| dc.contributor.author |
Pani, Ajaya Kumar |
|
| dc.date.accessioned |
2021-10-07T12:27:38Z |
|
| dc.date.available |
2021-10-07T12:27:38Z |
|
| dc.date.issued |
2020-02 |
|
| dc.identifier.uri |
https://link.springer.com/chapter/10.1007/978-981-15-0135-7_1 |
|
| dc.identifier.uri |
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/2651 |
|
| dc.description.abstract |
This research work involves the application of multivariate adaptive regression spline (MARS) for estimating back pressure (p) created in a condenser of a coal-fired thermal power plant. MARS employs the plant load (L) and temperature of cooling water (T) as input variables. The output of the MARS is condenser back pressure p^. The designed MARS-based model gives equations for determination of p. Further, the MARS-generated objective function is optimized by randomized search cross validation. Simulation study shows that the accuracy of the reported MARS model is quite satisfactory for the prediction of back pressure. |
en_US |
| dc.language.iso |
en |
en_US |
| dc.publisher |
Springer |
en_US |
| dc.subject |
Chemical Engineering |
en_US |
| dc.subject |
Fault Detection |
en_US |
| dc.subject |
Modelling |
en_US |
| dc.subject |
Multivariate adaptive regression spline |
en_US |
| dc.title |
Back Pressure Monitoring of Power Plant Condenser Using Multiple Adaptive Regression Spline |
en_US |
| dc.type |
Other |
en_US |