IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v74y2015icp87-94.html
   My bibliography  Save this article

Modeling and optimization of bioethanol production from breadfruit starch hydrolyzate vis-à-vis response surface methodology and artificial neural network

Author

Listed:
  • Betiku, Eriola
  • Taiwo, Abiola Ezekiel

Abstract

This study investigated the use of Breadfruit Starch Hydrolysate (BFSH) as the sole carbon source for bioethanol production and the optimization of the fermentation parameters. The results showed that the yeast was able to utilize the BFSH with and without nutrient supplements, with highest bioethanol yield of 3.96 and 3.60% volume fraction, respectively after 24 h of fermentation. A statistically significant quadratic regression model (p < 0.05) was obtained for bioethanol yield prediction. Response Surface Methodology (RSM) optimal condition values established for the bioethanol yield were BFSH concentration of 134.81 g L−1, time of 21.33 h and pH of 5.01 with predicted bioethanol yield of 3.95% volume fraction. Using Artificial Neural Network (ANN), multilayer normal feedforward incremental back propagation with hyperbolic tangent function gave the best performance as a predictive model for bioethanol yield. ANN optimal condition values were BFSH concentration of 120 g L−1, time of 24 h and pH of 4.5 with predicted bioethanol yield of 4.21% volume fraction. The predicted bioethanol yield was validated experimentally as 4.10% volume fraction and 4.22% volume fraction for RSM and ANN, respectively. Coefficient of Determination (R2) and Absolute Average Deviation (AAD) were determined as 1 and 0.09% for ANN and 0.9882 and 1.67% for RSM, respectively. Thus, confirming ANN was better than RSM in both data fittings and estimation capabilities.

Suggested Citation

  • Betiku, Eriola & Taiwo, Abiola Ezekiel, 2015. "Modeling and optimization of bioethanol production from breadfruit starch hydrolyzate vis-à-vis response surface methodology and artificial neural network," Renewable Energy, Elsevier, vol. 74(C), pages 87-94.
    • Handle: RePEc:eee:renene:v:74:y:2015:i:c:p:87-94
    DOI: 10.1016/j.renene.2014.07.054
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148114004522
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2014.07.054?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Akhabue, Christopher Ehiaguina & Osa-Benedict, Evidence Osayi & Oyedoh, Eghe Amenze & Otoikhian, Shegun Kevin, 2020. "Development of a bio-based bifunctional catalyst for simultaneous esterification and transesterification of neem seed oil: Modeling and optimization studies," Renewable Energy, Elsevier, vol. 152(C), pages 724-735.
    2. Arora, Richa & Behera, Shuvashish & Sharma, Nilesh Kumar & Kumar, Sachin, 2017. "Augmentation of ethanol production through statistically designed growth and fermentation medium using novel thermotolerant yeast isolates," Renewable Energy, Elsevier, vol. 109(C), pages 406-421.
    3. Li, Lu & Zou, Changjun & Zhou, Lu & Lin, Lang, 2017. "Cucurbituril-protected Cs2.5H0.5PW12O40 for optimized biodiesel production from waste cooking oil," Renewable Energy, Elsevier, vol. 107(C), pages 14-22.
    4. Chohan, Naseeha A. & Aruwajoye, G.S. & Sewsynker-Sukai, Y. & Gueguim Kana, E.B., 2020. "Valorisation of potato peel wastes for bioethanol production using simultaneous saccharification and fermentation: Process optimization and kinetic assessment," Renewable Energy, Elsevier, vol. 146(C), pages 1031-1040.
    5. Dhandayuthapani, K. & Kumar, P. Senthil & Chia, Wen Yi & Chew, Kit Wayne & Karthik, V. & Selvarangaraj, H. & Selvakumar, P. & Sivashanmugam, P. & Show, Pau Loke, 2022. "Bioethanol from hydrolysate of ultrasonic processed robust microalgal biomass cultivated in dairy wastewater under optimal strategy," Energy, Elsevier, vol. 244(PA).
    6. Guo, Wencheng & Yang, Jiandong, 2018. "Dynamic performance analysis of hydro-turbine governing system considering combined effect of downstream surge tank and sloping ceiling tailrace tunnel," Renewable Energy, Elsevier, vol. 129(PA), pages 638-651.
    7. Shadbahr, Jalil & Zhang, Yan & Khan, Faisal & Hawboldt, Kelly, 2018. "Multi-objective optimization of simultaneous saccharification and fermentation for cellulosic ethanol production," Renewable Energy, Elsevier, vol. 125(C), pages 100-107.
    8. Abiola Ezekiel Taiwo & Tafirenyika Nyamayaro Madzimbamuto & Tunde Victor Ojumu, 2018. "Optimization of Corn Steep Liquor Dosage and Other Fermentation Parameters for Ethanol Production by Saccharomyces cerevisiae Type 1 and Anchor Instant Yeast," Energies, MDPI, vol. 11(7), pages 1-20, July.
    9. Marina Corral Bobadilla & Roberto Fernández Martínez & Rubén Lostado Lorza & Fátima Somovilla Gómez & Eliseo P. Vergara González, 2018. "Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector Machines," Energies, MDPI, vol. 11(11), pages 1-19, November.
    10. Nazarpour, Mehrshad & Taghizadeh-Alisaraei, Ahmad & Asghari, Ali & Abbaszadeh-Mayvan, Ahmad & Tatari, Aliasghar, 2022. "Optimization of biohydrogen production from microalgae by response surface methodology (RSM)," Energy, Elsevier, vol. 253(C).
    11. Sahu, Omprakash, 2021. "Appropriateness of rose (Rosa hybrida) for bioethanol conversion with enzymatic hydrolysis: Sustainable development on green fuel production," Energy, Elsevier, vol. 232(C).
    12. Małgorzata Smuga-Kogut & Tomasz Kogut & Roksana Markiewicz & Adam Słowik, 2021. "Use of Machine Learning Methods for Predicting Amount of Bioethanol Obtained from Lignocellulosic Biomass with the Use of Ionic Liquids for Pretreatment," Energies, MDPI, vol. 14(1), pages 1-16, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:74:y:2015:i:c:p:87-94. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.