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Near infrared hyperspectral imaging combined with chemometrics was used to assess the potential for the prediction of the moisture content, specific energy and the feed rate of the feedstock into the pellet die. Samples were produced from a diverse set of agricultural products and wood chips, with a range of moisture contents. Image analysis was also utilised to assess the efficient mixing of biomass...
The potential of near infrared spectroscopy in conjunction with partial least squares regression to predict quality indices of biomass pellet blends was assessed. A diverse range of biomass was used including wood, Miscanthus and herbaceous energy grasses. The moisture, carbon and ash contents and gross calorific value were predicted with a root mean square error of cross validation of 0.73% (R2 = 0...
The real-time prediction of crucial biomass pellet quality parameters such as higher heating value (HHV) and mechanical durability (MD) will allow for more efficient operation of energy production systems. Multiple linear regression (MLR) models were developed to predict HHV and MD from proximate and ultimate analysis of biomass pellets. A diverse range of biomasses from energy crops including pine,...
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