In this paper, a predictive mono-dimensional (1D) model for Mg(OH)2 precipitation is proposed and its predictive capability is tested. Two different reactor configurations are analyzed and compared, namely a T-mixer and a Y-mixer followed by two consecutive diverging channels and a final coil of constant diameter. Both setups were chosen for their high mixing efficiency. The suspension samples were characterized by Dynamic Light Scattering (DLS), thus obtaining particle size distributions (PSD). The experimental data collected using the T-mixer was used to identify the kinetics parameters set, while the data obtained through the Y-mixer setup was employed to assess the model predictive capability under different fluid dynamics conditions. Computational Fluid Dynamics (CFD) simulations were conducted to characterize the flow fields and the turbulence, which were integrated into the 1D model. Predictions were found to be in good agreement with the experimental data and further improved after introducing a novel correction factor for the aggregation kernel.
Population balance modelling of magnesium hydroxide precipitation: Full validation on different reactor configurations / Raponi, Antonello; Achermann, Ramona; Romano, Salvatore; Trespi, Silvio; Mazzotti, Marco; Cipollina, Andrea; Buffo, Antonio; Vanni, Marco; Marchisio, Daniele. - In: CHEMICAL ENGINEERING JOURNAL. - ISSN 1385-8947. - 477:(2023). [10.1016/j.cej.2023.146540]
Population balance modelling of magnesium hydroxide precipitation: Full validation on different reactor configurations
Antonello Raponi;Antonio Buffo;Marco Vanni;Daniele Marchisio
2023
Abstract
In this paper, a predictive mono-dimensional (1D) model for Mg(OH)2 precipitation is proposed and its predictive capability is tested. Two different reactor configurations are analyzed and compared, namely a T-mixer and a Y-mixer followed by two consecutive diverging channels and a final coil of constant diameter. Both setups were chosen for their high mixing efficiency. The suspension samples were characterized by Dynamic Light Scattering (DLS), thus obtaining particle size distributions (PSD). The experimental data collected using the T-mixer was used to identify the kinetics parameters set, while the data obtained through the Y-mixer setup was employed to assess the model predictive capability under different fluid dynamics conditions. Computational Fluid Dynamics (CFD) simulations were conducted to characterize the flow fields and the turbulence, which were integrated into the 1D model. Predictions were found to be in good agreement with the experimental data and further improved after introducing a novel correction factor for the aggregation kernel.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2983476