Neural Computing and Applications

, Volume 19, Issue 5, pp 725–740 | Cite as

Prediction of power output of a coal-fired power plant by artificial neural network

  • J. Smrekar
  • D. Pandit
  • M. Fast
  • M. Assadi
  • Sudipta DeEmail author
Original Article


Accurate modeling of thermal power plant is very useful as well as difficult. Conventional simulation programs based on heat and mass balances represent plant processes with mathematical equations. These are good for understanding the processes but usually complicated and at times limited with large number of parameters needed. On the other hand, artificial neural network (ANN) models could be developed using real plant data, which are already measured and stored. These models are fast in response and easy to be updated with new plant data. Usually, in ANN modeling, energy systems can also be simulated with fewer numbers of parameters compared to mathematical ones. Step-by-step method of the ANN model development of a coal-fired power plant for its base line operation is discussed in this paper. The ultimate objective of the work was to predict power output from a coal-fired plant by using the least number of controllable parameters as inputs. The paper describes two ANN models, one for boiler and one for turbine, which are eventually integrated into a single ANN model representing the real power plant. The two models are connected through main steam properties, which are the predicted parameters from boiler ANN model. Detailed procedure of ANN model development has been discussed along with the expected prediction accuracies and validation of models with real plant data. The interpolation and extrapolation capability of ANN models for the plant has also been studied, and observed results are reported.


Power plant Coal-fired boiler Steam turbine ANN model Real plant data Extrapolation Interpolation 

List of symbols


Artificial intelligence


Artificial neural network


Bleed steam data, pressure (kg/cm2) and temperature (°C)


Combined heat and power


Concentration (%)


Multi layer perceptron


Mean relative error (%)


Mass flow rate (t/h)


Pressure (kg/cm2)


Group of parameters (m coal, ΦB,out, p fw, t fw) optimized after first stage sensitivity analysis


Temperature (°C)

Greek symbol


Valve opening (°)



Steam extractions from turbine for bleeding








Flue gas to stack


Feed water


At the inlet


At the outlet








Secondary air to the boiler furnace





Professor Mohsen Assadi, Department of Energy Sciences, Lund University, Sweden and Dr. Sudipta De, Department of Mechanical Engineering, Jadavpur University, Kolkata, India gratefully acknowledge the financial support for this work from the Swedish Research Council (Vetenskapsrådet) under Swedish Research Link Program (Grant No.: 348-2006-5349).


