Biographie
Antonello Raponi is an Assistant Professor (Maître de conférences) in Chemical Engineering at Mines Saint-Étienne, Institut Mines-Télécom, France. He is affiliated with the Georges Friedel Laboratory (UMR 5307 CNRS), within the SPIN Center and the Process Engineering for Environment and Georesources (PEG) Department.
He received his Ph.D. in Chemical Engineering from Politecnico di Torino, Italy, in 2024. Before joining Mines Saint-Étienne, he conducted postdoctoral research at Politecnico di Torino and Purdue University (USA), where he worked with Prof. Zoltan Nagy’s research group on crystallization process modeling and artificial intelligence.
His research focuses on the development of predictive and computationally efficient frameworks for complex multiphase processes, combining Computational Fluid Dynamics (CFD), Population Balance Models (PBM), and Artificial Intelligence (AI). Particular emphasis is placed on crystallization, reactive precipitation, multiscale modeling, parameter identification, and data-driven inverse design.
His broader research objective is to bridge fundamental process understanding and industrial applications through hybrid physics-based and data-driven modeling, supporting the development of more efficient, scalable, and sustainable chemical processes.
Activités de recherche
My research focuses on the development of hybrid computational frameworks combining first-principles modeling, multiscale simulations, and Artificial Intelligence for the prediction, optimization, and inverse design of chemical processes.
The main research directions include:
- Computational Fluid Dynamics (CFD): Modeling of complex multiphase flows, mixing phenomena, and reactive systems.
- Population Balance Modeling (PBM): Prediction of particle nucleation, growth, aggregation, and particle size distributions in crystallization and precipitation processes.
- Artificial Intelligence and Machine Learning: Deep learning, surrogate modeling, kinetic parameter identification, uncertainty quantification, and data-driven inverse design.
- Hybrid and Reduced-Order Modeling: Development of AI-assisted compartmental models and computationally efficient alternatives to high-fidelity CFD–PBM simulations.
- Sustainable Chemical Processes: Applications to reactive crystallization, mineral carbonation, lithium recovery, magnesium-based materials, and battery precursor synthesis.
Enseignement
My teaching activities at Mines Saint-Étienne cover fundamental and advanced topics in Chemical and Process Engineering, with particular emphasis on fluid dynamics, thermodynamics, crystallization, and computational modeling.
Main Teaching Areas
- Fluid Dynamics and Transport Phenomena: Fluid mechanics, momentum transport, and practical Computational Fluid Dynamics (CFD) applications.
- Industrial Crystallization: Fundamentals of crystallization, nucleation and growth kinetics, particle size distributions, and population balance modeling.
- Process Design: Chemical process analysis and design, with applications to crystallization and separation processes.
- Thermodynamics and Physical Chemistry: Fundamental principles and applications to chemical engineering systems.
- Numerical Methods and Scientific Computing: Introduction to OpenFOAM, numerical simulations, and computational approaches to process engineering.
Formation
2020–2024 | Ph.D. in Chemical Engineering
Politecnico di Torino, Italy
Doctoral research on the modeling, simulation, and optimization of reactive precipitation processes, with particular emphasis on magnesium hydroxide production, CFD–PBM modeling, and kinetic parameter identification.
2018–2020 | M.Sc. in Chemical Engineering
Politecnico di Torino, Italy
2015–2018 | B.Sc. in Chemical Engineering
Sapienza University of Rome, Italy
Principaux ouvrages
Selected Peer-Reviewed Publications
- Raponi, A., & Marchisio, D. (2026). Data-driven inverse design for magnesium hydroxide precipitation: Coupling CFD–PBM with deep learning to control hexagonal platelets morphology formation and particle size distribution. Chemical Engineering Journal, 539, 176546. https://doi.org/10.1016/j.cej.2026.176546
- Raponi, A., & Marchisio, D. (2026). Multivariate optimization and inverse design of multiphase reactive systems via deep learning and 3D CFD–PBM simulations. Chemical Engineering Journal, 531, 173908. https://doi.org/10.1016/j.cej.2026.173908
- Raponi, A., & Nagy, Z. K. (2026). CompArt: Hybrid Artificial Intelligence–Compartmental modeling for transferable scale-up of multiphase reactors. Chemical Engineering Journal Advances, 27, 101365. https://doi.org/10.1016/j.ceja.2026.101365
- Raponi, A., & Nagy, Z. K. (2026). CompArt: Next generation COMPartmental models powered by ARTificial intelligence. Separation and Purification Technology, 394, 137623. https://doi.org/10.1016/j.seppur.2026.137623
- Marchisio, D., Querio, A., & Raponi, A. (2026). Computational flow models for crystallization processes. Current Opinion in Chemical Engineering, 52, 101246. https://doi.org/10.1016/j.coche.2026.101246
- Raponi, A., & Marchisio, D. (2024). Deep learning for kinetics parameters identification: A novel approach for multi-variate optimization. Chemical Engineering Journal, 489, 151149. https://doi.org/10.1016/j.cej.2024.151149
- Raponi, A., Achermann, R., et al. (2023). Population balance modelling of magnesium hydroxide precipitation: Full validation on different reactor configurations. Chemical Engineering Journal, 477, 146540. https://doi.org/10.1016/j.cej.2023.146540
- Raponi, A., Romano, S., et al. (2023). Computational modeling of magnesium hydroxide precipitation and kinetics parameters identification. Crystal Growth & Design, 23(7), 4748–4759.
