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Year 2020, Volume: 4 Issue: 2, 148 - 158, 30.12.2020

Fatma Erdem , Dilek Özçelik , Mübeccel Ergun

Abstract

References

  • [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.
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  • [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)

Year 2020, Volume: 4 Issue: 2, 148 - 158, 30.12.2020

Fatma Erdem , Dilek Özçelik , Mübeccel Ergun

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

Bioethanol Artificial Neural Network Modelling Fermentation

References

  • [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.
There are 32 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Fatma Erdem 0000-0002-6014-6664

Dilek Özçelik

Mübeccel Ergun

Publication Date December 30, 2020
Submission Date October 2, 2020
Acceptance Date December 2, 2020
Published in Issue Year 2020Volume: 4 Issue: 2

Cite

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.
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