Araştırma Makalesi
PDF Zotero Mendeley EndNote BibTex Kaynak Göster

Yıl 2020, Cilt 4, Sayı 2, 148 - 158, 30.12.2020

Öz

Kaynakça

  • [1] P. Nematizade, B. Ghobadian, G. Najafi, F. Ommi, A. Abbaszadeh, Investigation some of the properties of fossil fuels and liquid biofuels blends for utilize at SI engines, IJSR, 2:4 (2013) 92-103.
  • [2] A. Tesfaw, F. Assefa, Current trends in bioethanol production by Saccharomyces cerevisiae: substrate, inhibitor reduction, growth variables, coculture, and immobilization, Int. Sch. Res. Notices (2014) 1-11.
  • [3] M. Mohamed, A.A. Eman, W. Elgammal, Roba, G. Ghitas. Comparative study on modeling by neural networks and response surface methodology for better prediction and optimization of fermentation parameters: Application on thermo-alkaline lipase production by Nocardiopsis sp. strain NRC/WN5, Biocatal. and Agricultural Biotechnol. 25 (2020).
  • [4] M. C. Anumaka, J.K. Offor, C.A Nwabueze, P.I. Obi, Techno-economic Feasibility of Bioethanol Production for Fossil Fuel-Fired Generating Plant in Nigeria, AJEST 1(4) (2014) 121-127.
  • [5] U. Saarela, K. Leiviska, E. Juuso, Modeling of a Fed-Batch Fermentation Process, Technical Report A No. 21, University of Oulu, Finland. (2003) 2-3.
  • [6] F. Erdem, S. cerevisiae ile Remazol Sarı (RR) Giderimine Yapay Sinir Ağı (YSA) Yaklaşımı, Uludağ University Journal of The Faculty of Engineering 24(2) (2019) 289-298.
  • [7] E. Betiku, E. Taiwo Abiola, Modeling and optimization of bioethanol production from breadfruit starch hydrolyzate vis-à-vis response surface methodology and artificial neural network, Renewable Energy (2015) 87-94.
  • [8] H. Ahmadian-Moghadam, F. Elegado, R. Banzuela Navye, Prediction of Ethanol Concentration in Biofuel Production Using Artificial Neural Networks AJMO 1(3) (2013) 31-35.
  • [9] R. Abd, K. Norliza, T. Noorhisham, Z. Yaakob, S. Gauri, Estimation of Bioethanol Production from Jatropha curcas Using Neural Network Key Engineering Materials (2013) 943–947.
  • [10] Y. Nagata, H.C. Khim, Optimization of a fermentation medium using neural networks and genetic algorithms Biotechnol Lett, 25 (2013) 1837-1842.
  • [11] H. Zentou, Z. Zainal Abidin, R. Yunus, D.R. Awang Biak , M. Zouanti, A. Hassani, Modeling of Molasses Fermentation for Bioethanol Production: A Comparative Investigation of Monod and Andrews Models Accuracy Assessment Biomolecules, 9(8) (2019) 308.
  • [12] G.M. Walker, Metals in yeast fermentation processes, Adv. Appl. Microbiol., 54 (2004) 197-229.
  • [13] R.C. Nabais, I. Sá-Correia, C.A.Viegas, J.M. Novais, Influence of Calcium Ion on Ethanol Tolerance of Saccharomyces bayanus and Alcoholic Fermentation by Yeasts, AEM 54(10) (1988) 2439-2446.
  • [14] Md. Fakruddin, Md.A. Quayum, M.M. Ahmed, N. Choudhury, Analysis of Key Factors Affecting Ethanol Production by Saccharomyces cerevisiae IFST-072011, Biotechnology 11 (2012) 248-252.
  • [15] Q. Xu, M. Wu, J. Hu, M. Gao, Effects of nitrogen sources and metal ions on ethanol fermentation with cadmium-containing medium J. Basic Microbiol. 56(1) (2016) 26-35.
