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Klinger, Jordan L

Publications and source records attributed to Klinger, Jordan L.

Assessment of a tomography-informed polyhedral discrete element modelling approach for complex-shaped granular woody biomass in stress consolidation

The design of handling equipment for granular biomass primarily requires experiments. Discrete element models (DEM) can provide designers with detailed insight into the behaviour of granular materials. However, granular biomass comprising complex-shaped particles is difficult to model with DEM. A tomography-informed DEM approach and an exhaustive assessment of the ability of the approach to predict the bulk behaviour of milled pines using experimental data is presented. Nano-CT scan was conducted to obtain 3D particle surface geometries as the basis for particle shape approximation by a polyhedral model and a sphero-polyhedral model. These models were applied in the simulation of a compressibility test. Our parametric study showed that particle Young's modulus and restitution coefficient are the two main properties influencing the simulated bulk behaviour of the DEM particles and that the level of approximating DEM particle shape for real particles is critical for accurately replicating the bulk behaviour of milled pines. The polyhedral model demonstrated better suitability than its sphero-polyhedral counterpart for modelling pine particles. The polyhedral model calibrated in a compressibility test was then applied in the simulations of a friction test without additional parameter tuning. Interlocking was dominant in the shear of bulk pine particles. Remarkably, the polyhedral model predicted the frictional behaviour of the pine particles when compared to the experimental results. The limitations of the model, as well as possible ways for enhancement, are discussed. This work provided novel insights into the suitability of complex-shaped DEM models for granular woody biomass.

Biomass flow↗

Torrefaction of Sorted MSW Pellets for Uniform Biopower Feedstock

The project objective is to use advanced compositional and thermophysical characterization methods from INL to support the evaluation of new production pathways (partnered with MTU) to biopower applications under the guidance of an industrial partner operating the in U.S. power sector (CE). Ultimately, this project aims to create a uniform feedstock from non-recyclable wastes that can be used in bioenergy and biomaterials, which torrefaction and preprocessing strategies are critical to achieving this goal.

09 BIOMASS FUELS↗

Anatomical Fraction segmentation in the biomass bales

The U.S. Department of Energy Bioenergy Technologies Office (BETO) is committed to the development of sustainable, nationwide, commercial biofuel production to displace petroleum-derived fuels, increase domestic energy production, and encourage the creation of a domestic bioenergy and bioproducts industry. Operating a commercial-scale bioenergy operation requires significant technological advancements for determining biomass feedstock quality during the preprocessing stage. Usage of non-food feedstocks- e.g., corn-stover, pine residue, and forest residue - at the biorefineries reduces strain on the food supply chain. But the non-food feedstock heterogeneity- physical size, shape, and chemical composition-poses a significant challenge during milling, conveyance, feeding, and biofuel conversion processes. 3D X-Ray imaging (CT) is capable of distinguishing material features by detecting density and compositional differences. But the inherent complexity introduced during harvesting and bailing makes the reconstruction and interpretation of baled biomass materials from x-ray data time consuming, laborious, and expensive. Feedstocks are low-density materials that produce small contrast differences in the x-ray images. This paper focuses on using the shape and texture properties, using 3D image processing techniques like 3D skeletonization, directional statistics to characterize and extract volumetric content of the different tissue samples in the biomass bales.

09 BIOMASS FUELS↗