Publications HAL
- Baggie W. Nyande, Antonello Raponi. A review of coupled computational fluid dynamics–population balance model and machine learning frameworks for crystallization processes. Chemical Engineering Science, 2027, 338, Part D, pp.125072. ⟨10.1016/j.ces.2026.125072⟩. ⟨emse-05752961⟩
- Kensaku Matsunami, Yash Barhate, Antonello Raponi, Varun Sundarkumar, Guanghui Zhu, et al.. Integrating machine learning and CFD-derived hydrodynamics for predictive scale-up of combined cooling and antisolvent crystallization. Chemical Engineering Journal, 2026, 548, pp.181334. ⟨10.1016/j.cej.2026.181334⟩. ⟨emse-05753736⟩
- Antonello Raponi, Zoltan K. Nagy. CompArt: Hybrid Artificial Intelligence–Compartmental modeling for transferable scale-up of multiphase reactors. Chemical Engineering Journal Advances, 2026, 27, pp.101365. ⟨10.1016/j.ceja.2026.101365⟩. ⟨emse-05700877⟩
- Antonello Raponi, Zoltan K. Nagy. CompArt: Next generation COMPartmental models powered by ARTificial intelligence. Separation and Purification Technology, 2026, 394 - Part 3, pp.137623. ⟨10.1016/j.seppur.2026.137623⟩. ⟨emse-05562960⟩
- Antonello Raponi, Daniele Marchisio. Data-driven inverse design for magnesium hydroxide precipitation: Coupling CFD–PBM with deep learning to control hexagonal platelets morphology formation and particle size distribution. Chemical Engineering Journal, 2026, 539, pp.176546. ⟨10.1016/j.cej.2026.176546⟩. ⟨emse-05610364⟩
- Daniele Marchisio, Andrea Querio, Antonello Raponi. Computational flow models for crystallization processes. Current Opinion in Chemical Engineering, 2026, 52, pp.101246. ⟨10.1016/j.coche.2026.101246⟩. ⟨emse-05564903⟩
- Antonello Raponi, Daniele Marchisio. Multivariate optimization and inverse design of multiphase reactive systems via deep learning and 3D CFD–PBM simulations. Chemical Engineering Journal, 2026, 531, pp.173908. ⟨10.1016/j.cej.2026.173908⟩. ⟨emse-05523779⟩
- Yung-Shun Kang, Antonello Raponi, Zoltan K. Nagy. Capturing Thermal and Hydrodynamic Heterogeneity in Polymorphic Crystallization Scale-Up via Physics-Inspired Multicompartmental Population Balance Models. Industrial and engineering chemistry research, 2026, ⟨10.1021/acs.iecr.6c02482⟩. ⟨emse-05835638⟩
Documents récupérés de l'archive ouverte HAL 
Carrière
2025–Present | Assistant Professor (Maître de conférences)
Mines Saint-Étienne, Institut Mines-Télécom, France
Georges Friedel Laboratory (UMR 5307 CNRS), SPIN Center, Process Engineering for Environment and Georesources (PEG) Department.
Postdoctoral Research Associate | Purdue University, USA
Davidson School of Chemical Engineering — Crystallization and Particle Technology Systems Engineering (CryPT-Sys), Prof. Zoltan Nagy’s research group.
Research on hybrid AI-assisted process modeling, crystallization, compartmental modeling, and computationally efficient approaches to process scale-up.
May–July 2024 | Postdoctoral Researcher
Politecnico di Torino, Italy
Research on the numerical modeling and optimization of chemical processes, with particular emphasis on reactive precipitation and CFD–PBM simulations.