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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/2651
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dc.contributor.authorPani, Ajaya Kumar-
dc.date.accessioned2021-10-07T12:27:38Z-
dc.date.available2021-10-07T12:27:38Z-
dc.date.issued2020-02-
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-981-15-0135-7_1-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/2651-
dc.description.abstractThis 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.isoenen_US
dc.publisherSpringeren_US
dc.subjectChemical Engineeringen_US
dc.subjectFault Detectionen_US
dc.subjectModellingen_US
dc.subjectMultivariate adaptive regression splineen_US
dc.titleBack Pressure Monitoring of Power Plant Condenser Using Multiple Adaptive Regression Splineen_US
dc.typeOtheren_US
Appears in Collections:Department of Chemical Engineering

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