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 \( \hat{p} \). 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.
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References
Rajakarunakaran, S., Venkumar, P., Devaraj, D., Surya Prakasa Rao, K.: Artificial neural network approach for fault detection in rotary system. Appl. Soft Comput. 8, 740–748 (2008)
Jain, V., Kishore, P., Kumar, R.A., Pani, A.K.: Inferential sensing of output quality in petroleum refinery using principal component regression and support vector regression. In: 2017 IEEE 7th International on Advance Computing Conference (IACC), pp. 461–465 (2017)
Morey, A., Pradhan, S., Kumar, R.A., Pani, A.K., Vijayan, S., Jain, V., Gupta, A.: Pollutant monitoring in tail gas of sulfur recovery unit with statistical and soft computing models. Chem. Eng. Commun. 206, 69–85 (2019)
Chen, K.-Y., Chen, L.-S., Chen, M.-C., Lee, C.-L.: Using SVM based method for equipment fault detection in a thermal powerplant. Comput. Ind. 62, 42–50 (2011)
Munoz, A., Sanz-Bobi, M.A.: An incipient fault detection system based on the probabilistic radial basis function network: application to the diagnosis of the condenser of a coal power plant. Neurocomputing 23, 177–194 (1998)
Friedman, J.H.: Multivariate adaptive regression splines. Ann. Stat. 19, 1–67 (1991)
Frank, I.E.: Modern nonlinear regression methods. Chemom. Intell. Lab. Syst. 27, 1–19 (1995)
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The authors gratefully acknowledge the data provided by NTPC Solapur (Maharashtra, India) for this study.
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Basu, D., Pani, A.K. (2020). Back Pressure Monitoring of Power Plant Condenser Using Multiple Adaptive Regression Spline. In: Venkata Krishna, P., Obaidat, M. (eds) Emerging Research in Data Engineering Systems and Computer Communications. Advances in Intelligent Systems and Computing, vol 1054. Springer, Singapore. https://doi.org/10.1007/978-981-15-0135-7_1
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DOI: https://doi.org/10.1007/978-981-15-0135-7_1
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