IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2018i1p63-d193181.html
   My bibliography  Save this article

Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process

Author

Listed:
  • Iftikhar Ahmad

    (Department of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Ahsan Ayub

    (US Pakistan Center for Advanced Studies in Energy, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Uzair Ibrahim

    (Department of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Mansoor Khan Khattak

    (Department of Agricultural Mechanization, The University of Agriculture Peshawar, Peshawar 25000, Pakistan)

  • Manabu Kano

    (Department of Systems Science, Kyoto University, Kyoto 606-8501, Japan)

Abstract

Biodiesel production is a field of outstanding prospects due to the renewable nature of its feedstock and little to no overall CO 2 emissions to the environment. Data-based soft sensors are used in realizing stable and efficient operation of biodiesel production. However, the conventional data-based soft sensors cannot grasp the effect of process uncertainty on the process outcomes. In this study, a framework of data-based soft sensors was developed using ensemble learning method, i.e., boosting, for prediction of composition, quantity, and quality of product, i.e., fatty acid methyl esters (FAME), in biodiesel production process from vegetable oil. The ensemble learning method was integrated with the polynomial chaos expansion (PCE) method to quantify the effect of uncertainties in process variables on the target outcomes. The proposed modeling framework is highly accurate in prediction of the target outcomes and quantification of the effect of process uncertainty.

Suggested Citation

  • Iftikhar Ahmad & Ahsan Ayub & Uzair Ibrahim & Mansoor Khan Khattak & Manabu Kano, 2018. "Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process," Energies, MDPI, vol. 12(1), pages 1-13, December.
    • Handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:63-:d:193181
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/1/63/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/1/63/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Igor Linkov & Dmitriy Burmistrov, 2003. "Model Uncertainty and Choices Made by Modelers: Lessons Learned from the International Atomic Energy Agency Model Intercomparisons," Risk Analysis, John Wiley & Sons, vol. 23(6), pages 1297-1308, December.
    2. Helton, J.C. & Johnson, J.D. & Sallaberry, C.J. & Storlie, C.B., 2006. "Survey of sampling-based methods for uncertainty and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1175-1209.
    3. Mostafaei, Mostafa & Javadikia, Hossein & Naderloo, Leila, 2016. "Modeling the effects of ultrasound power and reactor dimension on the biodiesel production yield: Comparison of prediction abilities between response surface methodology (RSM) and adaptive neuro-fuzzy," Energy, Elsevier, vol. 115(P1), pages 626-636.
    4. Atadashi, I.M. & Aroua, M.K. & Aziz, A.R. Abdul & Sulaiman, N.M.N., 2011. "Refining technologies for the purification of crude biodiesel," Applied Energy, Elsevier, vol. 88(12), pages 4239-4251.
    5. Oladyshkin, S. & Nowak, W., 2012. "Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 179-190.
    6. Atadashi, I.M. & Aroua, M.K. & Abdul Aziz, A.R. & Sulaiman, N.M.N., 2012. "Production of biodiesel using high free fatty acid feedstocks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3275-3285.
    Full references (including those not matched with items on IDEAS)

    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. Singh, Bhaskar & Guldhe, Abhishek & Rawat, Ismail & Bux, Faizal, 2014. "Towards a sustainable approach for development of biodiesel from plant and microalgae," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 216-245.
    2. Aneta Sienkiewicz & Alicja Piotrowska-Niczyporuk & Andrzej Bajguz, 2020. "Fatty Acid Methyl Esters from the Herbal Industry Wastes as a Potential Feedstock for Biodiesel Production," Energies, MDPI, vol. 13(14), pages 1-21, July.
    3. Zhou, Yuekuan & Zheng, Siqian, 2020. "Uncertainty study on thermal and energy performances of a deterministic parameters based optimal aerogel glazing system using machine-learning method," Energy, Elsevier, vol. 193(C).
    4. Helton, Jon C. & Johnson, Jay D. & Sallaberry, Cédric J., 2011. "Quantification of margins and uncertainties: Example analyses from reactor safety and radioactive waste disposal involving the separation of aleatory and epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1014-1033.
    5. Plischke, Elmar & Borgonovo, Emanuele, 2019. "Copula theory and probabilistic sensitivity analysis: Is there a connection?," European Journal of Operational Research, Elsevier, vol. 277(3), pages 1046-1059.
    6. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    7. Saurbayeva, Assemgul & Memon, Shazim Ali & Kim, Jong, 2023. "Integrated multi-stage sensitivity analysis and multi-objective optimization approach for PCM integrated residential buildings in different climate zones," Energy, Elsevier, vol. 278(PB).
    8. 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.
    9. Donghyeon Yoo & Jinhwan Park & Jaemin Moon & Changwan Kim, 2021. "Reliability-Based Design Optimization for Reducing the Performance Failure and Maximizing the Specific Energy of Lithium-Ion Batteries Considering Manufacturing Uncertainty of Porous Electrodes," Energies, MDPI, vol. 14(19), pages 1-15, September.
    10. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    11. Javier Urquizo & Carlos Calderón & Philip James, 2017. "Using a Local Framework Combining Principal Component Regression and Monte Carlo Simulation for Uncertainty and Sensitivity Analysis of a Domestic Energy Model in Sub-City Areas," Energies, MDPI, vol. 10(12), pages 1-22, December.
    12. Shang, Xiaobing & Su, Li & Fang, Hai & Zeng, Bowen & Zhang, Zhi, 2023. "An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    13. Cao, Jiaokun & Du, Farong & Ding, Shuiting, 2013. "Global sensitivity analysis for dynamic systems with stochastic input processes," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 106-117.
    14. Guo, Zehua & Dailey, Ryan & Feng, Tangtao & Zhou, Yukun & Sun, Zhongning & Corradini, Michael L & Wang, Jun, 2021. "Uncertainty analysis of ATF Cr-coated-Zircaloy on BWR in-vessel accident progression during a station blackout," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    15. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost," Energy, Elsevier, vol. 192(C).
    16. 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.
    17. Jingwen Song & Zhenzhou Lu & Pengfei Wei & Yanping Wang, 2015. "Global sensitivity analysis for model with random inputs characterized by probability-box," Journal of Risk and Reliability, , vol. 229(3), pages 237-253, June.
    18. Murphy, Fionnuala & Devlin, Ger & Deverell, Rory & McDonnell, Kevin, 2014. "Potential to increase indigenous biodiesel production to help meet 2020 targets – An EU perspective with a focus on Ireland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 154-170.
    19. Rehman, Hassam ur & Hirvonen, Janne & Sirén, Kai, 2017. "A long-term performance analysis of three different configurations for community-sized solar heating systems in high latitudes," Renewable Energy, Elsevier, vol. 113(C), pages 479-493.
    20. Auder, Benjamin & De Crecy, Agnès & Iooss, Bertrand & Marquès, Michel, 2012. "Screening and metamodeling of computer experiments with functional outputs. Application to thermal–hydraulic computations," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 122-131.

    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:12:y:2018:i:1:p:63-:d:193181. 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.