Mikael Thyrel

High quality biofuel pellet production from pre-compacted low density raw materials

In this study, pre-compaction was evaluated as a method to enhance stable reed canary grass pellet production. An experimental design of the factors raw material moisture content, steam addition, raw material bulk density, and die temperature was used to find production conditions for high quality pellets by multiple linear regression modelling of responses. Response variables being modelled were variability of pelletizer current (as a measurement of uneven production), pellet bulk density, and pellet durability. By pre-compacting the raw material from a bulk density of 150 kg/m3 to 270kg/m3, continuous production could be obtained at minimum raw material moisture content of 13.8%. Bulk density and durability were both highly correlated to raw material moisture content, but showed different optima. Multiple response optimization was used to target process settings for production of high quality reed canary grass pellets with bulk density >650kg/m3 and durability >97.5%.

Near infrared image analysis for online identification and separation of wood chips with elevated levels of extractives.

Forest-based biorefinery feedstocks are usually broken up into wood chips prior to any form of processing. These wood chips have a complex and highly variable composition, although they may look identical to an inexperienced observer. Some chips have high contents of valuable extractives. Therefore, it would be desirable to separate such chips that are rich in extractives. Various fractions of pine and spruce wood were used to acquire near infrared (1000–2498 nm) hyperspectral images in order to explore the usefulness of multivariate image analysis for detection and separation purposes. Multivariate modelling by image principal component analysis detected large variations in extractive content among wood chips of different biomass types, for example, sapwood, heartwood and knotwood. The extractive parts could be classified in the images and their content could be reasonably well predicted. Partial least squares (PLS) regression models could be made between collected spectra and measured extractive contents. These worked better for milled and homogenised bulk samples than for average image spectra. Regression coefficients showed that the C–H bonds in the spectra were responsible for the validity of the models. The average image PLS models could be used to make prediction images showing the location of the regions with high extractive content in knotwood. The results indicate that extremely rapid spectral-based fractionation could be used to separate tailored biomass streams of wood chips.