Prediction of power output of a coalfired power plant by artificial neural network
(2010) In Neural Computing & Applications 19(5). p.725740 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. Stepbystep method of the ANN model development of a coalfired 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. Stepbystep method of the ANN model development of a coalfired 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 coalfired 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)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/1630317
 author
 Smrekar, J.; Pandit, D.; Fast, Magnus ^{LU} ; Assadi, Mohsen ^{LU} and De, Sudipta
 organization
 publishing date
 2010
 type
 Contribution to journal
 publication status
 published
 subject
 keywords
 Real plant, ANN model, Steam turbine, Power plant, Coalfired boiler, Interpolation, data, Extrapolation
 in
 Neural Computing & Applications
 volume
 19
 issue
 5
 pages
 725  740
 publisher
 Springer
 external identifiers

 wos:000278837800008
 scopus:77953916617
 ISSN
 09410643
 DOI
 10.1007/s0052100903316
 language
 English
 LU publication?
 yes
 id
 f02f8ea08b74400bbba12f2f5bcbc691 (old id 1630317)
 date added to LUP
 20100722 14:58:05
 date last changed
 20190220 07:01:58
@article{f02f8ea08b74400bbba12f2f5bcbc691, 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. Stepbystep method of the ANN model development of a coalfired 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 coalfired 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 = {09410643}, keyword = {Real plant,ANN model,Steam turbine,Power plant,Coalfired boiler,Interpolation,data,Extrapolation}, language = {eng}, number = {5}, pages = {725740}, publisher = {Springer}, series = {Neural Computing & Applications}, title = {Prediction of power output of a coalfired power plant by artificial neural network}, url = {http://dx.doi.org/10.1007/s0052100903316}, volume = {19}, year = {2010}, }