Back Pressure Monitoring of Power Plant Condenser Using Multiple Adaptive Regression Spline

  • Debarchan Basu
  • Ajaya Kumar PaniEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1054)


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 \( \hat{p} \). 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.


Fault detection Modelling Condenser Multivariate adaptive regression spline MARS 



The authors gratefully acknowledge the data provided by NTPC Solapur (Maharashtra, India) for this study.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Chemical EngineeringBirla Institute of Technology and SciencePilaniIndia

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