(106b) Modeling and Performance Assessment of Rotating Packed Beds for the Production of Precipitated Carbonate Nanoparticles

The global warming due to anthropogenic CO₂ emissions is a pressing issue that attracts significant research efforts for the development of efficient CO₂ capture and utilization (CCU) technologies. The exploitation of CO₂ as a raw material of added value will be key to the wide industrial adoption of CCU solutions. Among the numerous different utilization options that are being investigated, the production of carbonate nanoparticles such as precipitated calcium carbonate (PCC) is a very appealing option for carbon leakage industries such as cement, lime, pulp and paper and steel. PCC nanoparticles have a broad range of applications in building materials, cement, plastics, paper, pharmaceuticals, dyes etc.¹. Their commercial value ranges on average within $1150 - 2350 / ton, depending on their size. The nanoparticle market is very dynamic and is expected to need about 40 million tons/yr by 2024, with a total value of $10.5 Billion². The vast majority of PCC production worldwide takes place in conventional CSTR or bubble reactors³. In such systems the production requires chemical additives for size control, whereas the reactors are not appropriate for large capacities as they require large residence times in larger than 50 m³ volumes³. Rotating Packed Beds (RPBs)⁴ have emerged as a promising alternative for PCC production⁵. Centrifugal forces are exploited in a spinning packing material to intensify mixing and mass transfer, and to significantly reduce the residence time and reactor volume, among other advantages. Despite the high industrial potential, RPB-based PCC production is currently very limited⁵. Few details are available in published literature regarding operating parameters and conditions, while limited experimental information includes lab-scale results on precipitation and particle size distribution (PSD)⁶.

The use of model-based approaches is very important for the scaling-up of RPBs, but there are currently no models for RPB-based PCC production, that can combine the mixing and precipitation phenomena with macroscopic, RPB operating parameters for performance assessment. In the production of nanoparticles, the prediction of the particle size distribution of the product is equally important to the assessment of the operating efficiency of the process. The majority of published works focus on barium sulfate production with computational fluid dynamics (CFD) simulations on various types of mixing equipment. For example, Gradl et al.⁷ investigated the precipitation of barium sulfate in a T-mixer by coupling the particle population dynamics with CFD, using direct numerical simulations. Gavi et al.⁸ performed CFD simulations of barium sulfate precipitation in confined impinging jet reactors, using the Reynolds averaged Navier–Stokes approach. Steyer et al.⁹ studied the applicability of different activity coefficient models to barium sulfate precipitation. The work of Pan et al.¹⁰ investigated the carbonation of calcium ions in an RPB, but focused on gas-liquid mass transfer without considering precipitation. The work of Xiang et al.¹¹ is the only one that investigated precipitation in combination with a mixing model in an RPB, but it refers to barium sulfate.

In the current work, a robust model that combines micro- and macro-mixing with population balances in an RPB used for PCC production is developed. The model captures nucleation, molecular growth and aggregation in the representation of the precipitation process, whereas the engulfment model for micro-mixing is coupled with the population balance equation to investigate mixing effects during scale-up. The proposed model essentially associates the PSD that is attained under different operating conditions in the RPB, with key process performance indicators such as the rotor speed, the energy consumption and so forth. The model predictions are validated against experimental data from published literature, considering various packing types and similar rotor speeds, and flowrates. The simulated results presented in Table 1 indicate a very good match with the experimental results. Using the proposed model, a parametric analysis of the process is performed. Among other parameters, the sensitivity of the nucleation rate to variations of the interfacial energy constant and the supersaturation are investigated. The results indicate that the variation of the interfacial energy constant causes a change of the nucleation rate by more than 3 orders of magnitude. Furthermore, a wide range of operating conditions are explored in order to determine the particle sizes and distribution that can be attained within a given range of energy dissipation. According to the present analysis, the mean particle size decreases with increase of the energy dissipation rate and rotor speed respectively, and is found to vary between 5.5 – 7.5 nm for cases of fast mixing. Overall, the proposed model is capable of fast and efficient prediction of the particle size and distribution.

Acknowledgements

This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call Research−Create− Innovate, Project T1EDK-02472.

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