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Generalized categorical regression model for size reduction of multiple biomass and grinders

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2016 ASABE Annual International Meeting  162461255.(doi:10.13031/aim.20162461255)
Authors:   Manlu Yu, Igathinathane Cannayen
Keywords:   Biomass, Energy, Modeling, Machine vision, Processing.

Abstract. Predicting the energy expended in size reduction process will affect the efficiency of the various conversion processes that biomass feedstocks undergo. Several factors as moisture content, particle size distribution (PSD – using machine vision) of the ground product, and the mechanical shear stress (using universal testing machine) of the stalks are expected to be significant to influence the specific size reduction energy (SRE). Grinding experiments using laboratory grinders and specific SRE measurements using clamp-on power meter were conducted on the selected big bluestem, corn stalks and switchgrass samples at moisture contents of 7%, 14%, and 20% d.b, using a knife mill and a hammer mill (both fitted with 3 different screen sizes). Parameters such as, moisture content, screen size, shear stress, geometric mean length, and uniformity index were selected for the SRE modeling. Best specific SRE models, based on stepwise linear regression of direct five parameters, gave very good (0.85 ≤ AdjR2 ≤ 0.97) while that with interactions (7 to 16 parameters) gave excellent (0.979 ≤ AdjR2 ≤ 0.999) model performance. Grinder specific generalized models had good performance for knife mill (R2 = 0.86) and hammer mill (R2 = 0.76) models, and a similar comparable performance was obtained for the overall generalized model (R2 = 0.83) that can make predictions irrespective of the crop and grinder used. Procedure outlined in the study for the newly developed generalized models can be applied to more devices and feedstocks. Models of SRE will help in better decisions on handling, processing, and utilization of biomass.

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