  • [16] M.A. Palukurty, N.K. Telgana, H.M. Sundar, R. Bora, Screening and optimization of metal ions to enhance ethanol production using statistical experimental designs, Afr. J. Microbiol. Res. (2) (2008) 87-94.
  • [17] D. Soyuduru, M. Ergun, A. Tosun, Application of a statistical technique to investigate calcium, sodium magnesium ion effect in yeast fermentation Appl. Biochem. Biotechnol. 152:2 (2009) 326-333.
  • [18] U. Greeshma, S. Annalakshmi, Artificial Neural Network (Research paper on basics of ANN), International Journal of Scientific & J. Eng. Res. 6(4) (2015) 110-115.
  • [19] K. Shiruru, An Introduction to Artificial Neural Network, International Journal of Advance Research and Innovative Ideas in Education 1:27 (2016) 30.
  • [20] S.N. Mahmoud, A.E.M. Medhat, A.E.S. Hamdy, G. El Kobrosy, Application of artificial neural network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT, Alex. Eng. J. 51(1) (2012) 37-43.
  • [21] C. Cranganu, H. Luchian, M. Breaban, 2015. Artificial intelligent approaches in petroleum geosciences, (Berlin: Springer 2015) pp. 149.
  • [22] A. Sebayang, H. Masjuki, H. Ong, S.C. Chyuan, S. Dharma, A. Silitonga, F. Kusumo, J. Milano, Optimization of bioethanol production from sorghum grains using artificial neural networks integrated with ant colony Ind. Crops. Prod. 97 (2017) 146-155.
  • [23] B. Genç, H. Tunç, Optimal training and test sets design for machine learning, Turk. J. Elec. Eng. & Comp. Sci. 27 (2019) 1534-1545.
  • [24] N. Bekkari, A. Zeddouri, Using artificial neural network for predicting and controlling the effluent chemical oxygen demand in wastewater treatment plant, Manag. Environ. Qual. 30:3 (2019) 593-608.
  • [25] C.K. Chui, X. Li, Approximation by ridge functions and neural networks with one hidden layer, J. Approx. Theory 70 (1992) 131-141.
  • [26] K. Smith-Miles, J.N Gupta, Neural networks in business: techniques and applications for the operations researcher Comput. Oper. Res. 27 (2000) 1023-1044.
  • [27] D.R. Baughman, Y.A. Liu, Neural Networks in Bioprocessing and Chemical Engineering (Academic Press, 1995) pp. 21-109.
  • [28] A.C. Awang, B.S. Mohammed, M.R. Mustafa, Mix design proportion for strength prediction of rubbercrete using artificial neural network. Engineering Challenges for Sustainable Future (1st edition, London CRS Press, 2016).
  • [29] O.A. Adeoti, P.A. Osanaiye, Effect of Training Algorithms on the Performance of ANN for Pattern Recognition of Bivariate Process. Int. J. Comput. Appl. 69 (2013) 8-12.
  • [30] A. Okewale, O.A. Adesina, M.O. Oloko-oba, Comparative Study of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) on Optimization of Ethanol Production from Sawdust, Int. J. Eng. Res. Afr. 30 (2017) 125-133.
  • [31] M. Esfahanian, M. Nikzad, G.D. Najafpour, A.A. Ghoreyshi, Modeling and optimization of ethanol fermentation using Saccharomyces cerevisiae: Response surface methodology and artificial neural network Chem. Ind. Chem. Eng. Q. 19(2) (2013) 241-252.
  • [32] O.D. Samuel, M.O. Okwu, Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in modeling of waste coconut oil ethyl esters production, Energ. Source. Part A 41 (2019) 1049-1061.

