Machine Learning Assisted Cross-Scale Hopper Design for Flowing Biomass Granular Materials

ACS Sustainable Chemistry & Engineering, Год журнала: 2025, Номер 13(16), С. 5838 - 5851

Опубликована: Апрель 16, 2025

The promise of biomass-derived biofuels is often overshadowed by intricate material handling challenges such as hopper clogging and screw feeder jamming. These issues stem from the knowledge gap among particle-scale properties (e.g., particle size), bulk-scale attributes relative density), macro-scale equipment design inclination), flow performance probability clogging). This work combines physical experiments, validated numerical simulations, data augmentation to develop a machine learning-based for flowing granular woody biomass materials. behavior simulated against tests utilizing developed smoothed hydrodynamics (SPH) solver modified hypoplastic model. A comprehensive evaluation performance, including rate, stability, pattern, conducted on an extensive set encompassing various sizes, moisture contents, densities, operating conditions. feed-forward neural network trained optimized with this correlate cross-scale metrics. results reveal promising predictive accuracy seen unseen sets. Further how input affect predicted metrics carried out. indicate that opening width primarily dictates throughput, while density, wall friction, inclination angle, collectively impact stability. Additionally, patterns are predominantly governed angle. Moreover, potential found be exclusively characterized index dedicated combination high dense packing, smooth low angles, small widths substantially elevates risk unstable flows clogging. study serves potent tool milled materials in hoppers all stakeholders biorefineries manufacturing.

Язык: Английский

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On the Fidelity of Computational Models for the Flow of Milled Loblolly Pine: A Benchmark Study on Continuum-Mechanics Models and Discrete-Particle Models

Frontiers in Energy Research, Год журнала: 2022, Номер 10

Опубликована: Июнь 28, 2022

The upstream of bioenergy industry has suffered from unreliable operations granular biomass feedstocks in handling equipment. Computational modeling, including continuum-mechanics models and discrete-particle models, offers insightful understandings predictive capabilities on the flow milled can assist equipment design optimization. This paper presents a benchmark study fidelity continuum discrete modeling approaches for predicting flow. We first introduce constitutive law model contact coarse-grained model, with parameters calibrated against laboratory characterization tests loblolly pine. Three classical material systems (i.e., lab-scale rotating drum, pilot-scale hopper, full-scale inclined plane) are then simulated using two same initial boundary conditions as physical experiments. close agreement numerical predictions experimental measurements hopper mass rate, critical outlet width, stopping thickness plane, dynamic angle repose, clearly indicates that methods capture behavior biomass. qualitative comparison shows outperforms parameterization materials wall friction, large-scale systems, while is more preferred discontinuous at smaller scales. Industry stakeholders use these findings guidance choosing appropriate tools to part optimization biorefineries.

Язык: Английский

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Wedge-Shaped Hopper Design for Milled Woody Biomass Flow

ACS Sustainable Chemistry & Engineering, Год журнала: 2022, Номер 10(50), С. 16803 - 16813

Опубликована: Дек. 5, 2022

The bioenergy industry has been challenged by unstable flow and transport of milled biomass in material handling operations. Handling issues such as hopper clogging auger jamming are attributed to knowledge gaps between existing units designed for bulk solids their suitability with high compressibility. This work investigates various behaviors granular woody wedge-shaped hoppers. Hopper physical experiments numerical simulations conducted study the influence critical attributes processing parameters on pattern, arching, throughput. results show that (1) preferred mass flow, can be achieved controlling material's internal friction angle, inclination, wall friction; (2) governed competing gravity-driven force against resistance from material–wall friction, controlled angle inclination angle; (3) throughput accurately estimated our empirical equation inputs outlet geometry particle-scale bulk-scale attributes. elucidates physics provides guidance industrial equipment design.

Язык: Английский

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Hopper discharge flow dynamics of milled pine and prediction of process upsets using the discrete element method

Powder Technology, Год журнала: 2022, Номер 415, С. 118165 - 118165

Опубликована: Дек. 13, 2022

Язык: Английский

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SPH modeling of biomass granular flow: Theoretical implementation and experimental validation

Powder Technology, Год журнала: 2023, Номер 426, С. 118625 - 118625

Опубликована: Май 10, 2023

Язык: Английский

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