Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process
Biodiesel production is a field of outstanding prospects due to the renewable nature of its feedstock and little to no overall CO2 emissions to the environment. Data-based soft sensors are used in realizing stable and efficient operation of biodiesel production. However, the conventional data-based...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-12-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/12/1/63 |
| _version_ | 1798041405865590784 |
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| author | Iftikhar Ahmad Ahsan Ayub Uzair Ibrahim Mansoor Khan Khattak Manabu Kano |
| author_facet | Iftikhar Ahmad Ahsan Ayub Uzair Ibrahim Mansoor Khan Khattak Manabu Kano |
| author_sort | Iftikhar Ahmad |
| collection | DOAJ |
| description | Biodiesel production is a field of outstanding prospects due to the renewable nature of its feedstock and little to no overall CO2 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. |
| first_indexed | 2024-04-11T22:21:05Z |
| format | Article |
| id | doaj.art-8ad9ac90499848e680ab404ea50968a3 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T22:21:05Z |
| publishDate | 2018-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-8ad9ac90499848e680ab404ea50968a32022-12-22T04:00:07ZengMDPI AGEnergies1996-10732018-12-011216310.3390/en12010063en12010063Data-Based Sensing and Stochastic Analysis of Biodiesel Production ProcessIftikhar Ahmad0Ahsan Ayub1Uzair Ibrahim2Mansoor Khan Khattak3Manabu Kano4Department of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, PakistanUS Pakistan Center for Advanced Studies in Energy, National University of Sciences and Technology, Islamabad 44000, PakistanDepartment of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, PakistanDepartment of Agricultural Mechanization, The University of Agriculture Peshawar, Peshawar 25000, PakistanDepartment of Systems Science, Kyoto University, Kyoto 606-8501, JapanBiodiesel production is a field of outstanding prospects due to the renewable nature of its feedstock and little to no overall CO2 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.http://www.mdpi.com/1996-1073/12/1/63biodieselmachine learningensemble learningboostinguncertainty analysispolynomial chaos expansion |
| spellingShingle | Iftikhar Ahmad Ahsan Ayub Uzair Ibrahim Mansoor Khan Khattak Manabu Kano Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process Energies biodiesel machine learning ensemble learning boosting uncertainty analysis polynomial chaos expansion |
| title | Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process |
| title_full | Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process |
| title_fullStr | Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process |
| title_full_unstemmed | Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process |
| title_short | Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process |
| title_sort | data based sensing and stochastic analysis of biodiesel production process |
| topic | biodiesel machine learning ensemble learning boosting uncertainty analysis polynomial chaos expansion |
| url | http://www.mdpi.com/1996-1073/12/1/63 |
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