IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v76y2015icp408-417.html
   My bibliography  Save this article

Performance evaluation of artificial neural network coupled with generic algorithm and response surface methodology in modeling and optimization of biodiesel production process parameters from shea tree (Vitellaria paradoxa) nut butter

Author

Listed:
  • Betiku, Eriola
  • Okunsolawo, Samuel S.
  • Ajala, Sheriff O.
  • Odedele, Olatunde S.

Abstract

This work investigated the potential of shea butter oil (SBO) as feedstock for synthesis of biodiesel. Due to high free fatty acid (FFA) of SBO used, response surface methodology (RSM) was employed to model and optimize the pretreatment step while its conversion to biodiesel was modeled and optimized using RSM and artificial neural network (ANN). The acid value of the SBO was reduced to 1.19 mg KOH/g with oil/methanol molar ratio of 3.3, H2SO4 of 0.15 v/v, time of 60 min and temperature of 45 °C. Optimum values predicted for the transesterification reaction by RSM were temperature of 90 °C, KOH of 0.6 w/v, oil/methanol molar ratio of 3.5, and time of 30 min with actual shea butter oil biodiesel (SBOB) yield of 99.65% (w/w). ANN combined with generic algorithm gave the optimal condition as temperature of 82 °C, KOH of 0.40 w/v, oil/methanol molar ratio of 2.62 and time of 30 min with actual SBOB yield of 99.94% (w/w). Coefficient of determination (R2) and absolute average deviation (AAD) of the models were 0.9923, 0.83% (RSM) and 0.9991, 0.15% (ANN), which demonstrated that ANN model was more efficient than RSM model. Properties of SBOB produced were within biodiesel standard specifications.

Suggested Citation

  • Betiku, Eriola & Okunsolawo, Samuel S. & Ajala, Sheriff O. & Odedele, Olatunde S., 2015. "Performance evaluation of artificial neural network coupled with generic algorithm and response surface methodology in modeling and optimization of biodiesel production process parameters from shea tr," Renewable Energy, Elsevier, vol. 76(C), pages 408-417.
  • Handle: RePEc:eee:renene:v:76:y:2015:i:c:p:408-417
    DOI: 10.1016/j.renene.2014.11.049
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148114007794
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2014.11.049?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.

