TY - JOUR
T1 - Predictions of biochar production and torrefaction performance from sugarcane bagasse using interpolation and regression analysis
AU - Chen, Wei Hsin
AU - Hsu, Hung Jen
AU - Kumar, Gopalakrishnan
AU - Budzianowski, Wojciech M.
AU - Ong, Hwai Chyuan
N1 - Publisher Copyright:
© 2017 Elsevier Ltd
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2017/12
Y1 - 2017/12
N2 - This study focuses on the biochar formation and torrefaction performance of sugarcane bagasse, and they are predicted using the bilinear interpolation (BLI), inverse distance weighting (IDW) interpolation, and regression analysis. It is found that the biomass torrefied at 275 °C for 60 min or at 300 °C for 30 min or longer is appropriate to produce biochar as alternative fuel to coal with low carbon footprint, but the energy yield from the torrefaction at 300 °C is too low. From the biochar yield, enhancement factor of HHV, and energy yield, the results suggest that the three methods are all feasible for predicting the performance, especially for the enhancement factor. The power parameter of unity in the IDW method provides the best predictions and the error is below 5%. The second order in regression analysis gives a more reasonable approach than the first order, and is recommended for the predictions.
AB - This study focuses on the biochar formation and torrefaction performance of sugarcane bagasse, and they are predicted using the bilinear interpolation (BLI), inverse distance weighting (IDW) interpolation, and regression analysis. It is found that the biomass torrefied at 275 °C for 60 min or at 300 °C for 30 min or longer is appropriate to produce biochar as alternative fuel to coal with low carbon footprint, but the energy yield from the torrefaction at 300 °C is too low. From the biochar yield, enhancement factor of HHV, and energy yield, the results suggest that the three methods are all feasible for predicting the performance, especially for the enhancement factor. The power parameter of unity in the IDW method provides the best predictions and the error is below 5%. The second order in regression analysis gives a more reasonable approach than the first order, and is recommended for the predictions.
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U2 - 10.1016/j.biortech.2017.07.184
DO - 10.1016/j.biortech.2017.07.184
M3 - Article
C2 - 28803060
AN - SCOPUS:85027259101
VL - 246
SP - 12
EP - 19
JO - Bioresource Technology
JF - Bioresource Technology
SN - 0960-8524
ER -