Flow behavior characterization of biomass Feedstocks

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

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

Criteria for Assessing Sustainability of Lignocellulosic Wastes: Applied to the Cellulose Nanofibril Packaging Production in the UK

Extensive use of petrochemical plastic packaging leads to the greenhouse gas emission and contamination to soil and oceans, posing major threats to the ecosystem. The packaging needs, hence, are shifting to bioplastics with natural degradability. Lignocellulose, the biomass from forest and agriculture, can produce cellulose nanofibrils (CNF), a biodegradable material with acceptable functional properties, that can make packaging among other products. Compared to primary sources, CNF extracted from lignocellulosic wastes reduces the feedstock cost without causing an extension to agriculture and associated emissions. Most of these low value feedstocks go to alternative applications, making their use in CNF packaging competitive. To transfer the waste materials from current practices to the packaging production, it is imperative to assess their sustainability, encompassing environmental and economic impacts along with the feedstock physical and chemical properties. A combined overview of these criteria is absent in the literature. This study consolidates thirteen attributes, delineating sustainability of lignocellulosic wastes for commercial CNF packaging production. These criteria data are gathered for the UK waste streams, and transformed into a quantitative matrix, evaluating the waste feedstock sustainability for CNF packaging production. The presented approach can be adopted to decision scenarios in bioplastics packaging conversion and waste management.

Determination and Validation of Discrete Element Model Parameters of Soybeans with Various Moisture Content for the Discharge Simulation from a Cylindrical Model Silo

This study investigates the physical parameters that affect the flow patterns of soybeans with various moisture content (12% to 60%) at varying orifice sizes (20, 40, and 60 mm) in a cylindrical silo. The flow conditions required to obtain a steady mass flow during discharge were evaluated via experiments and three-dimensional discrete element method (DEM) simulation. The discharged mass flow rates at different flow conditions provided the critical size of the orifice. If the reduced diameter (Dred) of an orifice is >5.59, the flow showed a steady state. Based on the mass flow index (MFI), the flow patterns at 40% and 60% moisture content at 40 and 60 mm orifice sizes, respectively, showed funnel flows. although these flow conditions were satisfied to maintain a steady flow. The maximum wall pressure for the funnel flow showed the location of the interlocking phenomenon where the stagnant zone began during discharging. DEM simulation was validated through the mass profiles using the parameters obtained by the experiments. This study demonstrates that the experimental and analytical results with DEM simulation predict the flow behaviors of soybeans well at various moisture contents. These results are useful for designing silos for continuous food processing.