (ICPF), where she has steadily progressed from junior researcher to research scientist and eventually to a leadership role as Head of the Department of Chemical Engineering and Head of the Research Group for Multiphase Reactors. Her trajectory reflects both scientific depth and leadership capacity. She has also taken on academic responsibilities as a teacher at the University of Chemistry and Technology in Prague, expanding her influence to the educational sphere. Notably, her international experience includes collaborative research stays in the United Kingdom and Italy, reflecting her commitment to cross-border scientific exchange and cooperation.

Contributions and Research Focus

Dr. Zedníková’s research is centered on multiphase flow systems with a special emphasis on gas-liquid interactions, bubble dynamics, and mass transfer phenomena. Her work delves into the complex behaviors of bubbles and drops in turbulent environments, investigating how these particles deform, break up, and interact with fluid structures. She has developed theoretical models and experimental setups to study hydrodynamics, drop-particle collisions, and surfactant effects on fluid interfaces. Additionally, her research in stirred tank reactors and gas-lift systems addresses crucial industrial applications in chemical processing, biotechnology, and environmental engineering.

Impact and Influence

Her influence is visible both through her scholarly output and her engagement with the broader scientific community. Dr. Zedníková has authored 30 original research papers, the majority of which appear in peer-reviewed, high-impact journals. She has also contributed a chapter to a scientific monograph and holds a registered utility model. Her active presence at international conferences with over 90 contributions demonstrates her role in advancing and disseminating knowledge in her field. Furthermore, her involvement in national and international research projects as both leader and team member has positioned her as a respected figure in collaborative scientific networks.

Academic Citations and Recognition

With an H-index of 14 and nearly 700 citations most of which are from independent researchers Dr. Zedníková has established a significant academic footprint. Her research is regularly cited by peers, which confirms the value and relevance of her work in the scientific community. The Best Poster Award from the European Federation of Chemical Engineering and her active membership in professional societies and scientific committees further highlight her recognition and contributions on both national and European platforms.

Technical Skills

Dr. Zedníková possesses extensive technical expertise in experimental design, fluid flow diagnostics, multiphase reactor modeling, and mass transfer analysis. Her work includes advanced techniques for measuring bubble deformation, analyzing flow regimes, and modeling surfactant behavior in dynamic fluid systems. Her ability to integrate theoretical modeling with experimental validation distinguishes her technical acumen in the chemical engineering domain.

Teaching Experience

As an educator, Dr. Zedníková has taken a leading role in a wide range of chemical engineering subjects. She has conducted lectures and seminars on fluid mechanics, chemical technologies, process projects, and laboratory courses. Her engagement in mentoring includes the supervision of over 20 master’s, bachelor’s, and internship students. This dedication to teaching complements her research activities and ensures knowledge transfer to the next generation of engineers and scientists.

Legacy and Future Contributions

Dr. Zedníková’s career reflects a consistent drive for scientific innovation, education, and leadership. Her multidisciplinary collaborations, international exposure, and applied research make her a role model in the engineering sciences. Looking ahead, her ongoing research on bubble dynamics, reactor hydrodynamics, and surfactant behavior is expected to contribute significantly to chemical process optimization and sustainability. Her involvement in international committees and editorial boards also sets the stage for continued influence in shaping research agendas and policy in chemical engineering.

Notable Publications

Dynamic regimes in granular mixing: Effect of sliding friction and stirrer rotational frequency
Authors: Martin Kozakovic, David Kramolis, Maria Zednikova, Stanislav Parez, Jaromir Havlica
Journal: Powder Technology
Year: 2025
Size distribution of daughter bubbles or drops resulting from binary breakup due to random initial deformation conditions
Authors: Maria Zednikova, Petr Stanovsky, Sandra Orvalho
Journal: Separation and Purification Technology
Year: 2025
Gas phase behaviour in environment of fermentation processes
Authors: Adrián Žák, Lukáš Valenz, Tomáš Moucha, Maria Zednikova
Journal: Chemical Engineering Research and Design
Year: 2025
Viscosity influence on hydrodynamics behaviour in a stirred tank reactor
Authors: Adrián Žák, Mária Zedníková, Tomáš Moucha
Journal: Chemical Engineering Research and Design
Year: 2025
Surfactant effect on bubble deformation and breakup after interaction with vortex structure
Authors: Maria Zednikova, Tereza Semlerová, Sandra Orvalho, Jaromir Havlica, Jaroslav Tihon
Journal: Chemical Engineering Science
Year: 2025

Conclusion

Dr. Mária Zedníková is a highly accomplished chemical engineering researcher with deep expertise in multiphase flow systems and hydrodynamics. Her balanced portfolio of research, teaching, leadership, and international collaboration reflects a dynamic and impactful career. Her academic and professional journey illustrates a strong commitment to advancing both fundamental science and its practical applications. She stands out as a leading figure in her field, with a legacy that is poised to grow in the coming years.

