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

Smrekar, J.; Pandit, D.; Fast, Magnus LU ; Assadi, Mohsen LU and De, Sudipta (2010) In Neural Computing & Applications 19(5). p.725-740
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... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Real plant, ANN model, Steam turbine, Power plant, Coal-fired boiler, Interpolation, data, Extrapolation
in
Neural Computing & Applications
volume
19
issue
5
pages
725 - 740
publisher
Springer
external identifiers
  • wos:000278837800008
  • scopus:77953916617
ISSN
0941-0643
DOI
10.1007/s00521-009-0331-6
language
English
LU publication?
yes
id
f02f8ea0-8b74-400b-bba1-2f2f5bcbc691 (old id 1630317)
date added to LUP
2010-07-22 14:58:05
date last changed
2018-07-15 04:00:23
@article{f02f8ea0-8b74-400b-bba1-2f2f5bcbc691,
  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.},
  author       = {Smrekar, J. and Pandit, D. and Fast, Magnus and Assadi, Mohsen and De, Sudipta},
  issn         = {0941-0643},
  keyword      = {Real plant,ANN model,Steam turbine,Power plant,Coal-fired boiler,Interpolation,data,Extrapolation},
  language     = {eng},
  number       = {5},
  pages        = {725--740},
  publisher    = {Springer},
  series       = {Neural Computing & Applications},
  title        = {Prediction of power output of a coal-fired power plant by artificial neural network},
  url          = {http://dx.doi.org/10.1007/s00521-009-0331-6},
  volume       = {19},
  year         = {2010},
}