Sequential fixed-fluidized bed foam granulation (SFFBFG) and drying: Multivariate model development for water content monitoring with near–infrared spectroscopy

Foam granulation (FG) is a non-conventional technique in which a binding solution is sprayed as aqueous foam. The main objectives of this work are, converting a lab-scale fluidized bed dryer into a fluidized bed foam granulator and dryer. Second, developing a partial least squares regression model for monitoring granules water content during this process, and third, bringing forward sufficient evidence of the fluidized bed foam granulation process feasibility. In this work, a mini-Glatt fluidized bed apparatus was used and aqueous foam was produced through an internal mixing nozzle located above the static powder bed, which is fluidized after foam dispensing.

Highlights

• Foam granulation and drying processes are performed in a fluidized bed apparatus.

• An internal mixing nozzle producing foam was used to deliver the binder.

• A protocol for fluidized bed foam granulation was developed and tested.

• Near Infrared spectroscopy was successfully used to monitor water content during the process.

• A Partial Least Square model built by cross-validation successfully predicts water content.

A micro-NIR probe was inserted across the powder container and gather spectral data during granulation and drying. The calibration model was built using the spectral data by Random Subset Cross-Validation and validated externally. Concerning the results, an Rcal2 and RMSECV of 0.945 and 1.67% w/w were obtained respectively. Model validation gave an Rval2 and RMSEP of 0.9 and 2.7% w/w respectively, demonstrating the ability of NIR spectroscopy to predict granules water content. The scanning electron microscopy (SEM) pictures validated the process feasibility.

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Abdoulah Ly, Inès Esma Achouri, Ryan Gosselin, Nicolas Abatzoglou,
Sequential fixed-fluidized bed foam granulation (SFFBFG) and drying: Multivariate model development for water content monitoring with near–infrared spectroscopy,
Chemical Engineering Science, Volume 262, 2022, 118039, ISSN 0009-2509, https://doi.org/10.1016/j.ces.2022.118039.