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Volume 110
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Alkadhem, A. M., Al Majed, H., Mohamed, H. O., Tsotsas, E., & Castaño, P. (2026). Aggregation kinetics of technical catalysts in a spray-fluidized bed. Particuology, 110, 63-74. https://doi.org/10.1016/j.partic.2026.01.005
Aggregation kinetics of technical catalysts in a spray-fluidized bed
Ali M. Alkadhem, Hani Al Majed, Hend Omar Mohamed, Evangelos Tsotsas, Pedro Castaño *
a Multiscale Reaction Engineering, KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
b Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
c Thermal Process Engineering, Otto von Guericke University, Universitätsplatz 2, 39106, Magdeburg, Germany
d Chemical Engineering Program, Physical Science and Engineering (PSE) Division, King Abdullah University of Science and Technology, Saudi Arabia
10.1016/j.partic.2026.01.005
Volume 110, March 2026, Pages 63-74
Received 1 October 2025, Revised 15 December 2025, Accepted 5 January 2026, Available online 14 January 2026, Version of Record 22 January 2026.
E-mail: pedro.castano@kaust.edu.sa
Highlights

• Catalyst particle growth in a bottom spray–fluidized bed (Wurster) was modeled.

• Two modeling approaches were used: population balance (PBE) and machine learning.

• Single-pathway PBE was insufficient to describe complex early-stage aggregation.

• Two-pathway PBE captured early- and late-stage growth.

• The collision efficiencies provided granulation mechanism insights.


Abstract

Size enlargement control and modeling in fluidized beds are crucial in the pharmaceutical and food industries but remain underdeveloped for technical catalyst formulation and shaping. This work uses different modeling approaches to understand aggregation kinetics: single- and two-pathway population balance equation (PBE) modeling and machine learning. These models are trained on a large dataset of experimental results from a bottom spray-fluidized bed, using realistic technical catalyst conditions and ingredients: ZSM-5 zeolite, bentonite, and alumina. Our optimized model is based on a two-pathway PBE with two distinct collision efficiencies for early- and late-stage growth dynamics across nucleation, seed formation, seed aggregation, and layered growth. With this model, we discuss the granulation and agglomeration dynamics of realistic technical catalysts and study the controlled shaping of several case studies with tailored morphologies (50, 100, and 200 μm pellets) under optimized conditions (i.e., maximum yield within the desired particle range) as validation.

Graphical abstract
Keywords
Granulation; Wurster agglomeration; Technical catalyst; Population balance equation (PBE); Multi-mechanistic PBE
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