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Development of artificial neural network model for a coal-fired boiler using real plant data

Smrekar, J.; Assadi, Mohsen LU ; Fast, M.; Kustrin, I. and De, S. (2009) In Energy 34(2). p.144-152
Abstract
Development of artificial neural network (ANN) models using real plant data for the prediction of fresh steam properties from a brown coal-fired boiler of a Slovenian power plant is reported. Input parameters for this prediction were selected from a large number of available parameters. Initial selection was made on a basis of expert knowledge and previous experience. However, the final set of input parameters was optimized with a compromise between smaller number of parameters and higher level of accuracy through sensitivity analysis. Data for training were selected carefully from the available real plant data. Two models were developed, one including mass flow rate of coal and the other including belt conveyor speed as one of the input... (More)
Development of artificial neural network (ANN) models using real plant data for the prediction of fresh steam properties from a brown coal-fired boiler of a Slovenian power plant is reported. Input parameters for this prediction were selected from a large number of available parameters. Initial selection was made on a basis of expert knowledge and previous experience. However, the final set of input parameters was optimized with a compromise between smaller number of parameters and higher level of accuracy through sensitivity analysis. Data for training were selected carefully from the available real plant data. Two models were developed, one including mass flow rate of coal and the other including belt conveyor speed as one of the input parameters. The rest of the input parameters are identical for both models. Both models show good accuracy in prediction of real data not used for their training. Thus both of them are proved suitable for use in real life, either on-line or off-line. Better model out of these two may be decided on a case-to-case basis depending on the objective of their use. The objective of these studies was to examine the feasibility of ANN modeling for coal-based power or combined heat and power (CHP) plants. (c) 2008 Elsevier Ltd. All rights reserved. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Steam properties, Real plant data, ANN modeling, Coal-fired boiler, prediction
in
Energy
volume
34
issue
2
pages
144 - 152
publisher
Elsevier
external identifiers
  • wos:000264588900003
  • scopus:60449106698
ISSN
1873-6785
DOI
10.1016/j.energy.2008.10.010
language
English
LU publication?
yes
id
d0eddfd7-2fef-4d6e-a509-247e262ff0be (old id 1401615)
date added to LUP
2009-06-15 10:53:21
date last changed
2017-11-12 03:31:20
@article{d0eddfd7-2fef-4d6e-a509-247e262ff0be,
  abstract     = {Development of artificial neural network (ANN) models using real plant data for the prediction of fresh steam properties from a brown coal-fired boiler of a Slovenian power plant is reported. Input parameters for this prediction were selected from a large number of available parameters. Initial selection was made on a basis of expert knowledge and previous experience. However, the final set of input parameters was optimized with a compromise between smaller number of parameters and higher level of accuracy through sensitivity analysis. Data for training were selected carefully from the available real plant data. Two models were developed, one including mass flow rate of coal and the other including belt conveyor speed as one of the input parameters. The rest of the input parameters are identical for both models. Both models show good accuracy in prediction of real data not used for their training. Thus both of them are proved suitable for use in real life, either on-line or off-line. Better model out of these two may be decided on a case-to-case basis depending on the objective of their use. The objective of these studies was to examine the feasibility of ANN modeling for coal-based power or combined heat and power (CHP) plants. (c) 2008 Elsevier Ltd. All rights reserved.},
  author       = {Smrekar, J. and Assadi, Mohsen and Fast, M. and Kustrin, I. and De, S.},
  issn         = {1873-6785},
  keyword      = {Steam properties,Real plant data,ANN modeling,Coal-fired boiler,prediction},
  language     = {eng},
  number       = {2},
  pages        = {144--152},
  publisher    = {Elsevier},
  series       = {Energy},
  title        = {Development of artificial neural network model for a coal-fired boiler using real plant data},
  url          = {http://dx.doi.org/10.1016/j.energy.2008.10.010},
  volume       = {34},
  year         = {2009},
}