Mr. Filip Rękas | Computational Modeling | Best Researcher Award

Rzeszów University of Technology, Poland

👨‍🎓Profiles

ORICD Profile

🎓 Early Academic Pursuits

Mr. Filip Rękas began his academic journey at the Rzeszów University of Technology in Poland, where he earned both his Bachelor of Engineering and Master of Science in Engineering in Chemical Technology. He graduated with the highest distinction (5.0 / A) for his master’s degree. His early interests centered on polymer synthesis, process modeling, and Monte Carlo simulations, which laid the groundwork for his computational research career.

🧪 Professional Endeavors

Currently a PhD student in Chemical Engineering, Mr. Rękas focuses on advanced computational techniques, particularly the integration of machine learning into chemical process modeling and optimization. A self-taught programmer, he independently develops customized simulation software and machine learning models that bridge the gap between chemical engineering and artificial intelligence. His research embodies an interdisciplinary approach, combining classical engineering, computer science, and applied mathematics.

🔬 Contributions and Research Focus

Mr. Rękas specializes in machine learning applications for chemical engineering, with a specific focus on physics-informed neural networks (PINNs) for solving complex partial differential equations. His models, named A1 and A2, have demonstrated the ability to predict concentration profiles in gradient liquid chromatography (GLC) under various elution conditions. These include nonlinear gradients, fast and slow gradient adjustments, and systems with mass transfer resistances. By embedding system dynamics into the loss functions of the neural networks, he achieved high prediction accuracy while reducing computation time by a factor of 272 compared to the OCFE method—highlighting his contributions to real-time process optimization.

🤝 Collaborations

Mr. Rękas collaborates with several prominent researchers. He works closely with Prof. Krzysztof Kaczmarski, an internationally recognized expert in chromatographic modeling and process scale-up, and with Dr. Eng. Marcin Chutkowski, a specialist in powder process modeling using the Discrete Element Method (DEM). Their joint research involves applying machine learning to enhance gradient liquid chromatography. Additionally, he collaborates with Prof. Jaromir Lechowicz on modeling polymerization and degradation phenomena, using Monte Carlo simulations and artificial intelligence for advanced material behavior predictions.

🌍 Impact and Influence

Although early in his research career, Mr. Rękas has already contributed to high-impact computational methods that address complex chemical systems. His innovative use of machine learning in chemical process engineering demonstrates potential for revolutionizing process design and optimization in industry and academia. His work significantly reduces simulation times and enhances accuracy, which is critical for scalable and sustainable chemical manufacturing.

📚 Academic Citations and Recognition

Mr. Rękas has published one peer-reviewed journal article and contributed a chapter to a scientific monograph (ISBN: 978-83-67881-52-4). While his citation count currently stands at 1, his work on PINNs and AI-driven chromatography modeling is rapidly gaining attention in specialized research communities.

🛠️ Technical Skills

He possesses extensive programming and modeling skills, with a focus on physics-informed neural networks, graph neural networks (GCNs, GATs), recurrent networks (RNNs, LSTMs, GRUs), XGBoost, and SVMs. His technical toolkit includes Monte Carlo simulations, process modeling frameworks, and custom algorithm development—demonstrating both depth and versatility in computational chemical engineering.

👨‍🏫 Teaching and Mentorship

Though not formally a lecturer, Mr. Rękas actively shares his expertise with peers and junior researchers in machine learning applications and scientific programming, contributing to knowledge exchange in research groups and seminars within his university.

🌟 Legacy and Future Contributions

With a strong foundation in both chemical engineering and artificial intelligence, Mr. Rękas is poised to become a leading figure in data-driven chemical process innovation. His future goals include expanding the use of neural networks in real-time industrial systems, enhancing predictive modeling in chromatography and polymer science, and contributing to the broader adoption of AI in engineering. His work exemplifies a new generation of scientists blending scientific rigor with computational agility.

📖Notable Publications

Application of physics-informed neural networks to predict concentration profiles in gradient liquid chromatography
Authors: Filip Rękas, Marcin Chutkowski, Krzysztof Kaczmarski
Journal: Journal of Chromatography A
Year: 2025

Maria Zednikova | Chemical Engineering | Best Researcher Award