List of Figures 2.1 Limitations of photographic imaging technique. (a) out of focused particle on a photographic image plane; (b) aggregated particles on the image plane. 12 2.2 Geometry of DIH 14 2.3 Schematic representation showing step-wise implementation of DH for micro-objects analysis. 2.4 Diffraction pattern of a particle onto the CCD array. 2.5 Maximum spatial frequency resolved by a CCD array. 2.6 Particle with diverging illumination. 3.1 Materials used for particle concentration and turbidity effects. (a) for concentration effect; (b) for turbidity effect. 3.2 Optical setup of the digital inline holographic microscopy. 3.3 Schematic for the calculation of sample volume imaged onto the camera. 3.4 Effect of particle concentration on detection efficiency. 3.5 Reconstructed images for 10 mm depth. (a) 0.01 v/v% particle concentration; (b) 0.05 v/v% particle concentration showing particle-particle interaction; (c) 0.05 v/v% particle concentration showing size reduction due to the noise (particle outlines at both lower and higher concentrations are shown in the inset for comparison). The green region is the area identified as the particle by the image processing algorithm. 3.6 Holograms and reconstructed images at 0.01 v/v% particle concentration. (a) hologram for 5 mm depth; (b) hologram for 10 mm depth; (c) reconstructed image at 25.55 mm for the hologram shown at (a); (d) reconstructed image at 29.95 mm for the hologram shown at (b). 30 3.7 PSDs and particle sizes at different concentrations. (a) PSDs for 10 mm depth; (b) Errorbar plot of the particle sizes for 10 mm depth; (c) PSDs for 5 mm depth; (d) Errorbar plot of the particle sizes for 5 mm depth. Errorbar plots are drawn based on the means and standard deviations of the distributions.
A large number of mangoes are utilized in process industries which produce a large amount of mango kernel that can be utilized effectively as a by-product. Processing and utilization of mango kernel flour is getting more scientific interest day by day. Drying is one of the major operations of flour processing where the moisture loss and heat transfer phenomena across the different portions of the products have to be pre-defined for better drying accuracy. The study was conducted to define a compatible predictive model that represents a 3D expression of heat penetration through the mango kernel slice (MKS) and moisture losses during drying with convective oven dryer using COMSOL Multiphysics with originated boundary conditions, excluding the changes of properties due to varying mangoes species. The model was validated using a convective drying process for varying thicknesses 4, 6 and 8 mm of MKSs having temperature elevated to 60oC and 1 ms-1 air velocity up to 5 hr. The developed model was concurred and correlated well with the experimental data and can be used in describing heat and mass transfer phenomena while drying the mango kernel.
Chemical Engineering Research Bulletin 21(2020) 82-87
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