Considering the little understanding of the hydrodynamics of multicomponent particle beds involving
biomass, a detailed investigation has been performed, which combines well-known experimental and
theoretical approaches, relying, respectively, on conventional pressure drop methods and artificial
neural network (ANN) techniques. Specific research tasks related to this research work include: i. to
experimentally investigate by means of visual observation the mixing and segregation behavior of
selected binary mixtures when varying the biomass size and shape as well as the properties (size and
density) of the granular solids in cold flow experiments; ii. to carry out a systematic experimental
investigation on the effect of the biomass weight and volume fractions on the characteristic velocities
(e.g., complete fluidization velocity and minimum slugging velocity) of the investigated binary mixtures
in order to select the critical weight fraction of biomass in the mixtures beyond which the fluidization
properties deteriorate (e.g., channeling, segregation, slugging); iii. to analyze the results obtained in
about 80 cold flow experiments by means of ANN techniques to scrutinize the key factors that influence
the behavior and the characteristic properties of binary mixtures. Experimental results suggest that the
bed components’ density difference prevails over the size difference in determining the
mixing/segregation behavior of binary fluidized bed, whereas the velocities of minimum and complete
fluidization increase with a growing biomass weight fraction in the bed. The training of ANNs
demonstrated good performances for both outputs (Umf and Ucf); in particular, the best predictions have
been obtained for Umf with a MAPE1
<4% (R2=0.98), while for Ucf the best ANN returned a MAPE of
about 7% (R2=0.93). The analysis on the importance of each individual input on ANN predictions
confirmed the importance of particle density of the bed components. Unexpectedly, results showed that
morphological features of biomass have a limited importance on Ucf