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

Yıl 2020, Cilt 4, Sayı 2, 148 - 158, 30.12.2020

Öz

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.

Kaynakça

  • [1] P. Nematizade, B. Ghobadian, G. Najafi, F. Ommi, A. Abbaszadeh, Investigation some of the properties of fossil fuels and liquid biofuels blends for utilize at SI engines, IJSR, 2:4 (2013) 92-103.
  • [2] A. Tesfaw, F. Assefa, Current trends in bioethanol production by Saccharomyces cerevisiae: substrate, inhibitor reduction, growth variables, coculture, and immobilization, Int. Sch. Res. Notices (2014) 1-11.
  • [3] M. Mohamed, A.A. Eman, W. Elgammal, Roba, G. Ghitas. Comparative study on modeling by neural networks and response surface methodology for better prediction and optimization of fermentation parameters: Application on thermo-alkaline lipase production by Nocardiopsis sp. strain NRC/WN5, Biocatal. and Agricultural Biotechnol. 25 (2020).
  • [4] M. C. Anumaka, J.K. Offor, C.A Nwabueze, P.I. Obi, Techno-economic Feasibility of Bioethanol Production for Fossil Fuel-Fired Generating Plant in Nigeria, AJEST 1(4) (2014) 121-127.
  • [5] U. Saarela, K. Leiviska, E. Juuso, Modeling of a Fed-Batch Fermentation Process, Technical Report A No. 21, University of Oulu, Finland. (2003) 2-3.
  • [6] F. Erdem, S. cerevisiae ile Remazol Sarı (RR) Giderimine Yapay Sinir Ağı (YSA) Yaklaşımı, Uludağ University Journal of The Faculty of Engineering 24(2) (2019) 289-298.
  • [7] E. Betiku, E. Taiwo Abiola, Modeling and optimization of bioethanol production from breadfruit starch hydrolyzate vis-à-vis response surface methodology and artificial neural network, Renewable Energy (2015) 87-94.
  • [8] H. Ahmadian-Moghadam, F. Elegado, R. Banzuela Navye, Prediction of Ethanol Concentration in Biofuel Production Using Artificial Neural Networks AJMO 1(3) (2013) 31-35.
  • [9] R. Abd, K. Norliza, T. Noorhisham, Z. Yaakob, S. Gauri, Estimation of Bioethanol Production from Jatropha curcas Using Neural Network Key Engineering Materials (2013) 943–947.
  • [10] Y. Nagata, H.C. Khim, Optimization of a fermentation medium using neural networks and genetic algorithms Biotechnol Lett, 25 (2013) 1837-1842.
  • [11] H. Zentou, Z. Zainal Abidin, R. Yunus, D.R. Awang Biak , M. Zouanti, A. Hassani, Modeling of Molasses Fermentation for Bioethanol Production: A Comparative Investigation of Monod and Andrews Models Accuracy Assessment Biomolecules, 9(8) (2019) 308.
  • [12] G.M. Walker, Metals in yeast fermentation processes, Adv. Appl. Microbiol., 54 (2004) 197-229.
  • [13] R.C. Nabais, I. Sá-Correia, C.A.Viegas, J.M. Novais, Influence of Calcium Ion on Ethanol Tolerance of Saccharomyces bayanus and Alcoholic Fermentation by Yeasts, AEM 54(10) (1988) 2439-2446.
  • [14] Md. Fakruddin, Md.A. Quayum, M.M. Ahmed, N. Choudhury, Analysis of Key Factors Affecting Ethanol Production by Saccharomyces cerevisiae IFST-072011, Biotechnology 11 (2012) 248-252.
  • [15] Q. Xu, M. Wu, J. Hu, M. Gao, Effects of nitrogen sources and metal ions on ethanol fermentation with cadmium-containing medium J. Basic Microbiol. 56(1) (2016) 26-35.
