Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks

The amount of bottom ash formed in a pulverized coal-fired power plant was predicted by artificial neural network modeling using one-year operating data of the plant and the properties of the coals processed. The model output was defined as the ratio of amount of bottom ash produced to amount of coal burned (Bottom ash/Coal burned). The input parameters were the moisture contents, ash contents and lower heating values of the coals. The total 653 data were divided into two groups for the training (90% of the data) and the testing (10% of the data) of the network. A three-layer, feed-forward type network architecture with back-propagation learning was used in the modeling study. The activation function was sigmoid function. The best prediction performance was obtained for a one hidden layer network with 29 neurons. The learning rate and the tolerance value were 0.2 and 0.05, respectively. R2 (coefficient of determination) values between the actual (Bottom ash/Coal burned) ratios and the model predictions were 0.988 for the training set and 0.984 for the testing set. In addition, the sensitivity analysis indicated that the ash content of coals was the most effective parameter for the prediction of the ratio of bottom ash to coal burned. © 2012 Elsevier Ltd.

Dergi Adı Energy
Dergi Cilt Bilgisi 45
Dergi Sayısı 1
Sayfalar 882 - 887
Yayın Yılı 2012
Eser Adı
[dc.title]
Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks
Yazar
[dc.contributor.author]
Bekat, Tuğçe
Yazar
[dc.contributor.author]
Erdoğan, Muharrem
Yazar
[dc.contributor.author]
İnal, Fikret
Yazar
[dc.contributor.author]
Genç, Ayten
Yayın Yılı
[dc.date.issued]
2012
Yayıncı
[dc.publisher]
Elsevier Ltd
Yayın Türü
[dc.type]
article
Özet
[dc.description.abstract]
The amount of bottom ash formed in a pulverized coal-fired power plant was predicted by artificial neural network modeling using one-year operating data of the plant and the properties of the coals processed. The model output was defined as the ratio of amount of bottom ash produced to amount of coal burned (Bottom ash/Coal burned). The input parameters were the moisture contents, ash contents and lower heating values of the coals. The total 653 data were divided into two groups for the training (90% of the data) and the testing (10% of the data) of the network. A three-layer, feed-forward type network architecture with back-propagation learning was used in the modeling study. The activation function was sigmoid function. The best prediction performance was obtained for a one hidden layer network with 29 neurons. The learning rate and the tolerance value were 0.2 and 0.05, respectively. R2 (coefficient of determination) values between the actual (Bottom ash/Coal burned) ratios and the model predictions were 0.988 for the training set and 0.984 for the testing set. In addition, the sensitivity analysis indicated that the ash content of coals was the most effective parameter for the prediction of the ratio of bottom ash to coal burned. © 2012 Elsevier Ltd.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Dili
[dc.language.iso]
eng
Konu Başlıkları
[dc.subject]
Artificial neural networks
Konu Başlıkları
[dc.subject]
Bottom ash
Konu Başlıkları
[dc.subject]
Pulverized coal-fired power plant
Künye
[dc.identifier.citation]
Bekat, T., Erdogan, M., Inal, F. ve Genc, A. (2012). Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks. Energy, The 24th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy, ECOS 2011, 45(1), 882-887. doi:10.1016/j.energy.2012.06.075
Haklar
[dc.rights]
info:eu-repo/semantics/openAccess
ISSN
[dc.identifier.issn]
0360-5442
İlk Sayfa Sayısı
[dc.identifier.startpage]
882
Son Sayfa Sayısı
[dc.identifier.endpage]
887
Dergi Adı
[dc.relation.journal]
Energy
Dergi Sayısı
[dc.identifier.issue]
1
Dergi Cilt Bilgisi
[dc.identifier.volume]
45
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1016/j.energy.2012.06.075
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/7115
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network amount bottom (Bottom prediction burned learning ash/Coal burned) contents training testing values function modeling hidden obtained performance parameter neurons tolerance Elsevier ratios addition sensitivity analysis indicated predictions content actual between determination) effective (coefficient respectively
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