### Primary Superheater Spray Control Valve Modeling Based on Levenberg-Marquardt Learning Algorithm

^{(*)}

*Corresponding author*

**DOI's assignment:**

*the author of the article can submit here a request for assignment of a DOI number to this resource!*

**Cost of the service: euros 10,00 (for a DOI)**

#### Abstract

Main steam temperature is one of the most important parameters in a coal fired power plant and its characteristics are non-linear and having large inertia with long dead time. Successful control of main steam temperature within ± 2 deg C from its setpoint is the ultimate target for the coal fired power plant operators. Two of the most common main steam temperature circuit are primary superheater spray and secondary superheater spray. This paper present the primary superheater spray control valve modeling based on Levenberg-Marquardt learning algorithm. The neural network algorithm will be trained using actual plant data. The result of the simulation showed that the primary superheater spray control valve modeling based on neural network with Levenberg-Marquardt learning algorithm is able to replicate closely actual plant behavior. Generator output, main steam flow, total spraywater flow and secondary superheater outlet steam temperature are proven to be the main parameters affected the behavior of spray control valve opening in the unit *Copyright © 2014 Praise Worthy Prize - All rights reserved.*

#### Keywords

#### Full Text:

PDF#### References

N.A Mazalan, A. A Malek, Mazlan A. Wahid, M. Mailah, Aminuddin Saat, Mohsin M. Sies, " Main Steam Temperature Modeling Based on Levenberg-Marquardt Learning Algorithm," Applied Mechanics and Materials, Vol. 388 (2013) pp. 307-311.

Ilhan Kocaarslan, Ertugrul Cam, Hasan Tiryaki (2005). A fuzzy Logic Controller Application For Thermal Power Plants. Energy Conversion And Management 47 (2006) pg 442-458.

S. Matsumura, K. Ogata, S. Fujii, H.Shoya and H. Nakamura (1994). Adaptive Control For The Steam Temperature Of Thermal Power Plants. Control Engineering Practice, Vol 2, No. 4, 567-575.

Hui Peng, Toru Ozaki, Yukihiro Toyoda, Keiji Oda (2001). Exponential ARX Model-Based Long-Range Predictive Control Strategy For Power Plants. Control Engineering Practice 9 (2001) pg 1353-1360.

McCulloch, W.S., and Pitts, W., . A Logical Calculus of The Ideas Immanent In Nervous Activity. Bull. Of Mathematical Biophysics, 5 (1943) pg 115-133.

von Neumann, J., The General And Logical Theory Of Automata. Cerebral Mechanisms Of Behavior: The Hixon Symposium, Wiley, New York, NY (1951) pg 1-32.

Hebb, D.O., The Organization Of Behavior. John Wiley, New York, NY, (1949).

Rosenblatt, F. (1958). The Perceptron: A Probabilistic Model For Information Storage And Organization In The Brain. Psychological Review, 65 (1958) pg 386-408.

Rosenblatt, F. (1960). On The Convergence Of Reinforcement Procedures In Simple Perceptrons. Tech. Rep. VG-1196-G-4, Cornell Aeronautical Laboratory, Buffalo, NY, (1960).

Rosenblatt, F. (1960). Perceptron Simulation Experiments. Proceedings Of The Institute Of Radio Engineers, 48 (1960) pg 301-309.

Rosenblatt, F. (1962). Principles Of Neurodynamics. Spartan Books, Washington, (1962).

Ganesan, T., Elamvazuthi, I., Vasant, P., Solving engineering optimization problems with the Karush-Kuhn-Tucker hopfield neural networks, (2011) International Review of Mechanical Engineering (IREME), 5 (7), pp. 1333-1339.

Devi, S., Sivakumar, L., Saravanan, M., An innovative study and binary modeling of thermal power plant using artificial neural network and multiple linear regression, (2013) International Review of Mechanical Engineering (IREME), 7 (6), pp. 1171-1179.

R Rojas (1996). Neural Networks: A Systematic Introduction. Springer-Verlag, Berlin.

K. Levenberg. A Method for the Solution of Certain Non-linear Problems in Least Squares. Quarterly of Applied Mathematics, 2(2):164–168, Jul. 1944.

D.W. Marquardt. An Algorithm for the Least-Squares Estimation of NonlinearParameters. SIAM Journal of Applied Mathematics, 11(2):431–441, Jun. 1963.

K. Madsen, H.B. Nielsen, and O. Tingleff. Methods for Non-Linear Least Squares Problems. Technical University of Denmark, 2004. Lecture notes.

Piasecki, J.S., Zohdy, M.A., Robust hybrid complex motion control using fuzzy logic, inverse dynamic and PID-Q controllers, (2013) International Review of Automatic Control (IREACO), 6 (1), pp. 19-28.

Paulusova, J., Dubravska, M., Neuro-fuzzy predictive control, (2012) International Review of Automatic Control (IREACO), 5 (5), pp. 667-672.

### Refbacks

- There are currently no refbacks.

Please send any question about this web site to info@praiseworthyprize.com**Copyright © 2005-2024**** Praise Worthy Prize**