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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Elsevier B.V.
2022
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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 |
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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 |
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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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