Using the macromolecular composition to predict process settings that give high pellet durability in ring-die biomass pellet production

FUEL

卷:283

Article

Larsson, Sylvia H.¹  Agar, David A.¹  Rudolfsson, Magnus¹  Perez, Denilson da Silva²  Campargue, Matthieu³  Kalen, Gunnar¹  Thyrel, Mikael¹ 

[1] Swedish Univ Agr Sci, Dept Forest Biomat & Technol, Biomass Technol Ctr, SE-90183 Umea, Sweden

[2] FCBA, 10 Rue Galilee, F-77420 Champs Sur Marne, France

[3] RAGT Energie, Zone Innoprod, Chemin Teuliere, F-81000 Albi, France

关键词: PCA;    OPLS;    Hardwoods;    Softwood;    Straw;    Pelletizing;    Lignocellulose;   

DOI  :  10.1016/j.fuel.2020.119267

来源: Elsevier

【 摘 要 】

This study was performed to investigate if the process settings that give high pellet durability can be modelled from the biomass' macromolecular composition. Process and chemical analysis data was obtained from a previous pilot-scale study of six biomass assortments that by Principal Component Analysis (PCA) was confirmed as representative for their biomass types: hardwood, softwood bark, short rotation coppice (SRC), and straw and energy crops. Orthogonal Partial Least Squares Projections to Latent Structures (OPLS) models were created with the content of macromolecules as factors and the die compression ratio and the feedstock moisture content at which the highest pellet durability was obtained as responses. The models for die compression ratio (R2X = 0.90 and Q2 = 0.58) and feedstock moisture content (R2X = 0.87 and Q2 = 0.60), rendered a prediction error for obtained mechanical durability of approximately +/- 1%-unit, each. Important factors for modelling of the die compression ratio were: soluble lignin (negative), acetyl groups (negative), acetone extractives (positive), and arabinan (positive). For modelling of the feedstock moisture content, Klason lignin (negative), xylan (positive), water-soluble extractives (negative), and mannan (negative), were the most influential. Results obtained in this study indicate that it is possible to predict optimal process conditions in pelletizing based on the macromolecular composition of the raw material. In practice, this would mean a higher raw material flexibility in the pellet factories through drastically reduced risk when introducing new raw materials.

【 授权许可】

Free