The evaluation of the flow properties of biomass powders is essential for the design of handling systems within a thermochemical valorization context. The Discrete Element Method (DEM) is a valuable tool for simulating the bulk behavior of granular materials that has rarely been used for biomass feedstocks. This work focuses on the numerical investigation of the flow of raw and torrefied biomass particles in a loose and dynamic conditioning using a rotating drum. The relevance of DEM parameters calibrated using bulk experiments (angle-of-repose, bulk density, retainment ratio) is tested by comparison with experimental data obtained using a rotating drum system. The calibrated DEM material model considers the elongated, submillimetric and cohesive nature of the biomass powder. Several flowability descriptors (Upper Angle of Stability, size of avalanches, fraction of revolution to trigger events and irregularity of the free surface) are evaluated using both experimental data and DEM simulations. DEM results reproduced well the experimental trends and distinguished between the different cohesive extent of the samples. DEM is therefore a relevant technique for assessing flowability of biomass powders in a non-consolidated dynamic flow. This paves the way for investigating the effects of particle characteristics on bulk flow, which are briefly discussed.

Biomass feeding problems greatly hinder the industrialization of entrained-flow gasification systems for production of 2nd generation biofuels. Appropriate DEM modelling could allow engineers to design solutions that overcome these flow problems. This work shows the application of a DEM calibration framework to produce a realistic, calibrated and efficient material model for lignocellulosic biomass. A coarse (500–710 µm) and a fine (200–315 µm) sieving cut of milled poplar were used in this study. The elongated shape and the cohesive behavior were respectively simulated using a coarse-grained multisphere approach and a cohesive SJKR contact model. Measurements of three physical responses (angle-of-repose, bulk density, a retainment ratio) allowed calibration of the sliding (µ_s) and rolling friction (µ_r) coefficients and the cohesion energy density (CED). Using a statistical analysis, the most influential calibration parameters for each bulk response were identified. A Non-Dominated Sorting Genetic Algorithm was used to solve the calibration multi-objective optimization problem. Several sets of optimal solutions reproduced accurately the three physical responses and the experimental shear responses were closely reproduced by simulations for the population of coarse particles. The DEM calibration framework studied here aims to produce material models useful for assessing flow behavior and equipment interaction for biomass particles.