The PSE Model-Based Innovation Prize 2021

Winning paper

Modeling of maize breakage in hammer mills of different scale through a population balance approach by Ivana Cotabarren*, Juliana Piña, Agustina Di Battista of Departamento de Ingeniería Química (DIQ), Universidad Nacional del Sur (UNS), Argentina; Planta Piloto de Ingeniería Química (PLAPIQUI, UNS-CONICET), Argentina; and María Paz Fernández of Planta Piloto de Ingeniería Química (PLAPIQUI, UNS-CONICET), Argentina.

Published in Powder Technology, Elsevier.

Authors

Ivana-Cotabarren Maria Paz Fernandez Agustina Di Battista
Ivana Cotabarren Maria Paz Fernandez Agustina Di Battista
Juliana Piña
Juliana Piña

Abstract

In this contribution a dynamic model of maize breakage in hammer mills of different scales is proposed through a population balance approach. The results of the pilot-scale experiments were used to fit the parameters of the breakage and classification functions, considering the effects of rotor speed and screen opening size. The model was validated with both pilot- and industrial- scales steady-state data, which also included variations in feed rates. The developed model was used to predict the dynamic behavior of the product mass geometric mean diameter and mill hold-up as a function of disturbances in the feed rate and the rotor speed. These results are highly valuable since they indicate the feasibility of using the fitted model to predict with confidence new operating points, study industrial milling performance and optimize hammer mill operation reducing the need to carry out expensive and time-consuming experimental tests.

Biographies

Ivana Cotabarren*

Ivana Cotabarren is Junior Professor at the Chemical Engineering Department of Universidad Nacional del Sur and Adjunt Researcher in the Particle Technology and Engineering group at Planta Piloto de Ingeniería Química (PLAPIQUI, CONICET-UNS, Argentina). She received her Ph.D. degree in 2012 from the same university. She held doctoral and postdoctoral positions at Louisiana State University (USA), Université de Technologie de Compiègne (France), the Massachusetts Institute of Technology (USA) and the Rutgers University (NJ, USA). Her research is focused in the area of modeling and simulation of solids processes, particularly milling and fluidized bed granulation. She is also interested in pharmaceutical product development by additive manufacturing.

Maria Paz Fernandez

Maria Paz Fernandez is a Ph.D. candidate in the Particle Technology and Engineering group at Planta Piloto de Ingeniería Química (PLAPIQUI, CONICET-UNS, Argentina). She received her B.S. degree in chemical engineering in 2015 from the same university. She worked as a Field Engineer in Wheatherford from 2018 to 2020. She focuses primarily on modelling of milling processes at different scales.

Agustina Di Battista

Agustina Di Battista is Food Engineer and received her Ph. D. degree in chemical engineering from the Universidad Nacional del Sur (UNS, Argentina). Actually, she is member of the Professional Support Staff in the Particle Technology and Engineering group at Planta Piloto de Ingeniería Química (PLAPIQUI, CONICET-UNS, Argentina). She is also Teaching Assistant at the Chemical Engineering Department at the Universidad Nacional del Sur. She collaborates in the research of solids processing and design of particulate products.

Juliana Piña

Juliana Piña is professor of Solids Processing at the Chemical Engineering Department at the Universidad Nacional del Sur. She is a senior researcher in the Particle Technology and Engineering group at Planta Piloto de Ingeniería Química (PLAPIQUI, CONICET-UNS; Argentina). She received her B.S. and Ph. D. degrees in chemical engineering from the same university. She held a postdoctoral research fellow position at University of Western Ontario, London, Canada. Her research is focused in the area of modeling and simulation of solids processes and design of particulate products.

* Submitting author

More Information
An impressive multi-scale modeling application, with good use of gPROMS ModelBuilder for population balance modelling
JUDGES' COMMENT

2021 Winner Paper