| dc.creator |
Yasar, S. |
|
| dc.creator |
Cengiz, M. |
|
| dc.date |
2010-03-31T21:00:00Z |
|
| dc.date.accessioned |
2020-10-06T11:37:45Z |
|
| dc.date.available |
2020-10-06T11:37:45Z |
|
| dc.identifier |
e4f3565d-6c50-4de2-abd6-126aaecd50eb |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/e4f3565d-6c50-4de2-abd6-126aaecd50eb/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/74638 |
|
| dc.description |
The present study describes a simple and efficient artificial neural network (ANN) modelling to predict the hot water and total acidities of lignocellulosic materials including wood and agricultural residues from the hot water and alkali solubilities and pH values. The performance of the proposed model trained by Levenberg-Marquardt algorithm was evaluated by analysis of the predicted as well as the experimental data. The prediction error of 1.31% and the correlation R(2) values varying between 0.9983 and 0.9940 confirmed that three layered ANN model with 3 hidden neurons produced more accurate results. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Artificial Neural Network Estimation of Lignocellulosic Material Acidity |
|
| dc.type |
info:eu-repo/semantics/article |
|