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This study provides a simple model for biomass char yield obtained under conditions relevant for suspension firing. Using the multivariate data analysis methods, principal component analysis (PCA) and partial least-squares regression (PLS regression), an equation is presented, which predicts the char yield for wood and herbaceous biomass. The model parameters are heating rate (0.1–12 ·103 K/s), average particle size (0.13–0.93 mm), maximum temperature (873–1673 K), potassium content (from 0.02 wt %db and upward), and char yield (1–15 wt %daf). The model is developed based on wood biomass data and subsequently expanded to include straw and other herbaceous biomass. It is validated against experimental data from the literature, and in general, it exhibits the same characteristics. Independent data sets of wood are predicted with an average error (RMSEP) of 0.9 wt %point daf and straw with an RMSEP = 0.9 wt %daf for the model, when a slope/intercept correction is applied or RMSEP = 1.1 wt %daf otherwise. To include herbaceous biomass, the model introduces a potassium cut off level at 0.53 wt %db, because the catalytic effect of potassium on the devolatilization process levels off above this concentration. The model consists of one equation, making implementation into CFD and devolatilization models possible without adding to the computational costs.
Parameters important for char yield prediction for biomass devolatilized under high heating rates determined by chemometrics7 Sep 2018
Activity: Talks and presentations › Conference presentations