(309a) Recent Progress on the Development of a Virtual Feedstock Preprocessing & Handling Laboratory | AIChE

(309a) Recent Progress on the Development of a Virtual Feedstock Preprocessing & Handling Laboratory


Xia, Y. - Presenter, Idaho National Laboratory
Jin, W., Idaho National Laboratory
Klinger, J., Idaho National Laboratory
Bhattacharjee, T., Idaho National Laboratory
Thompson, V., Idaho National Laboratory
Computational modeling is an emergent approach that is aimed to enhance and expand the quality-by-design (QbD) approach through the development and deployment of experiment informed and/or validated multiphysics models and predictive simulation tools for the design of biomass feedstock preprocessing and material handling operation equipment. This presentation will exhibit the recent technical advancement in the research, development, validation, and deployment of a science-based, advanced computing-powered virtual preprocessing & handling laboratory as an integral part of the Feedstock-Conversion Interface Consortium (FCIC) funded by the U.S. Department of Energy Bioenergy Technologies Office (BETO). The over-arching objective of this virtual laboratory is a predictive numerical replica of the operation units (e.g., grinders, feeders, and hoppers) that can simulate not only the preprocessing and handling equipment in normal working conditions and generate the experiment-validated performance attributes (e.g., particle size distributions, feeding and discharge mass rates), but also expand the possible testing ranges of the material attributes and processing parameters to the extent that is too costly for physical experimentation in terms of material preparation, equipment wear, and power consumption. A number of first-in-its-kind particulate biomass flow simulation techniques based on discrete element method (DEM) models and continuum mechanics finite element method (FEM) and smoothed particle hydrodynamics (SPH) models, as well as their implementations in proprietary and open-source toolkits and application examples, will be presented. Novel DEM models that can predict the fracture mechanics of biomass materials, and that are coupled to computational fluid dynamics (CFD) for predicting biomass fractionation separation will also be introduced. The limitations and best practices of those numerical models regarding their fidelity and computational affordability will be discussed as well.