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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
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    References listed on IDEAS

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    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.
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