The dynamic calibration of an electrical capacitance tomography sensor applied to the fluidized bed drying of pharmaceutical granule

Electrical capacitance tomographic data collected in a lab-scale fluidized bed used for the drying of pharmaceutical granule have been corrected for the influence of moisture on the permittivity of the drying material. The correction is based on a linear least-squares fit to measurements of capacitance in a packed bed of granule at various moisture contents. X-ray tomography has been used to independently verify this correction procedure. The influence of permittivity models and number of iterations used for the reconstruction of tomograms have also been examined. It has been determined that the Bottcher permittivity model performs best at bed moistures above approximately 5 wt% while the parallel model is superior at bed moisture below this value. The reconstruction technique based on iterative linear back-projection utilized for the reconstruction of ECT data required approximately 50 iterations to successfully reproduce the density behaviour seen in the x-ray tomographs. Instability in the reconstruction technique at higher numbers of iterations indicates that a linear least-squares fit does not completely capture the response of the sensor to moisture changes. For future applications, changes in bed voidage associated with the drying of pharmaceuticals must be addressed and included in this calibration procedure in order to implement this calibration technique throughout the drying process. Nevertheless, the viability of this technique for on-line calibration of an ECT sensor applied to the drying process has been demonstrated.