Modeling of Ion effect on fermentation for bioethanol production using artificial neural network (ANN)
Yükleniyor...
Dosyalar
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Aksaray Üniversitesi
Erişim Hakkı
Attribution-NonCommercial-NoDerivs 3.0 United States
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
Özet
The object of this study is modeling the effect of the interaction of Na, Ca and Mg ions on the ethanol fermentation process by using Artificial Neural Network (ANN). The obtained model results were compared with the optimised results by The Response Surface Method (RSM) and the experimental laboratory data obtained before. Model success criteria was measured via the parameters of Mean Squared Error (MSE) and the correlation coefficient (R). ANN model input variables were the concentration of ions Na, Ca and Mg (Ca: 69-2961 g/L, Na: 209-3621 g/L, Mg: 4-253 g/L) and output was percent ethanol yield. ANN training was done with the Levenberg–Marquardt feed forward algorithm and the data was categorised as 75% training, 15% validation and 15% testing. The maximum epoch value was determined as 14 iterations. R2 values of the system were determined as 99% for education, 99% for validation and 99% for the whole biosorption system. MSE value was 0.0004 for education, 0.00381 for validation and 0.0285 for testing. Different activation functions such as logsig, tansig, purelin and different transfer training algorithm such as trainrp, trainbfg, trainlm and others were tried, tansig and trainlm gave the best results with higher R2 value.
Açıklama
Anahtar Kelimeler
Bioethanol, Artificial Neural Network, Modelling, Fermentation
Kaynak
Aksaray University Journal of Science and Engineering
WoS Q Değeri
Scopus Q Değeri
Cilt
4
Sayı
2
