Antonello Raponi
Materials Science · Purdue University West Lafayette
Publications
30
Citations
203
Est. group size
~1
Recurring co-author estimate
Active years
6
Publishing since 2021
Antonello Raponi works on modeling and simulation of industrial crystallization and precipitation processes, particularly the formation of magnesium hydroxide particles. His research combines computational fluid dynamics (CFD), population balance models (mathematical tools for tracking particle size and shape distributions), and deep learning to predict and control particle size, shape, and manufacturing scale-up of chemical reactors.
Publication output was minimal before 2021 but has grown substantially since 2022, with a peak in 2023 and another surge of publications projected for 2026, indicating an increasingly active recent research output.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Multivariate optimization and inverse design of multiphase reactive systems via deep learning and 3D CFD–PBM simulations
Chemical Engineering Journal · 2026
- Computational flow models for crystallization processes
Current Opinion in Chemical Engineering · 2026
- CompArt: Next generation COMPartmental models powered by ARTificial intelligence
SSRN Electronic Journal · 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
SSRN Electronic Journal · 2026
- CompArt: Next generation COMPartmental models powered by ARTificial intelligence
Separation and Purification Technology · 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 · 2026
- CompArt: Hybrid Artificial Intelligence–Compartmental modelling for transferable scale-up of multiphase reactors
SSRN Electronic Journal · 2026
- A Physics-Inspired Multi-compartmental (PIMC) Population Balance Framework for Size and Aspect Ratio Control in Crystallization Scale-Up
SSRN Electronic Journal · 2026
- CompArt: Hybrid Artificial Intelligence–Compartmental modelling for transferable scale-up of multiphase reactors
Chemical Engineering Journal Advances · 2026
- Computational Fluid Dynamics and Population Balance Model Enhances the Smart Manufacturing and Performance Optimization of an Innovative Precipitation Reactor
Processes · 2025
- CompArt: Next-Generation Compartmental Models for Complex Systems Powered by Artificial Intelligence
Systems and Control Transactions · 2025
- Multivariate Optimization and Inverse Design of Digital Twins for Multiphase Reactive Systems via Deep Learning and 3D CFD–PBM Simulations
SSRN Electronic Journal · 2025
- Deep learning for kinetics parameters identification: A novel approach for multi-variate optimization
Chemical Engineering Journal · 2024
- Deep Learning for Kinetics Parameters Identification: A Novel Approach for Multi-Objective Optimization
SSRN Electronic Journal · 2024
- Mixing Influence on Magnesium Hydroxide Precipitation: Computational Modelling and Kinetics Identification
2024
- SSRN Electronic Journal×7
- Zenodo (CERN European Organization for Nuclear Research)×6
- Chemical Engineering Journal×4
- Crystal Growth & Design×2
- Powder Technology×1
- Yangyuan Ji
Materials Science · Purdue University West Lafayette
- Mark Russell
Engineering · Purdue University West Lafayette
- Álvaro García‐Romero
Materials Science · Indiana University
- Adharsh Raghavan
Materials Science · Purdue University West Lafayette
- Andrew W. Mitchell
Materials Science · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
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