  • [16] M.A. Palukurty, N.K. Telgana, H.M. Sundar, R. Bora, Screening and optimization of metal ions to enhance ethanol production using statistical experimental designs, Afr. J. Microbiol. Res. (2) (2008) 87-94.
  • [17] D. Soyuduru, M. Ergun, A. Tosun, Application of a statistical technique to investigate calcium, sodium magnesium ion effect in yeast fermentation Appl. Biochem. Biotechnol. 152:2 (2009) 326-333.
  • [18] U. Greeshma, S. Annalakshmi, Artificial Neural Network (Research paper on basics of ANN), International Journal of Scientific & J. Eng. Res. 6(4) (2015) 110-115.
  • [19] K. Shiruru, An Introduction to Artificial Neural Network, International Journal of Advance Research and Innovative Ideas in Education 1:27 (2016) 30.
  • [20] S.N. Mahmoud, A.E.M. Medhat, A.E.S. Hamdy, G. El Kobrosy, Application of artificial neural network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT, Alex. Eng. J. 51(1) (2012) 37-43.
  • [21] C. Cranganu, H. Luchian, M. Breaban, 2015. Artificial intelligent approaches in petroleum geosciences, (Berlin: Springer 2015) pp. 149.
  • [22] A. Sebayang, H. Masjuki, H. Ong, S.C. Chyuan, S. Dharma, A. Silitonga, F. Kusumo, J. Milano, Optimization of bioethanol production from sorghum grains using artificial neural networks integrated with ant colony Ind. Crops. Prod. 97 (2017) 146-155.
  • [23] B. Genç, H. Tunç, Optimal training and test sets design for machine learning, Turk. J. Elec. Eng. & Comp. Sci. 27 (2019) 1534-1545.
  • [24] N. Bekkari, A. Zeddouri, Using artificial neural network for predicting and controlling the effluent chemical oxygen demand in wastewater treatment plant, Manag. Environ. Qual. 30:3 (2019) 593-608.
  • [25] C.K. Chui, X. Li, Approximation by ridge functions and neural networks with one hidden layer, J. Approx. Theory 70 (1992) 131-141.
  • [26] K. Smith-Miles, J.N Gupta, Neural networks in business: techniques and applications for the operations researcher Comput. Oper. Res. 27 (2000) 1023-1044.
  • [27] D.R. Baughman, Y.A. Liu, Neural Networks in Bioprocessing and Chemical Engineering (Academic Press, 1995) pp. 21-109.
  • [28] A.C. Awang, B.S. Mohammed, M.R. Mustafa, Mix design proportion for strength prediction of rubbercrete using artificial neural network. Engineering Challenges for Sustainable Future (1st edition, London CRS Press, 2016).
  • [29] O.A. Adeoti, P.A. Osanaiye, Effect of Training Algorithms on the Performance of ANN for Pattern Recognition of Bivariate Process. Int. J. Comput. Appl. 69 (2013) 8-12.
  • [30] A. Okewale, O.A. Adesina, M.O. Oloko-oba, Comparative Study of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) on Optimization of Ethanol Production from Sawdust, Int. J. Eng. Res. Afr. 30 (2017) 125-133.
  • [31] M. Esfahanian, M. Nikzad, G.D. Najafpour, A.A. Ghoreyshi, Modeling and optimization of ethanol fermentation using Saccharomyces cerevisiae: Response surface methodology and artificial neural network Chem. Ind. Chem. Eng. Q. 19(2) (2013) 241-252.
  • [32] O.D. Samuel, M.O. Okwu, Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in modeling of waste coconut oil ethyl esters production, Energ. Source. Part A 41 (2019) 1049-1061.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Article
Yazarlar

