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

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Abstract

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.

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Abbreviations

AI:

Artificial intelligence

ANN:

Artificial neural network

B:

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

CHP:

Combined heat and power

C:

Concentration (%)

MLP:

Multi layer perceptron

MRE:

Mean relative error (%)

m :

Mass flow rate (t/h)

p :

Pressure (kg/cm2)

stg1:

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

t :

Temperature (°C)

Φ:

Valve opening (°)

1–6:

Steam extractions from turbine for bleeding

B:

Boiler

CN:

Condenser

coal:

Coal

FG:

Flue gas to stack

fw:

Feed water

in:

At the inlet

out:

At the outlet

oxygen:

Oxygen

p :

Pressure

s :

Steam

SA:

Secondary air to the boiler furnace

t :

Temperature

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Acknowledgments

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).

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Authors and Affiliations

  1. Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, Stavanger, Norway

    J. Smrekar & M. Assadi

  2. Department of Mechanical Engineering, Jadavpur University, Kolkata, India

    D. Pandit & Sudipta De

  3. Department of Energy Sciences, Lund University, Lund, Sweden

    M. Fast & M. Assadi

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  1. J. Smrekar
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  2. D. Pandit
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Correspondence to Sudipta De.

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Smrekar, J., Pandit, D., Fast, M. et al. Prediction of power output of a coal-fired power plant by artificial neural network. Neural Comput & Applic 19, 725–740 (2010). https://doi.org/10.1007/s00521-009-0331-6

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