Advancing biorefinery understanding particle by particle
Published on: November 24, 2020
Idaho National Laboratory (INL), one of 17 U.S. Department of Energy national laboratories, performs research in support of DOE’s mission to “discover the solutions to power and secure America’s future.”, Designated as the DOE’s leading center for nuclear research, INL also receives program funding for other areas of interest, including funding from the DOE Bioenergy Technology Office (BETO). INL and BETO share a mission to advance fundamental understanding of biomass materials. The objective is to leverage research and development of feedstock and machinery to develop optimal biofuel technology — ultimately reducing the cost of biorefining and making it a viable renewable alternative to traditional fossil energy. INL’s research is gathered in a database that can be utilized by industry to facilitate their own prospective designs.
As a computational R&D scientist at INL, Yidong Xia’s role is to examine discrete element modeling (DEM) technology to learn how it can benefit the biomass industry. DEM has a natural ability to capture the complex geometries and deformation of individual particles, as it resolves the underlying particle−particle and particle−wall interactions in biomass granular flow.
Potential feedstock material includes agricultural and forest residuals, such as pine and corn stover. Unfortunately, these ingredients can produce flowability issues — like arching, clogging, jamming — in feedstock-handling equipment. The materials exhibit a wide range of characteristics: highly flexible, fracture-prone, coarse-grained, elongated, and/or convex/concave/steep-verticed surface.
INL’s project began with an exhaustive review of literature related to biorefinery applications. One of the engineering challenges is that biomass materials have complex, irregular particle shapes, and, therefore, flow pattern in refinery equipment is difficult to accurately predict. Identifying particle shape and size is important, yet each particle is unique: A simple specification of length, width and thickness is not sufficient. Proper modeling requires using complex shapes to better represent particle surface details — which adds to the computational cost. Though non-composite, non-spherical and spherical particle models easily calculate contact detection/force and scale best in computing, they are not complex enough to provide accurate results for biomass materials.
Literature defined and identified a number of models that, when used under the right conditions, can be applied to feedstock. Composite-sphere, custom-polyhedron, sphero-polyhedron, sphero-cylinder, shell and composite-polygon models are suitable to simulate biomass particle-flow behavior depending on their varying strengths/weaknesses.
Biomass DEM modeling is an emerging area, so it is not yet possible to identify the most suitable models for all known biomass particle systems. Defining the characteristics and potential strengths/weaknesses (including those related to computational cost) of the different models constitutes a step in the right direction.
Using this summary as a baseline, INL’s work continues in evaluating which DEM models mimic biomass flow behavior better than others. The next step involves hands-on simulation experience, using Rocky DEM for a variety of assessment studies. For example, Yidong will need to determine that Rocky’s flexible-fiber model is suitable for analyzing corn stover tissue and use it to perform flow and shear tests that can offer insight to engineering companies that design biomass handling equipment. Because it’s critical that his study analyze complex particle shapes inherent to feedstock, Rocky DEM is the right tool, with its state-of-the-art polyhedral modeling (including fiber shapes), breakage-prediction capabilities, and GPU processing support that enables complex-scenario simulation.
Yidong’s research will continue for another few years, yet already the software sector is already starting to recognize his success. One possibility is that we’ll apply more-sophisticated DEM shapes and models. The scale-bridge between particle and scale might be the most challenging part in multiscale approaches, so it deserves significant research efforts. And advancing computational speed to crunch thousands of complex-shaped particles will always be part of the objectives.
Ideally, Yidong will develop a set of parameters that are critical to achieving accurate simulation results, which biorefinery pioneers can leverage to advance the industry.
By the Rocky DEM Team.
Computational R&D Scientist, U.S. DOE Idaho National Laboratory, PhD
Yidong Xia is a Computational R&D scientist for the U.S. Department of Energy Idaho National Laboratory’s Energy and Environment Science & Technology Directorate. Xia obtained his PhD degree in aerospace engineering with a minor in general mathematics from North Carolina State University. At Idaho National Laboratory, his Xia’s research and leadership experience lie in environmental subsurface science, nuclear energy, fossil energy, geothermal energy, bioenergy, and high-performance scientific computing. Xia’s technical expertise spans several fields, including computational fluid dynamics and heat transfer, computational particle mechanics, computational chemistry, and nuclear material and thermal hydraulics.