IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i4p1122-d502692.html
   My bibliography  Save this article

Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective

Author

Listed:
  • Krishna Kumar Gupta

    (Department of Mechanical Engineering, MPSTME, SVKM’s Narsee Monjee Institute of Management Studies (NMIMS), Shirpur Campus, Dhule 425 405, India)

  • Kanak Kalita

    (Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi 600 062, India)

  • Ranjan Kumar Ghadai

    (Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar 737 136, India)

  • Manickam Ramachandran

    (Data Analytics Lab, REST Labs, Kaveripattinam, Krishnagiri 635 112, India)

  • Xiao-Zhi Gao

    (School of Computing, University of Eastern Finland, FI-70211 Kuopio, Finland)

Abstract

Owing to the ever-growing impetus towards the development of eco-friendly and low carbon footprint energy solutions, biodiesel production and usage have been the subject of tremendous research efforts. The biodiesel production process is driven by several process parameters, which must be maintained at optimum levels to ensure high productivity. Since biodiesel productivity and quality are also dependent on the various raw materials involved in transesterification, physical experiments are necessary to make any estimation regarding them. However, a brute force approach of carrying out physical experiments until the optimal process parameters have been achieved will not succeed, due to a large number of process parameters and the underlying non-linear relation between the process parameters and responses. In this regard, a machine learning-based prediction approach is used in this paper to quantify the response features of the biodiesel production process as a function of the process parameters. Three powerful machine learning algorithms—linear regression, random forest regression and AdaBoost regression are comprehensively studied in this work. Furthermore, two separate examples—one involving biodiesel yield, the other regarding biodiesel free fatty acid conversion percentage—are illustrated. It is seen that both random forest regression and AdaBoost regression can achieve high accuracy in predictive modelling of biodiesel yield and free fatty acid conversion percentage. However, AdaBoost may be a more suitable approach for biodiesel production modelling, as it achieves the best accuracy amongst the tested algorithms. Moreover, AdaBoost can be more quickly deployed, as it was seen to be insensitive to number of regressors used.

