Effect of particle size and moisture on flow performance of loblolly pine anatomical fractions: Experimental findings and model predictions

The rising energy demand has highlighted biomass as a promising next-generation energy source. However, commercializing biomass-derived energy faces challenges, particularly in handling biomass feedstock. Factors like particle size, shape, moisture content, and surface roughness significantly impact biomass flowability. This study addresses a crucial knowledge gap by examining the effects of particle size and moisture content on the flow behavior and shear properties of different anatomical fractions of loblolly pine ( Pinus taeda ). The bulk shear behavior was examined using a Schulze ring shear tester, while flow performance was tested through gravity-driven flow experiments in a variable wedge-shape hopper. Results were incorporated into empirical and machine learning-based flow prediction models to evaluate their accuracy and limitations. The study found that samples with higher moisture content show higher unconfined yield strength. The critical arching distance increased with particle size, e.g. , from approximately 13 and 33 mm for 2- and 6-mm whole chips, respectively at a 32-degree inclination angle. Conversely, the flow rate decreased for a given hopper opening as particle size increased. For instance, at a 60-mm hopper opening and a 32-degree inclination angle, the mass flow rates for 2- and 6-mm whole chips were 7.83 and 6.42 tonne/h, respectively. The empirical model consistently overpredicted the mass flow rate for all anatomical fractions, while the machine learning model more accurately predicted the central tendency of flow rate but was insensitive to varying tissue proportions. These novel findings provide comprehensive characterization of anatomical fractions, reveal significant combined effects of particle size and moisture content on biomass flow behavior, and demonstrate a better predictive accuracy of a machine learning model, all of which are useful for optimizing material handling strategies and biomass utilization technologies in the industry. • Study examines particle size and moisture effects on loblolly pine residues flow. • Arching distances were 13 and 33 mm for 2 and 6 mm chips, respectively at a 32° IA. • Empirical model overpredicted flow rates, showing limitations for biomass fractions. • ML model predicted flow rates with RMSE of 0.37–0.53 tonne/h.

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Publication Details

Journal
Biomass and Bioenergy
Published
2025-10-26
DOI
https://doi.org/10.1016/j.biombioe.2025.108535
Primary Topic
Plant Water Relations and Carbon Dynamics
Type
article
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article

Effect of particle size and moisture on flow performance of loblolly pine anatomical fractions: Experimental findings and model predictions

Wencheng Jin, Jordan Lee Klinger, Yidong Xia, Nepu Saha et al.
Biomass and Bioenergy
Plant Water Relations and Carbon Dynamics
article

Effect of particle size and moisture on flow performance of loblolly pine anatomical fractions: Experimental findings and model predictions

Wencheng Jin, Jordan Lee Klinger, Yidong Xia, Nepu Saha, Tiasha Bhattacharjee, Noah Berglund
article en

Abstract

The rising energy demand has highlighted biomass as a promising next-generation energy source. However, commercializing biomass-derived energy faces challenges, particularly in handling biomass feedstock. Factors like particle size, shape, moisture content, and surface roughness significantly impact biomass flowability. This study addresses a crucial knowledge gap by examining the effects of particle size and moisture content on the flow behavior and shear properties of different anatomical fractions of loblolly pine ( Pinus taeda ). The bulk shear behavior was examined using a Schulze ring shear tester, while flow performance was tested through gravity-driven flow experiments in a variable wedge-shape hopper. Results were incorporated into empirical and machine learning-based flow prediction models to evaluate their accuracy and limitations. The study found that samples with higher moisture content show higher unconfined yield strength. The critical arching distance increased with particle size, e.g. , from approximately 13 and 33 mm for 2- and 6-mm whole chips, respectively at a 32-degree inclination angle. Conversely, the flow rate decreased for a given hopper opening as particle size increased. For instance, at a 60-mm hopper opening and a 32-degree inclination angle, the mass flow rates for 2- and 6-mm whole chips were 7.83 and 6.42 tonne/h, respectively. The empirical model consistently overpredicted the mass flow rate for all anatomical fractions, while the machine learning model more accurately predicted the central tendency of flow rate but was insensitive to varying tissue proportions. These novel findings provide comprehensive characterization of anatomical fractions, reveal significant combined effects of particle size and moisture content on biomass flow behavior, and demonstrate a better predictive accuracy of a machine learning model, all of which are useful for optimizing material handling strategies and biomass utilization technologies in the industry. • Study examines particle size and moisture effects on loblolly pine residues flow. • Arching distances were 13 and 33 mm for 2 and 6 mm chips, respectively at a 32° IA. • Empirical model overpredicted flow rates, showing limitations for biomass fractions. • ML model predicted flow rates with RMSE of 0.37–0.53 tonne/h.

Biomass and BioenergyVol. 205
Idaho National Laboratory (US), Texas A&M University (US)
Openalex Percentile: Top 71%
Plant Water Relations and Carbon Dynamics
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