ITALIANO HIGH ACCESSIBILITY
Torna alla Home Page Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria Search in the site...  

European Charter for Researchers      HR EXCELLENCE IN RESEARCH
CREA - Via Po, 14 - 00198 ROMA
P.IVA: 08183101008 - C.F.: 97231970589
Tel: +39 06 478361 - Fax: +39 06 47836320 -
Posta Elettronica Certificata:

CIVIC ACCESS PRESS REVIEW URP JOB OPPORTUNITIES CONTRACTS TRANSPARENT ADMINISTRATION

freccina You are here: Home->Publications->Datasheet


Publication datasheet
Title:
Automated determination of poplar chip size distribution based on combined image and multivariate analyses
Authors:
Febbi, P.; Menesatti, P.; Costa, C.; Pari, L.; Cecchini, M.
Year:
2015
Languages:
ENG, eng
Journal:
Biomass and Bioenergy
Kind of publication:
Cartaceo
Location:
Editor:
Elsevier
Abstract in Italian:
Abstract in English:
The European technical standard EN 14961 on solid biofuels determines the fuel quality classes and specifications for wood chips. Sieving methods are currently used for the determination of particle size distribution. Some authors suggested that image analysis tools could provide methods for a more accurate measure of size integrated with shape. This work for the first time analyzes how image analysis combined with multivariate modeling methods could be used to construct cumulative size distribution curves based on chip mass (or weight). This has been done through a Partial Least Squares Regression model for the weight prediction of poplar chips and Partial Least Squares Discriminant Analysis models for estimation of chips size classification. Images of 7583 poplar chips were analyzed to extract size and shape descriptors (area, major and minor axis lengths, perimeter, eccentricity, equivalent diameter, fractal dimension index, Feret diameters and Fourier descriptors). The weight prediction model showed a high accuracy (r = 0.94). The chip classification based on three size fractions (8-16 mm, 16-45 mm and 45-63 mm), with or without Fourier descriptors, showed accuracies equal to 92.9% of correct classification for both models in the independent test. The combination of image analysis with multivariate modeling approaches allow a better conversion of image analysis results to sieve results using the esteemed weight. The proposed method will allow to standardize processes applicable by biofuels laboratories and machinery certifiers.
Link:
http://dx.doi.org/10.1016/j.biombioe.2014.12.001

AREA RISERVATA  Webmaster:
Logo mySQL Logo PHP