Fatma ERDEM (Sorumlu Yazar)
Turkish Medicines and Medical Devices Agency
0000-0002-6014-6664
Türkiye


Dilek ÖZÇELİK
Türk Şeker Fabrikaları Genel Müdürlüğü
Türkiye


Mübeccel ERGUN
Gazi University
Türkiye

Yayımlanma Tarihi 30 Aralık 2020
Başvuru Tarihi 2 Ekim 2020
Kabul Tarihi 2 Aralık 2020
Yayınlandığı Sayı Yıl 2020, Cilt 4, Sayı 2

Kaynak Göster

Bibtex @araştırma makalesi { asujse804404, journal = {Aksaray University Journal of Science and Engineering}, issn = {}, eissn = {2587-1277}, address = {Aksaray Üniversitesi, Fen Bilimleri Enstitüsü, Merkez Kampüs, 68100 Aksaray}, publisher = {Aksaray Üniversitesi}, year = {2020}, volume = {4}, pages = {148 - 158}, doi = {}, title = {Modeling of Ion Effect on Fermentation for Bioethanol Production using Artificial Neural Network (ANN)}, key = {cite}, author = {Erdem, Fatma and Özçelik, Dilek and Ergun, Mübeccel} }
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 . Retrieved from http://asujse.aksaray.edu.tr/tr/pub/issue/58393/804404
MLA Erdem, F. , Özçelik, D. , Ergun, M. "Modeling of Ion Effect on Fermentation for Bioethanol Production using Artificial Neural Network (ANN)" . Aksaray University Journal of Science and Engineering 4 (2020 ): 148-158 <http://asujse.aksaray.edu.tr/tr/pub/issue/58393/804404>
Chicago Erdem, F. , Özçelik, D. , Ergun, M. "Modeling of Ion Effect on Fermentation for Bioethanol Production using Artificial Neural Network (ANN)". Aksaray University Journal of Science and Engineering 4 (2020 ): 148-158
RIS TY - JOUR T1 - Modeling of Ion Effect on Fermentation for Bioethanol Production using Artificial Neural Network (ANN) AU - Fatma Erdem , Dilek Özçelik , Mübeccel Ergun Y1 - 2020 PY - 2020 N1 - DO - T2 - Aksaray University Journal of Science and Engineering JF - Journal JO - JOR SP - 148 EP - 158 VL - 4 IS - 2 SN - -2587-1277 M3 - UR - Y2 - 2020 ER -
EndNote %0 Aksaray University Journal of Science and Engineering Modeling of Ion Effect on Fermentation for Bioethanol Production using Artificial Neural Network (ANN) %A Fatma Erdem , Dilek Özçelik , Mübeccel Ergun %T Modeling of Ion Effect on Fermentation for Bioethanol Production using Artificial Neural Network (ANN) %D 2020 %J Aksaray University Journal of Science and Engineering %P -2587-1277 %V 4 %N 2 %R %U
ISNAD Erdem, Fatma , Özçelik, Dilek , Ergun, Mübeccel . "Modeling of Ion Effect on Fermentation for Bioethanol Production using Artificial Neural Network (ANN)". Aksaray University Journal of Science and Engineering 4 / 2 (Aralık 2020): 148-158 .
AMA Erdem F. , Özçelik D. , Ergun M. Modeling of Ion Effect on Fermentation for Bioethanol Production using Artificial Neural Network (ANN). Aksaray J. Sci. Eng.. 2020; 4(2): 148-158.
Vancouver Erdem F. , Özçelik D. , Ergun M. Modeling of Ion Effect on Fermentation for Bioethanol Production using Artificial Neural Network (ANN). Aksaray University Journal of Science and Engineering. 2020; 4(2): 148-158.
IEEE F. Erdem , D. Özçelik ve M. Ergun , "Modeling of Ion Effect on Fermentation for Bioethanol Production using Artificial Neural Network (ANN)", Aksaray University Journal of Science and Engineering, c. 4, sayı. 2, ss. 148-158, Ara. 2020

Aksaray J. Sci. Eng. | e-ISSN: 2587-1277 | Period: Biannually | Founded: 2017 | Publisher: Aksaray University | https://asujse.aksaray.edu.tr


ASUJSE is indexing&Archiving in

crossref-logo-landscape-100.pngscilitLogo.png    scholar_logo_30dp.png   logo_wcmasthead_en.png   logo-large-explore.png    oaliblogo2.jpg   GettyImages_90309427_montage_255x130px.png search-result-logo-horizontal-TEST.jpgIndex Copernicus

Dimensions



Creative Commons License