    References listed on IDEAS

    as
    1. Gueguim Kana, E.B. & Oloke, J.K. & Lateef, A. & Adesiyan, M.O., 2012. "Modeling and optimization of biogas production on saw dust and other co-substrates using Artificial Neural network and Genetic Algorithm," Renewable Energy, Elsevier, vol. 46(C), pages 276-281.
    2. Betiku, Eriola & Omilakin, Oluwasesan Ropo & Ajala, Sheriff Olalekan & Okeleye, Adebisi Aminat & Taiwo, Abiola Ezekiel & Solomon, Bamidele Ogbe, 2014. "Mathematical modeling and process parameters optimization studies by artificial neural network and response surface methodology: A case of non-edible neem (Azadirachta indica) seed oil biodiesel synth," Energy, Elsevier, vol. 72(C), pages 266-273.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Takeno, Mitsuo L. & Mendonça, Iasmin M. & Barros, Silma de S. & de Sousa Maia, Paulo J. & Pessoa Jr., Wanison A.G. & Souza, Mayane P. & Soares, Elzalina R. & Bindá, Rosane dos S. & Calderaro, Fábio L., 2021. "A novel CaO-based catalyst obtained from silver croaker (Plagioscion squamosissimus) stone for biodiesel synthesis: Waste valorization and process optimization," Renewable Energy, Elsevier, vol. 172(C), pages 1035-1045.
    2. Pirmoradi, Neda & Ghaneian, Mohammad Taghi & Ehrampoush, Mohammad Hassan & Salmani, Mohammad Hossein & Hatami, Behnam, 2021. "The conversion of poultry slaughterhouse wastewater sludge into biodiesel: Process modeling and optimization," Renewable Energy, Elsevier, vol. 178(C), pages 1236-1249.
    3. Silitonga, A.S. & Shamsuddin, A.H. & Mahlia, T.M.I. & Milano, Jassinne & Kusumo, F. & Siswantoro, Joko & Dharma, S. & Sebayang, A.H. & Masjuki, H.H. & Ong, Hwai Chyuan, 2020. "Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification: ELM modeling and optimization," Renewable Energy, Elsevier, vol. 146(C), pages 1278-1291.
    4. Jisieike, Chiazor Faustina & Ishola, Niyi Babatunde & Latinwo, Lekan M. & Betiku, Eriola, 2023. "Crude rubber seed oil esterification using a solid catalyst: Optimization by hybrid adaptive neuro-fuzzy inference system and response surface methodology," Energy, Elsevier, vol. 263(PB).
    5. Sarlaki, Ehsan & Sharif Paghaleh, Ali & Kianmehr, Mohammad Hossein & Asefpour Vakilian, Keyvan, 2021. "Valorization of lignite wastes into humic acids: Process optimization, energy efficiency and structural features analysis," Renewable Energy, Elsevier, vol. 163(C), pages 105-122.
    6. Aghbashlo, Mortaza & Hosseinpour, Soleiman & Tabatabaei, Meisam & Dadak, Ali, 2017. "Fuzzy modeling and optimization of the synthesis of biodiesel from waste cooking oil (WCO) by a low power, high frequency piezo-ultrasonic reactor," Energy, Elsevier, vol. 132(C), pages 65-78.
    7. Soltanali, Hamzeh & Nikkhah, Amin & Rohani, Abbas, 2017. "Energy audit of Iranian kiwifruit production using intelligent systems," Energy, Elsevier, vol. 139(C), pages 646-654.
    8. Sakthivel, G. & Sivaraja, C.M. & Ikua, Bernard W., 2019. "Prediction OF CI engine performance, emission and combustion parameters using fish oil as a biodiesel by fuzzy-GA," Energy, Elsevier, vol. 166(C), pages 287-306.
    9. Muhammad, Gul & Potchamyou Ngatcha, Ange Douglas & Lv, Yongkun & Xiong, Wenlong & El-Badry, Yaser A. & Asmatulu, Eylem & Xu, Jingliang & Alam, Md Asraful, 2022. "Enhanced biodiesel production from wet microalgae biomass optimized via response surface methodology and artificial neural network," Renewable Energy, Elsevier, vol. 184(C), pages 753-764.
    10. He, Yuan & Meng, Zhiyi & Xu, Hong & Zou, Yue, 2020. "A dynamic model of evaluating differential automatic method for solving plane problems based on BP neural network algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    11. Soltani, Soroush & Roodbar Shojaei, Taha & Khanian, Nasrin & Shean Yaw Choong, Thomas & Asim, Nilofar & Zhao, Yue, 2022. "Artificial neural network method modeling of microwave-assisted esterification of PFAD over mesoporous TiO2‒ZnO catalyst," Renewable Energy, Elsevier, vol. 187(C), pages 760-773.
    12. Elahi, Ehsan & Zhang, Zhixin & Khalid, Zainab & Xu, Haiyun, 2022. "Application of an artificial neural network to optimise energy inputs: An energy- and cost-saving strategy for commercial poultry farms," Energy, Elsevier, vol. 244(PB).
    13. Kusumo, F. & Silitonga, A.S. & Masjuki, H.H. & Ong, Hwai Chyuan & Siswantoro, J. & Mahlia, T.M.I., 2017. "Optimization of transesterification process for Ceiba pentandra oil: A comparative study between kernel-based extreme learning machine and artificial neural networks," Energy, Elsevier, vol. 134(C), pages 24-34.
    14. Chong, Daniel Jia Sheng & Chan, Yi Jing & Arumugasamy, Senthil Kumar & Yazdi, Sara Kazemi & Lim, Jun Wei, 2023. "Optimisation and performance evaluation of response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in the prediction of biogas production ," Energy, Elsevier, vol. 266(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Iftikhar Ahmad & Adil Sana & Manabu Kano & Izzat Iqbal Cheema & Brenno C. Menezes & Junaid Shahzad & Zahid Ullah & Muzammil Khan & Asad Habib, 2021. "Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions," Energies, MDPI, vol. 14(16), pages 1-27, August.
