Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms

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 appl...

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Main Authors: Veza, I., Irianto, Panchal, H., Paristiawan, P.A., Idris, M., Fattah, I.M.R., Putra, N.R., Silambarasan, R.
Format: Article
Published: Elsevier B.V. 2022
Online Access:http://scholars.utp.edu.my/id/eprint/34047/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139393323&doi=10.1016%2fj.rineng.2022.100688&partnerID=40&md5=0dc9be659633a4ee962e7d26b04fe4e6
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spelling oai:scholars.utp.edu.my:340472022-12-28T07:54:20Z http://scholars.utp.edu.my/id/eprint/34047/ Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms Veza, I. Irianto Panchal, H. Paristiawan, P.A. Idris, M. Fattah, I.M.R. Putra, N.R. Silambarasan, R. 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 biomass� 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 Levenberg�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 Elsevier B.V. 2022 Article NonPeerReviewed Veza, I. and Irianto and Panchal, H. and Paristiawan, P.A. and Idris, M. and Fattah, I.M.R. and Putra, N.R. and Silambarasan, R. (2022) Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms. Results in Engineering, 16. ISSN 25901230 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139393323&doi=10.1016%2fj.rineng.2022.100688&partnerID=40&md5=0dc9be659633a4ee962e7d26b04fe4e6 10.1016/j.rineng.2022.100688 10.1016/j.rineng.2022.100688 10.1016/j.rineng.2022.100688
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description 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 biomass� 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 Levenberg�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
format Article
author Veza, I.
Irianto
Panchal, H.
Paristiawan, P.A.
Idris, M.
Fattah, I.M.R.
Putra, N.R.
Silambarasan, R.
spellingShingle Veza, I.
Irianto
Panchal, H.
Paristiawan, P.A.
Idris, M.
Fattah, I.M.R.
Putra, N.R.
Silambarasan, R.
Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms
author_facet Veza, I.
Irianto
Panchal, H.
Paristiawan, P.A.
Idris, M.
Fattah, I.M.R.
Putra, N.R.
Silambarasan, R.
author_sort Veza, I.
title Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms
title_short Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms
title_full Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms
title_fullStr Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms
title_full_unstemmed Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms
title_sort improved prediction accuracy of biomass heating value using proximate analysis with various ann training algorithms
publisher Elsevier B.V.
publishDate 2022
url http://scholars.utp.edu.my/id/eprint/34047/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139393323&doi=10.1016%2fj.rineng.2022.100688&partnerID=40&md5=0dc9be659633a4ee962e7d26b04fe4e6
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score 13.209306