Data-driven mechanical behavior modeling of granular biomass materials

Abstract

Significant equipment downtime, mainly caused by the variable attributes and complex mechanical behavior of granular biomass materials, has challenged the great potential of biofuel generation. Expensive and time-consuming lab experiments are not affordable in fully characterizing material behavior for samples with all possible attributes. In this paper, we present data-driven modeling of the cyclic axial compression and ring-shear stress–strain behavior for granular biomass materials with different moisture contents and mean particle sizes. Laboratory characterization data of milled pine samples, with a mean particle size of 2, 4, and 6 mm and a moisture content of 0%, 20%, and 40%, were employed to train two machine learning (ML) models independently. The sequential model takes a sequence of loading history into Gated Recurrent Units (GRU) and predicts the next step of strain/stress with a feed-forward neural network. In contrast, the incremental model takes only the current state to predict the stiffness/compliance coefficient and compute the stress/strain increment. Both models account for material variations in particle sizes and moisture content. After training, both models demonstrate their high efficiency and accuracy in predicting the mechanical behavior of samples with a diverse set of unseen material properties, augmenting the existing laboratory data set. They also effectively capture the underlying physics, which involves increased compressibility as moisture content rises and a linear relationship between applied compression and shear strength. This effort established the foundation for comprehensive data-driven constitutive modeling tailored for unconventional granular materials.