LAUSR

Abstract Multivariate analysis was used to study the influence of the biomass characteristics on the gasification process. Ten lignocellulosic biomass samples (almond shells –AS–, chestnut sawdust –CHE–, torrefied chestnut sawdust –CHET–, cocoa shells –CS–, grape pomace –GP–, olive stones –OS–, pine cone leafs –PCL–, pine sawdust –PIN–, torrefied pine sawdust –PINT–, and pine kernel shells –PKS–) were gasified in a bubbling fluidized bed gasifier under an air-steam atmosphere. Statistical analysis was applied to the variables that described the results of the gasification process, i.e., gas concentration, gas production (moles), calorific value of the product gas, energy density, and cold gas efficiency, together with the main biomass properties, such as those derived from the elemental and proximate analyses, the higher heating value (HHV), the particle density, and the elemental composition of the ashes. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied to the data of biomass properties and gasification parameters in order to elucidate which feedstock features had a more determinant influence on the gasification process. Both HCA and PCA revealed a clear separation of the biomass samples into two main groups on the basis of the gasification results. The results indicated that PKS, PCL, PINT, OS and PIN biomasses were characterized by high production of combustible gases, such as CO and CH4, high conversion and cold gas efficiency during gasification. This indicated that the most important biomass properties for promoting the gas production, calorific value of the product gas, gasification conversion and energy efficiency were the C and H contents and the HHV of the biomass. However, biomasses CS and GP were mainly characterized by high H2 concentration and H2/CO molar ratio in the gas product, which was mainly related to the higher H/O ratio and K2O ash content of the biomass. The H2 concentration in the product gas was negatively related to the O and VM contents of the biomass. Therefore, it can be concluded that the use of multivariate statistical techniques for analyzing gasification data facilitated to draw valuable conclusions about the influence of the biomass properties on the gasification results.