| dc.contributor.author |
Tripathy, A. |
|
| dc.contributor.author |
Bagchi, S. |
|
| dc.contributor.author |
Biswal, S.K. |
|
| dc.contributor.author |
Meikap, B.C. |
|
| dc.date.accessioned |
2023-07-28T05:00:21Z |
|
| dc.date.available |
2023-07-28T05:00:21Z |
|
| dc.date.issued |
2020 |
|
| dc.identifier.citation |
Separation Science and Technology, 55(1), 2020: 68-80 |
|
| dc.identifier.issn |
0149-6395 |
|
| dc.identifier.uri |
http://ore.immt.res.in/handle/2018/2711 |
|
| dc.description.abstract |
A predictive model for ?misplacement index? and ?normalized misplacement index? for predicting particle misplacement has been developed using artificial neural network (ANN). The ANN is having three-layer MLP 7-4-2 architecture. The ANN was trained using Broyden?Fletcher?Goldfarb?Shanno algorithm. The performance of the model was judged by sum of squares error function. The correlation coefficient values of 0.9942 and 0.9657 are achieved for training, dataset for prediction of misplacement index and normalized misplacement index, respectively. It was found that the ANN model developed is highly sensitive to the MPSR and least sensitive to static bed height. |
|
| dc.language |
en |
|
| dc.publisher |
Taylor and Francis |
|
| dc.relation.isreferencedby |
SCI |
|
| dc.rights |
Copyright [2020]. All efforts have been made to respect the copyright to the best of our knowledge. Inadvertent omissions, if brought to our notice, stand for correction and withdrawal of document from this repository. |
|
| dc.subject |
Chemical Sciences |
|
| dc.subject |
Engineering |
|
| dc.title |
Prediction of degree of particle misplacement in liquid solid fluidization using artificial neural network |
|
| dc.type |
Journal Article |
|
| dc.affiliation.author |
CSIR-IMMT, Bhubaneswar 751013, Odisha, India |
|