In light of 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 includes: i. to experimentally investigate by means of visual observation the mixing and segregation behavior of selected binary mixtures by 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 (i.e., complete fluidization velocities 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., channelling, segregation, slugging); iii. to analyze the results obtained in about 80 cold flow experiments by means of ANN techniques in order 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 increased with the increase of the biomass weight fraction in the bed. The training of ANNs demonstrated good performances for both outputs (Umf and Ucf), in particular, best predictions have been obtained for Umf with a MAPE < 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 single input on ANN predictions confirms the importance of particle density of the bed components. However, unexpected results showed that morphological features of biomass have a limited importance on Ucf.

Binary mixtures of biomass and inert components in fluidized beds: Experimental and neural network exploration

Brachi P.
Methodology
;
Miccio M.
Writing – Review & Editing
;
2023-01-01

Abstract

In light of 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 includes: i. to experimentally investigate by means of visual observation the mixing and segregation behavior of selected binary mixtures by 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 (i.e., complete fluidization velocities 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., channelling, segregation, slugging); iii. to analyze the results obtained in about 80 cold flow experiments by means of ANN techniques in order 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 increased with the increase of the biomass weight fraction in the bed. The training of ANNs demonstrated good performances for both outputs (Umf and Ucf), in particular, best predictions have been obtained for Umf with a MAPE < 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 single input on ANN predictions confirms the importance of particle density of the bed components. However, unexpected results showed that morphological features of biomass have a limited importance on Ucf.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4827371
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