Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process

Iftikhar Ahmad, Ahsan Ayub, Uzair Ibrahim, Mansoor Khan Khattak and Manabu Kano
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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

Energies, 2018, vol. 12, issue 1, 1-13

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.

Keywords: biodiesel; machine learning; ensemble learning; boosting; uncertainty analysis; polynomial chaos expansion (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
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