TY  - JOUR
N1  - cited By 11
N2  - The conventional experimental methods to determine biomass heating value are laborious and costly. Numerous correlations to estimate biomass' higher heating values have been proposed using proximate analysis. Recently, the utilisation of artificial neural network (ANN) has been extensively applied to predict HHV. However, most studies of ANN to estimate the biomassâ?? HHV only use one algorithm to train a small number of biomass datasets. The specific objective of this study is to predict the HHV of 350 samples of biomass from the proximate analysis by developing an ANN model which was trained with 11 different algorithms. This study fills a gap in the research on how to predict the HHV of biomass using numerous ANN training algorithms utilising sizeable biomass datasets. Results show that the ANN trained with Levenberg-Marquardt gave the highest accuracy. The Levenbergâ??Marquardt algorithm shows the best fit giving the highest R and R2 values and the lowest MAD, MSE, RMSE and MAPE. Compared with previous biomass HHV prediction studies, the ANN model developed in this study provides improved prediction accuracy with higher R2 and lower RMSE. Results from this study have also indicated that the Levenberg-Marquardt should be the first-choice supervised algorithm for feedforward-backpropagation. © 2022
ID  - scholars16131
TI  - Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms
KW  - Calorific value; Forecasting; Heating; Neural networks
KW  -  Artificial neural network modeling; Experimental methods; Heating value; High-accuracy; Higher heating value; Levenberg-Marquardt; Neural networks trainings; Prediction accuracy; Proximate analysis; Training algorithms
KW  -  Biomass
AV  - none
A1  - Veza, I.
A1  - Irianto
A1  - Panchal, H.
A1  - Paristiawan, P.A.
A1  - Idris, M.
A1  - Fattah, I.M.R.
A1  - Putra, N.R.
A1  - Silambarasan, R.
JF  - Results in Engineering
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139393323&doi=10.1016%2fj.rineng.2022.100688&partnerID=40&md5=0dc9be659633a4ee962e7d26b04fe4e6
VL  - 16
Y1  - 2022///
PB  - Elsevier B.V.
SN  - 25901230
ER  -