  1. 1.
    Bhambre K, Mitra SK, Gaitunde UN (2007) Modeling of a coal-fired natural circulation boiler. Trans ASME J Energy Res Tech 129:159–167CrossRefGoogle Scholar
  2. 2.
    Liu C, Liu J, Niu Y, Liang W (2001) Nonlinear boiler model of 300 MW power unit for system dynamic performance studies. IEEE Trans Ind Electron 2:1296–1300Google Scholar
  3. 3.
    Astrom KJ, Bell RD (2000) Drum boiler dynamics. Automatica 33:363–378CrossRefMathSciNetGoogle Scholar
  4. 4.
    Adam EJ, Marchetti JL (1999) Dynamic simulation of large boilers with natural circulation. Comput Chem Eng 23:1031–1040CrossRefGoogle Scholar
  5. 5.
    Lu S (1999) Dynamic modeling and simulation of power plant systems. Proc Inst Mech Eng Part A 213:7–22CrossRefGoogle Scholar
  6. 6.
    Lo KL, Song ZM, Marchand E, Pinkerton A (1990) Development of a static-state estimator for a power station boiler: part I. mathematical model. Electr Power Syst Res 18:175–179CrossRefGoogle Scholar
  7. 7.
    Tysso A (1981) Modeling and parameters estimation of a ship boiler. Automatica 17:157–166zbMATHCrossRefGoogle Scholar
  8. 8.
    Usoro PB (1977) Modeling and simulation of a drum boiler-turbine power plant under emergency state control. Master’s thesis, Massachusetts Institute of TechnologyGoogle Scholar
  9. 9.
    Kwan HW, Anderson JH (1970) A mathematical model of a 200 MW boiler. Int J Control 12(6):977–998CrossRefGoogle Scholar
  10. 10.
    Chein KL, Ergin EI, Ling C, Lee A (1958) Dynamic analysis of a boiler. Trans ASME 80:1809–1819Google Scholar
  11. 11.
    Eklund K (1971) Linear drum boiler–turbine models. Ph.D. thesis TFRT-1001, Lund Institute of Technology, SwedenGoogle Scholar
  12. 12.
    Brandl P, Reichert K, Vogt W (1975) Simulation of turbogenerators on steady state load. Brown Bovery Rev 9:444–449Google Scholar
  13. 13.
    Girija Shankar PV (1977) Simulation model of a nuclear reactor turbine. Nucl Eng Design 44(2):269–277CrossRefGoogle Scholar
  14. 14.
    Bell RD, Åström KJ (1987) Dynamic models for boiler–turbine-alternator units: data logs and parameter estimation for a 160 MW unit. Report TFRT-3192, Lund Institute of Technology, SwedenGoogle Scholar
  15. 15.
    Anglart H, Andersson S, Jadrny R (1992) BWR steam line and turbine model with multiple piping capability. Nucl Eng Design 137(1):1–10CrossRefGoogle Scholar
  16. 16.
    Schobeiri MT, Chakka P (2002) Prediction of turbine blade heat transfer and aerodynamics using a new unsteady boundary layer transition model. Int J Heat Mass Transf 45(4):815–829zbMATHCrossRefGoogle Scholar
  17. 17.
    Bassel WS, Gomes AV (2002) A metastable wet steam turbine stage model. Nucl Eng Design 216:113–119CrossRefGoogle Scholar
  18. 18.
    Liu JJ, Cui YQ, Jiang HD (2003) Investigation of flow in a steam turbine exhaust hood with/without turbine exit conditions simulated. J Eng Gas Turbines Power 125(1):292–299CrossRefGoogle Scholar
  19. 19.
    Lampart P, Yershov S (2003) Direct constrained computational fluid dynamics based optimization of three-dimensional blading for the exit stage of a large power steam turbine. J Eng Gas Turbines Power 125(1):385–390CrossRefGoogle Scholar
  20. 20.
    Perez RE, Lopez MAA, Vazquez EM, Littlewood EC, Cruz CA (2004) A comprehensive finite-element model of a turbine-generator infinite-busbar system. Finite Elements Anal Design 40:485–509CrossRefGoogle Scholar
  21. 21.
    Boccaletti C, Cerri G, Seyedan B (2001) A neural network simulator of a gas turbine with a waste heat recovery section. Trans ASME J Eng Gas Turb Power 123:371–376CrossRefGoogle Scholar
  22. 22.
    Mathioudakis K, Stamatis A, Tsalavoutas A, Aretakis N (2001) Performance analysis of industrial gas turbines for engine condition monitoring. Proc Inst Mech Eng Part A J Power Energy 215:173–184CrossRefGoogle Scholar
  23. 23.
    Mathioudakis K, Stamatis A, Bonataki E (2002) Allocating the causes of performance deterioration in combined cycle gas turbine plants. J Eng Gas Turbines Power 124:256–262CrossRefGoogle Scholar
  24. 24.
    Kalogirou SA (2000) Applications of artificial neural-networks for energy systems. Appl Energy 67:17–35CrossRefGoogle Scholar
  25. 25.
    Kalogirou SA (2003) Artificial intelligence for the modeling and control of combustion processes: a review. Prog Energy Combust Sci 29(6):515–566CrossRefGoogle Scholar
  26. 26.
    Kesgin U, Heperkan H (2005) Simulation of thermodynamic systems using soft computing techniques. Int J Energy Res 29:581–611CrossRefGoogle Scholar
  27. 27.
    Cerri G, Khatry DS (1998) A neural network approach in thermodynamic process evaluation. In: Proceedings of the international conference on engineering application of neural network (EANN-98), Gibraltar (GB), Paper No. 98172Google Scholar
  28. 28.
    Mesbahi E (2000) Artificial neural networks for fault diagnosis, modeling and control of diesel engines. Ph.D. thesis, University of Newcastle Upon Tyne, UKGoogle Scholar
  29. 29.
    De S, Kaiadi M, Fast M, Assadi M (2007) Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden. Energy 32:2099–2109CrossRefGoogle Scholar
  30. 30.
    Fast M, Assadi M, Smrekar J (2008) Application of artificial neural network to the condition monitoring and diagnosis of a CHP plant. In: Proceedings of the 21th international conference on efficiency, cost, optimization, simulation and environmental impact of energy systems, Poland, pp 981–988Google Scholar
  31. 31.
    Irwin G, Brown M, Hogg B, Swidenbank E (1995) Neural network modelling of a 200 MW boiler system. IEE Proc -Control Theory Appl 142(6):529–536zbMATHCrossRefGoogle Scholar
  32. 32.
    Lu S, Hogg BW (2000) Dynamic and nonlinear modelling of power plant by physical principles and neural networks. Electr Power Energy Syst 22:67–78CrossRefGoogle Scholar
  33. 33.
    Chu JZ, Shieh SS, Jang SS, Chien CI, Wan HP, Ko HH (2003) Constrained optimization of combustion in a simulated coal-fired boiler using artificial neural network model and information analysis. Fuel 82:693–703CrossRefGoogle Scholar
  34. 34.
    Feretti G, Piroddi L (2001) Estimation of NOX emissions in thermal power plants using neural networks. J Eng Gas Turbines Power 123: 465–471Google Scholar
  35. 35.
    Smrekar J, Assadi M, Fast M, Kuštrin I, De S (2009) Development of artificial neural network model for a coal-fired boiler using real plant data. Energy (Oxford) 34:144–152 [Print ed.]Google Scholar
  36. 36.
    Fast M, Assadi M, De S (2009) Development and multi-utility of an ANN model for an industrial gas turbine. Appl Energy 86:9–17CrossRefGoogle Scholar
  37. 37.
    Fast M (2005) Artificial neural networks for gas turbine modeling and sensor validation. Master Thesis, Lund UniversityGoogle Scholar
  38. 38.
    Haykin S (1999) Neural networks, a comprehensive foundation, 2nd edn. ISBN No. 0–13-273350–1. Prentice Hall, Inc, New Jersey, USAGoogle Scholar
  39. 39.
    Principe JC, Euliano NR, Lefebvre WC (1999) Neural and adaptive systems: fundamentals through simulations. Wiley, New YorkGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • J. Smrekar
    • 1
  • D. Pandit
    • 2
  • M. Fast
    • 3
  • M. Assadi
    • 1
    • 3
  • Sudipta De
    • 2
    Email author
  1. 1.Department of Mechanical and Structural Engineering and Materials ScienceUniversity of StavangerStavangerNorway
  2. 2.Department of Mechanical EngineeringJadavpur UniversityKolkataIndia
  3. 3.Department of Energy SciencesLund UniversityLundSweden

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