Suggested Citation

  • Krishna Kumar Gupta & Kanak Kalita & Ranjan Kumar Ghadai & Manickam Ramachandran & Xiao-Zhi Gao, 2021. "Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective," Energies, MDPI, vol. 14(4), pages 1-16, February.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1122-:d:502692
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/4/1122/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/4/1122/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pessoa Junior, Wanison A.G. & Takeno, Mitsuo L. & Nobre, Francisco X. & Barros, Silma de S. & Sá, Ingrity S.C. & Silva, Edson P. & Manzato, Lizandro & Iglauer, Stefan & de Freitas, Flávio A., 2020. "Application of water treatment sludge as a low-cost and eco-friendly catalyst in the biodiesel production via fatty acids esterification: Process optimization," Energy, Elsevier, vol. 213(C).
    2. Ahmad, Tanweer & Danish, Mohammed & Kale, Pradeep & Geremew, Belete & Adeloju, Samuel B. & Nizami, Maniruddin & Ayoub, Muhammad, 2019. "Optimization of process variables for biodiesel production by transesterification of flaxseed oil and produced biodiesel characterizations," Renewable Energy, Elsevier, vol. 139(C), pages 1272-1280.
    3. Matinja, Adamu Idris & Mohd Zain, Nor Azimah & Suhaimi, Mohd Suardi & Alhassan, Adamu Jibril, 2019. "Optimization of biodiesel production from palm oil mill effluent using lipase immobilized in PVA-alginate-sulfate beads," Renewable Energy, Elsevier, vol. 135(C), pages 1178-1185.
    4. Shafiee, Shahriar & Topal, Erkan, 2009. "When will fossil fuel reserves be diminished?," Energy Policy, Elsevier, vol. 37(1), pages 181-189, January.
    5. Jayaprabakar, J. & Dawn, S.S. & Ranjan, A. & Priyadharsini, P. & George, R.J. & Sadaf, S. & Rajha, C. Rajeswara, 2019. "Process optimization for biodiesel production from sheep skin and its performance, emission and combustion characterization in CI engine," Energy, Elsevier, vol. 174(C), pages 54-68.
    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. Janjhyam Venkata Naga Ramesh & Syed Khasim & Mohamed Abbas & Kareemulla Shaik & Mohammad Zia Ur Rahman & Muniyandy Elangovan, 2023. "Cloud Services User’s Recommendation System Using Random Iterative Fuzzy-Based Trust Computation and Support Vector Regression," Mathematics, MDPI, vol. 11(10), pages 1-14, May.
    2. Jingming Su & Xuguang Han & Yan Hong, 2023. "Short Term Power Load Forecasting Based on PSVMD-CGA Model," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    3. Hüseyin Çamur & Ahmed Muayad Rashid Al-Ani, 2022. "Prediction of Oxidation Stability of Biodiesel Derived from Waste and Refined Vegetable Oils by Statistical Approaches," Energies, MDPI, vol. 15(2), pages 1-26, January.
    4. Syauqi, Ahmad & Uwitonze, Hosanna & Chaniago, Yus Donald & Lim, Hankwon, 2024. "Design and optimization of an onboard boil-off gas re-liquefaction process under different weather-related scenarios with machine learning predictions," Energy, Elsevier, vol. 293(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. Sun, Shangde & Li, Kaiyue, 2020. "Biodiesel production from phoenix tree seed oil catalyzed by liquid lipozyme TL100L," Renewable Energy, Elsevier, vol. 151(C), pages 152-160.
    2. Aye, Goodness & Gupta, Rangan & Hammoudeh, Shawkat & Kim, Won Joong, 2015. "Forecasting the price of gold using dynamic model averaging," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 257-266.
    3. Ali Mubarak Al-Qahtani, 2023. "A Comprehensive Review in Microwave Pyrolysis of Biomass, Syngas Production and Utilisation," Energies, MDPI, vol. 16(19), pages 1-16, September.
    4. Jen-Yu Lee & Tien-Thinh Nguyen & Hong-Giang Nguyen & Jen-Yao Lee, 2022. "Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe," Energies, MDPI, vol. 15(11), pages 1-15, May.
    5. Steve Newbold & Charles Griffiths & Christopher C. Moore & Ann Wolverton & Elizabeth Kopits, 2010. "The "Social Cost of Carbon" Made Simple," NCEE Working Paper Series 201007, National Center for Environmental Economics, U.S. Environmental Protection Agency, revised Aug 2010.
    6. Ritter, Hendrik & Zimmermann, Karl, 2019. "Cap-and-Trade Policy vs. Carbon Taxation: Of Leakage and Linkage," EconStor Preprints 197796, ZBW - Leibniz Information Centre for Economics.
    7. Yassir El Karkri & Alexis B. Rey-Boué & Hassan El Moussaoui & Johannes Stöckl & Thomas I. Strasser, 2019. "Improved Control of Grid-connected DFIG-based Wind Turbine using Proportional-Resonant Regulators during Unbalanced Grid," Energies, MDPI, vol. 12(21), pages 1-21, October.
    8. Jānis Krūmiņš & Māris Kļaviņš, 2023. "Investigating the Potential of Nuclear Energy in Achieving a Carbon-Free Energy Future," Energies, MDPI, vol. 16(9), pages 1-31, April.
    9. Wang, Yifei & Leung, Dennis Y.C. & Xuan, Jin & Wang, Huizhi, 2016. "A review on unitized regenerative fuel cell technologies, part-A: Unitized regenerative proton exchange membrane fuel cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 961-977.
    10. Bodisco, Timothy & Brown, Richard J., 2013. "Inter-cycle variability of in-cylinder pressure parameters in an ethanol fumigated common rail diesel engine," Energy, Elsevier, vol. 52(C), pages 55-65.
    11. Foster, John & Bell, William Paul & Wild, Phillip & Sharma, Deepak & Sandu, Suwin & Froome, Craig & Wagner, Liam & Misra, Suchi & Bagia, Ravindra, 2013. "Analysis of institutional adaptability to redress electricity infrastructure vulnerability due to climate change," MPRA Paper 47787, University Library of Munich, Germany.
    12. Kannan, Nadarajah & Vakeesan, Divagar, 2016. "Solar energy for future world: - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 1092-1105.
    13. Leong, Jun Xing & Daud, Wan Ramli Wan & Ghasemi, Mostafa & Liew, Kien Ben & Ismail, Manal, 2013. "Ion exchange membranes as separators in microbial fuel cells for bioenergy conversion: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 575-587.
    14. Belen Moreno Santamaria & Fernando del Ama Gonzalo & Benito Lauret Aguirregabiria & Juan A. Hernandez Ramos, 2020. "Experimental Validation of Water Flow Glazing: Transient Response in Real Test Rooms," Sustainability, MDPI, vol. 12(14), pages 1-24, July.
    15. Meryem Sena Akkus, 2022. "Investigation of Hydrogen Production Performance Using Nanoporous NiCr and NiV Alloys in KBH 4 Hydrolysis," Energies, MDPI, vol. 15(24), pages 1-15, December.
    16. Johannes Karlsson & Anders Grauers, 2023. "Agent-Based Investigation of Charger Queues and Utilization of Public Chargers for Electric Long-Haul Trucks," Energies, MDPI, vol. 16(12), pages 1-25, June.
    17. Defne, Zafer & Haas, Kevin A. & Fritz, Hermann M. & Jiang, Lide & French, Steven P. & Shi, Xuan & Smith, Brennan T. & Neary, Vincent S. & Stewart, Kevin M., 2012. "National geodatabase of tidal stream power resource in USA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3326-3338.
    18. Manimaran, Rajayokkiam & Mohanraj, Thangavelu & Venkatesan, Moorthy & Ganesan, Rajamohan & Balasubramanian, Dhinesh, 2022. "A computational technique for prediction and optimization of VCR engine performance and emission parameters fuelled with Trichosanthes cucumerina biodiesel using RSM with desirability function approac," Energy, Elsevier, vol. 254(PB).
    19. Awad, Omar I. & Ali, Obed M. & Mamat, Rizalman & Abdullah, A.A. & Najafi, G. & Kamarulzaman, M.K. & Yusri, I.M. & Noor, M.M., 2017. "Using fusel oil as a blend in gasoline to improve SI engine efficiencies: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1232-1242.
    20. Bellekom, Sandra & Benders, René & Pelgröm, Steef & Moll, Henk, 2012. "Electric cars and wind energy: Two problems, one solution? A study to combine wind energy and electric cars in 2020 in The Netherlands," Energy, Elsevier, vol. 45(1), pages 859-866.

    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:gam:jeners:v:14:y:2021:i:4:p:1122-:d:502692. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.