PREDICTION AND OPTIMIZATION OF ETHANOL CONCENTRATION IN BIOFUEL PRODUCTION USING FUZZY NEURAL NETWORK
DOI:
https://doi.org/10.11113/jt.v78.7957Keywords:
Lignocellulose, neuro-Fuzzy, corn stoverAbstract
In recent years, producing economical biofuels especially bio-ethanol from lignocellulosic materials has been widely considered. Fermentation is an important step in ethanol production process. Fermentation process is completely nonlinear and depends on some parameters such as temperature, sugar content, and PH. One of the difficulties in producing biomass is finding the optimum point of the interrelated parameters in the fermentation step. In this study, an elaborate prediction Neuro-Fuzzy model was built to predict the bio-ethanol production from corn stover. Also, particle swarm optimization (PSO) method was used to optimize the three studied parameters: temperature, glucose content, and fermentation time. The attained correlation coefficient (0.99), and root mean square error (0.637) for model validation show the reliability of the model. Optimization of the model shows the optimum fermentation time and required temperature quantities, 69.39hours and 34.50 ͦC, respectively. The good result for ANFIS modeling on fermentation process in bio-ethanol production from corn stover shows that this method can be used to investigate more about other biomass lignocellulos sources.
References
Chin, K. L., H’ng, P. S., Wong, L. J., Tey, B. T. and Paridah, M. T. 2011. Production Of Glucose From Oil Palm Trunk And Sawdust Of Rubberwood And Mixed Hardwood. Applied Energy. 88(11): 4222-4228.
Zhuang, X., Wang, W., Yu, Q., Qi, W., Wang, Q., Tan, X., Zhou, G. & Yuan, Z. 2016. Liquid Hot Water Pretreatment Of Lignocellulosic Biomass For Bioethanol Production Accompanying With High Valuable Products. Bioresource Technology. 199: 68-75.
Ramos, L. P., da Silva, L., Ballem, A. C., Pitarelo, A. P., Chiarello, L. M. and Silveira, M. H. L. 2015. Enzymatic Hydrolysis Of Steam-Exploded Sugarcane Bagasse Using High Total Solids And Low Enzyme Loadings. Bioresource Technology. 175: 195-202.
Kim, J. S., Lee, Y. Y. and Kim, T. H. 2016. A Review On Alkaline Pretreatment Technology For Bioconversion Of Lignocellulosic Biomass. Bioresource Technology. 199: 42-48.
Ishola, M. M., Jahandideh, A., Haidarian, B., Brandberg, T. and Taherzadeh, M. J. 2013. Simultaneous Saccharification, Filtration And Fermentation (SSFF): A Novel Method For Bioethanol Production From Lignocellulosic Biomass. Bioresource Technology. 133: 68-73.
Karimi, K. and Chisti, Y. 2015. Future of Bioethanol. Biofuel Research Journal. 2: 147-147.
Bartee, J., Noll, P., Axelrud, C., Schweiger, C. and Sayyar-Rodsari, B. 2009. Industrial Application Of Nonlinear Model Predictive Control Technology For Fuel Ethanol Fermentation Process. In American Control Conference (ACC), 2009 IEEE. 2290-2294.
Talebnia, F., Karakashev, D. and Angelidaki, I. 2010. Production Of Bioethanol From Wheat Straw: An Overview On Pretreatment, Hydrolysis And Fermentation. Bioresource Technology. 101: 4744-4753.
Singh, R., Shukla, A., Tiwari, S. and Srivastava, M. 2014. A Review On Delignification Of Lignocellulosic Biomass For Enhancement Of Ethanol Production Potential. Renewable and Sustainable Energy Reviews. 32: 713-728.
Amillastre, E., Aceves-Lara, C.-A., Uribelarrea, J.-L., Alfenore, S. and Guillouet, S. E. 2012. Dynamic Model Of Temperature Impact On Cell Viability And Major Product Formation During Fed-Batch And Continuous Ethanolic Fermentation In Saccharomyces Cerevisiae. Bioresource Technology. 117: 242-250.
Sansonetti, S., Hobley, T. J., Calabrò, V., Villadsen, J. and Sin, G. 2011. A Biochemically Structured Model For Ethanol Fermentation By Kluyveromyces Marxianus: A Batch Fermentation And Kinetic Study. Bioresource Technology. 102(16): 7513-7520.
Karakuzu, C., Türker, M. and Öztürk, S. 2006. Modelling, On-Line State Estimation And Fuzzy Control Of Production Scale Fed-Batch Baker's Yeast Fermentation. Control Engineering Practice. 14(8): 959-974.
Rocha, M., Mendes, R., Rocha, O., Rocha, I. and Ferreira, E. C. 2014. Optimization Of Fed-Batch Fermentation Processes With Bio-Inspired Algorithms. Expert Systems with Applications. 41(5): 2186-2195.
Bagheri, A., Nariman-Zadeh, N., Jamali, A. and Dayjoori, K. 2009. Design of ANFIS Networks Using Hybrid Genetic and SVD Method for the Prediction of Coastal Wave Impacts. Applications of Soft Computing. 83-92.
Khoshnevisan, B., Rafiee, S., Omid, M. and Mousazadeh, H. 2014. Development Of An Intelligent System Based On ANFIS For Predicting Wheat Grain Yield On The Basis Of Energy Inputs. Information Processing in Agriculture. 1: 14-22.
Zhu, J.-Q., Qin, L., Li, B.-Z. and Yuan, Y.-J. 2014. Simultaneous Saccharification And Co-Fermentation Of Aqueous Ammonia Pretreated Corn Stover With An Engineered Saccharomyces cerevisiae SyBE005. Bioresource Technology. 169: 9-18.
Kar, S., Das, S. and Ghosh, P. K. 2014. Applications Of Neuro Fuzzy Systems: A Brief Review And Future Outline. Applied Soft Computing. 15: 243-259.
Jang, J.-S. R. 1993. ANFIS: Adaptive-Network-Based Fuzzy Inference System. Systems, Man and Cybernetics. IEEE Transactions on. 23(3): 665-685.
Bishop, C. M. 1995. Neural Networks For Pattern Recognition. New York: Oxford university press.
Charef, C., Taibi, M., Vincent, N. and Benmahammed, K. 2009. Fuzzy And Neuro-Fuzzy Modeling Of A Fermentation Process. Int. Arab J. Inf. Technol. 6(4): 378-384.
Abghari, S. Z. and Sadi, M. 2013. Application Of Adaptive Neuro-Fuzzy Inference System For The Prediction Of The Yield Distribution Of The Main Products In The Steam Cracking Of Atmospheric Gasoil. Journal of the Taiwan Institute of Chemical Engineers. 44(3): 365-376.
Shuler, M. L. and Kargi, F. 2002. Bioprocess Engineering. New York: Prentice Hall.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