    2. Yesilyurt, Murat Kadir & Eryilmaz, Tanzer & Arslan, Mevlüt, 2018. "A comparative analysis of the engine performance, exhaust emissions and combustion behaviors of a compression ignition engine fuelled with biodiesel/diesel/1-butanol (C4 alcohol) and biodiesel/diesel/," Energy, Elsevier, vol. 165(PB), pages 1332-1351.
    3. Najafi, Gholamhassan & Ghobadian, Barat & Yusaf, Talal & Safieddin Ardebili, Seyed Mohammad & Mamat, Rizalman, 2015. "Optimization of performance and exhaust emission parameters of a SI (spark ignition) engine with gasoline–ethanol blended fuels using response surface methodology," Energy, Elsevier, vol. 90(P2), pages 1815-1829.
    4. Elahi, Ehsan & Zhang, Zhixin & Khalid, Zainab & Xu, Haiyun, 2022. "Application of an artificial neural network to optimise energy inputs: An energy- and cost-saving strategy for commercial poultry farms," Energy, Elsevier, vol. 244(PB).
    5. Munir, Mamoona & Ahmad, Mushtaq & Saeed, Muhammad & Waseem, Amir & Rehan, Mohammad & Nizami, Abdul-Sattar & Zafar, Muhammad & Arshad, Muhammad & Sultana, Shazia, 2019. "Sustainable production of bioenergy from novel non-edible seed oil (Prunus cerasoides) using bimetallic impregnated montmorillonite clay catalyst," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 321-332.
    6. Abdel daiem, Mahmoud M. & Hatata, Ahmed & Galal, Osama H. & Said, Noha & Ahmed, Dalia, 2021. "Prediction of biogas production from anaerobic co-digestion of waste activated sludge and wheat straw using two-dimensional mathematical models and an artificial neural network," Renewable Energy, Elsevier, vol. 178(C), pages 226-240.
    7. Srinidhi, Campli & Madhusudhan, A. & Channapattana, S.V. & Gawali, S.V. & Aithal, Kiran, 2021. "RSM based parameter optimization of CI engine fuelled with nickel oxide dosed Azadirachta indica methyl ester," Energy, Elsevier, vol. 234(C).
    8. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    9. Chinese, D. & Patrizio, P. & Nardin, G., 2014. "Effects of changes in Italian bioenergy promotion schemes for agricultural biogas projects: Insights from a regional optimization model," Energy Policy, Elsevier, vol. 75(C), pages 189-205.
    10. Sakiewicz, P. & Piotrowski, K. & Ober, J. & Karwot, J., 2020. "Innovative artificial neural network approach for integrated biogas – wastewater treatment system modelling: Effect of plant operating parameters on process intensification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    11. Chaves, Gustavo T. & Teles, Felipe & Balbo, Antonio R. & dos Reis, Célia A. & Florentino, Helenice de Oliveira, 2024. "Mathematical modelling of biodigestion in an Indian biodigester and its stability analysis via Lyapunov technique," Renewable Energy, Elsevier, vol. 226(C).
    12. Ido, Alexander L. & de Luna, Mark Daniel G. & Capareda, Sergio C. & Maglinao, Amado L. & Nam, Hyungseok, 2018. "Application of central composite design in the optimization of lipid yield from Scenedesmus obliquus microalgae by ultrasound-assisted solvent extraction," Energy, Elsevier, vol. 157(C), pages 949-956.
    13. Soltanali, Hamzeh & Nikkhah, Amin & Rohani, Abbas, 2017. "Energy audit of Iranian kiwifruit production using intelligent systems," Energy, Elsevier, vol. 139(C), pages 646-654.
    14. Djatkov, Djordje & Effenberger, Mathias & Martinov, Milan, 2014. "Method for assessing and improving the efficiency of agricultural biogas plants based on fuzzy logic and expert systems," Applied Energy, Elsevier, vol. 134(C), pages 163-175.
    15. Willeghems, Gwen & Buysse, Jeroen, 2016. "Changing old habits: The case of feeding patterns in anaerobic digesters," Renewable Energy, Elsevier, vol. 92(C), pages 212-221.
    16. Cao, Leichang & Zhang, Shicheng, 2015. "Production and characterization of biodiesel derived from Hodgsonia macrocarpa seed oil," Applied Energy, Elsevier, vol. 146(C), pages 135-140.
    17. Leila Ezzatzadegan & Rubiyah Yusof & Noor Azian Morad & Parvaneh Shabanzadeh & Nur Syuhana Muda & Tohid N. Borhani, 2021. "Experimental and Artificial Intelligence Modelling Study of Oil Palm Trunk Sap Fermentation," Energies, MDPI, vol. 14(8), pages 1-22, April.
    18. 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.
    19. Postawa, Karol & Szczygieł, Jerzy & Kułażyński, Marek, 2020. "A comprehensive comparison of ODE solvers for biochemical problems," Renewable Energy, Elsevier, vol. 156(C), pages 624-633.
    20. KeChrist Obileke & Golden Makaka & Nwabunwanne Nwokolo, 2022. "Efficient Methane Production from Anaerobic Digestion of Cow Dung: An Optimization Approach," Challenges, MDPI, vol. 13(2), pages 1-11, October.

    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:76:y:2015:i:c:p:408-417. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.