Modeling of Ion Effect on Fermentation for Bioethanol Production using Artificial Neural Network (ANN)

Fatma Erdem; Dilek Özçelik; Mübeccel Ergun

EN

Modeling of Ion Effect on Fermentation for Bioethanol Production using Artificial Neural Network (ANN)

Abstract

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.

Keywords

References

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Fatma Erdem ^*
0000-0002-6014-6664
Türkiye

Dilek Özçelik
Türkiye

Mübeccel Ergun
Türkiye

Publication Date

December 30, 2020

Submission Date

October 2, 2020

Acceptance Date

December 2, 2020

Published in Issue

Year 2020 Volume: 4 Number: 2

IZ

https://izlik.org/JA26ZW37SR

Cite

RIS / Bibtex

APA

Erdem, F., Özçelik, D., & Ergun, M. (2020). Modeling of Ion Effect on Fermentation for Bioethanol Production using Artificial Neural Network (ANN). Aksaray University Journal of Science and Engineering, 4(2), 148-158. https://izlik.org/JA26ZW37SR