Proxy quality control of biomass particles using thermogravimetric analysis and Gaussian process regression models.
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| Title: | Proxy quality control of biomass particles using thermogravimetric analysis and Gaussian process regression models. |
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| Authors: | Watts, Jordan1,2 (AUTHOR), Potter, Adam1 (AUTHOR), Mohan, Vidyut1 (AUTHOR), Kumari, Pratima3 (AUTHOR), Thengane, Sonal K.4 (AUTHOR), Sokhansanj, Shahabaddine5 (AUTHOR), Cao, Yankai5 (AUTHOR), Kung, Kevin S.1,5 (AUTHOR) kevin@takachar.com |
| Source: | Biofuels, Bioproducts & Biorefining. Sep2023, Vol. 17 Issue 5, p1274-1289. 16p. |
| Subject Terms: | *Kriging, *Machine learning, *Thermogravimetry, *Quality control, *Regression analysis, *Combustion kinetics |
| Abstract: | The temperature experienced by reactants during preparation in a reactor is a key component in determining the yield and homogeneity of usable chemical products such as biomass particles. Thermocouples with sensors can be used to monitor spatial temperature gradients within reactors but these sensors are often too expensive and/or invasive. The present work proposes a strategy to identify optimal machine learning models to infer the maximum effective temperature experienced by particles during oxidative biomass torrefaction using key thermochemical combustion parameters. The maximum rate of weight loss, the corresponding temperature, and fixed carbon content on a dry‐ash‐free basis are used as literature‐based predictor variables obtained from thermogravimetric analysis. The evaluation of 24 machine‐learning models using the standard tenfold cross‐validation method suggests that the exponential Gaussian process regression (GPR) model is the most effective, followed by other GPR models. These high‐performing GPR models were also utilized to predict the effective preparation temperature distribution of reactor‐produced biomass particles under eight conditions of varying residence time and air‐to‐biomass ratio. The effective preparation temperature and residence time of individual biomass particles were then encoded into the torrefaction severity factor and used to estimate the energy yield of the reactor output as a novel quality control method. © 2023 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 171349124 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Proxy quality control of biomass particles using thermogravimetric analysis and Gaussian process regression models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Watts%2C+Jordan%22">Watts, Jordan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Potter%2C+Adam%22">Potter, Adam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mohan%2C+Vidyut%22">Mohan, Vidyut</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kumari%2C+Pratima%22">Kumari, Pratima</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Thengane%2C+Sonal+K%2E%22">Thengane, Sonal K.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sokhansanj%2C+Shahabaddine%22">Sokhansanj, Shahabaddine</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cao%2C+Yankai%22">Cao, Yankai</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kung%2C+Kevin+S%2E%22">Kung, Kevin S.</searchLink><relatesTo>1,5</relatesTo> (AUTHOR)<i> kevin@takachar.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Biofuels%2C+Bioproducts+%26+Biorefining%22">Biofuels, Bioproducts & Biorefining</searchLink>. Sep2023, Vol. 17 Issue 5, p1274-1289. 16p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Kriging%22">Kriging</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Thermogravimetry%22">Thermogravimetry</searchLink><br />*<searchLink fieldCode="DE" term="%22Quality+control%22">Quality control</searchLink><br />*<searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Combustion+kinetics%22">Combustion kinetics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The temperature experienced by reactants during preparation in a reactor is a key component in determining the yield and homogeneity of usable chemical products such as biomass particles. Thermocouples with sensors can be used to monitor spatial temperature gradients within reactors but these sensors are often too expensive and/or invasive. The present work proposes a strategy to identify optimal machine learning models to infer the maximum effective temperature experienced by particles during oxidative biomass torrefaction using key thermochemical combustion parameters. The maximum rate of weight loss, the corresponding temperature, and fixed carbon content on a dry‐ash‐free basis are used as literature‐based predictor variables obtained from thermogravimetric analysis. The evaluation of 24 machine‐learning models using the standard tenfold cross‐validation method suggests that the exponential Gaussian process regression (GPR) model is the most effective, followed by other GPR models. These high‐performing GPR models were also utilized to predict the effective preparation temperature distribution of reactor‐produced biomass particles under eight conditions of varying residence time and air‐to‐biomass ratio. The effective preparation temperature and residence time of individual biomass particles were then encoded into the torrefaction severity factor and used to estimate the energy yield of the reactor output as a novel quality control method. © 2023 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=171349124 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/bbb.2504 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1274 Subjects: – SubjectFull: Kriging Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Thermogravimetry Type: general – SubjectFull: Quality control Type: general – SubjectFull: Regression analysis Type: general – SubjectFull: Combustion kinetics Type: general Titles: – TitleFull: Proxy quality control of biomass particles using thermogravimetric analysis and Gaussian process regression models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Watts, Jordan – PersonEntity: Name: NameFull: Potter, Adam – PersonEntity: Name: NameFull: Mohan, Vidyut – PersonEntity: Name: NameFull: Kumari, Pratima – PersonEntity: Name: NameFull: Thengane, Sonal K. – PersonEntity: Name: NameFull: Sokhansanj, Shahabaddine – PersonEntity: Name: NameFull: Cao, Yankai – PersonEntity: Name: NameFull: Kung, Kevin S. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 1932104X Numbering: – Type: volume Value: 17 – Type: issue Value: 5 Titles: – TitleFull: Biofuels, Bioproducts & Biorefining Type: main |
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