A robust and frugal model of biomass pyrolysis in the range 100–800 °C: Inverse analysis of DAEM parameters, validation on static tests and determination of heats of reaction
[Display omitted] •A DAEM model of pyrolysis identified on a database with a wide range of temperature and heating rates.•Choice of 3 distributions to get a frugal but robust model, validated on demanding isothermal tests.•Heats of reaction determined using a fast test to limit the drift of heat flu...
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creator | Perré, Patrick Tian, Yong Lu, Pin Malinowska, Barbara Bekri, Jamila El Colin, Julien |
description | [Display omitted]
•A DAEM model of pyrolysis identified on a database with a wide range of temperature and heating rates.•Choice of 3 distributions to get a frugal but robust model, validated on demanding isothermal tests.•Heats of reaction determined using a fast test to limit the drift of heat flux baseline.•Expressions proposed to predict elemental composition for any time-temperature pathway.•The potential of the model (kinetics + heats of reaction) to control the process is highlighted.
This paper presents a robust and frugal Distributed Activation Energy Model to simulate pyrolysis of lignocellulosic biomass (spruce and poplar) over a wide range of temperature and residence time. The learning database consists of dynamic TGA-DSC experiments performed up to 800 °C at four heating rates (1, 2, 5 and 10 K/min). By employing one non-symmetrical distribution, three distributions and only 9 independent parameters were needed to correctly fit the experimental data: a Gaussian distribution for hemicelluloses, a Gaussian function degenerated into a Dirac function for cellulose and a gamma function degenerated into an exponential function for lignins. The robustness of the model was successfully validated with 2-h isothermal tests (250 °C to 500 °C with increments of 50 °C). The heats of reaction were determined using the heat flux measured under fast dynamic conditions, thus reducing the crucial problem of baseline drift. The prediction potential of the model is highlighted by two examples: pathway in the Van Krevelen’s diagram and control of the temperature rise to limit the heat source due to reactions. The model equations, the discretization and computational implementation, as well as the complete set of model parameters are presented in great detail, so that the reader can use them for process modelling, including the crucial concern of thermal runaway occurring in large particles or packed beds. |
doi_str_mv | 10.1016/j.fuel.2020.119692 |
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•A DAEM model of pyrolysis identified on a database with a wide range of temperature and heating rates.•Choice of 3 distributions to get a frugal but robust model, validated on demanding isothermal tests.•Heats of reaction determined using a fast test to limit the drift of heat flux baseline.•Expressions proposed to predict elemental composition for any time-temperature pathway.•The potential of the model (kinetics + heats of reaction) to control the process is highlighted.
This paper presents a robust and frugal Distributed Activation Energy Model to simulate pyrolysis of lignocellulosic biomass (spruce and poplar) over a wide range of temperature and residence time. The learning database consists of dynamic TGA-DSC experiments performed up to 800 °C at four heating rates (1, 2, 5 and 10 K/min). By employing one non-symmetrical distribution, three distributions and only 9 independent parameters were needed to correctly fit the experimental data: a Gaussian distribution for hemicelluloses, a Gaussian function degenerated into a Dirac function for cellulose and a gamma function degenerated into an exponential function for lignins. The robustness of the model was successfully validated with 2-h isothermal tests (250 °C to 500 °C with increments of 50 °C). The heats of reaction were determined using the heat flux measured under fast dynamic conditions, thus reducing the crucial problem of baseline drift. The prediction potential of the model is highlighted by two examples: pathway in the Van Krevelen’s diagram and control of the temperature rise to limit the heat source due to reactions. The model equations, the discretization and computational implementation, as well as the complete set of model parameters are presented in great detail, so that the reader can use them for process modelling, including the crucial concern of thermal runaway occurring in large particles or packed beds.</description><identifier>ISSN: 0016-2361</identifier><identifier>EISSN: 1873-7153</identifier><identifier>DOI: 10.1016/j.fuel.2020.119692</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Biomass ; Cellulose ; Computational modeling ; Computer applications ; Engineering Sciences ; Exponential functions ; Gamma function ; Gaussian distribution ; Heat flux ; Heat of reaction ; Hemicellulose ; Identification ; Lignocellulose ; Mathematical models ; Normal distribution ; Packed beds ; Parameters ; Poplar ; Prediction ; Pyrolysis ; Robustness ; Spruce ; Static tests ; Temperature ; Thermal runaway ; Validation</subject><ispartof>Fuel (Guildford), 2021-03, Vol.288, p.119692, Article 119692</ispartof><rights>2020 Elsevier Ltd</rights><rights>Copyright Elsevier BV Mar 15, 2021</rights><rights>Attribution - NonCommercial</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3212-b70035671eb3329415340d9618fe0ab80d977846181f8f742792b2d115fd73493</citedby><cites>FETCH-LOGICAL-c3212-b70035671eb3329415340d9618fe0ab80d977846181f8f742792b2d115fd73493</cites><orcidid>0000-0003-0419-4810 ; 0009-0006-6939-3991</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.fuel.2020.119692$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,780,784,885,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://hal.science/hal-03547145$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Perré, Patrick</creatorcontrib><creatorcontrib>Tian, Yong</creatorcontrib><creatorcontrib>Lu, Pin</creatorcontrib><creatorcontrib>Malinowska, Barbara</creatorcontrib><creatorcontrib>Bekri, Jamila El</creatorcontrib><creatorcontrib>Colin, Julien</creatorcontrib><title>A robust and frugal model of biomass pyrolysis in the range 100–800 °C: Inverse analysis of DAEM parameters, validation on static tests and determination of heats of reaction</title><title>Fuel (Guildford)</title><description>[Display omitted]
•A DAEM model of pyrolysis identified on a database with a wide range of temperature and heating rates.•Choice of 3 distributions to get a frugal but robust model, validated on demanding isothermal tests.•Heats of reaction determined using a fast test to limit the drift of heat flux baseline.•Expressions proposed to predict elemental composition for any time-temperature pathway.•The potential of the model (kinetics + heats of reaction) to control the process is highlighted.
This paper presents a robust and frugal Distributed Activation Energy Model to simulate pyrolysis of lignocellulosic biomass (spruce and poplar) over a wide range of temperature and residence time. The learning database consists of dynamic TGA-DSC experiments performed up to 800 °C at four heating rates (1, 2, 5 and 10 K/min). By employing one non-symmetrical distribution, three distributions and only 9 independent parameters were needed to correctly fit the experimental data: a Gaussian distribution for hemicelluloses, a Gaussian function degenerated into a Dirac function for cellulose and a gamma function degenerated into an exponential function for lignins. The robustness of the model was successfully validated with 2-h isothermal tests (250 °C to 500 °C with increments of 50 °C). The heats of reaction were determined using the heat flux measured under fast dynamic conditions, thus reducing the crucial problem of baseline drift. The prediction potential of the model is highlighted by two examples: pathway in the Van Krevelen’s diagram and control of the temperature rise to limit the heat source due to reactions. The model equations, the discretization and computational implementation, as well as the complete set of model parameters are presented in great detail, so that the reader can use them for process modelling, including the crucial concern of thermal runaway occurring in large particles or packed beds.</description><subject>Biomass</subject><subject>Cellulose</subject><subject>Computational modeling</subject><subject>Computer applications</subject><subject>Engineering Sciences</subject><subject>Exponential functions</subject><subject>Gamma function</subject><subject>Gaussian distribution</subject><subject>Heat flux</subject><subject>Heat of reaction</subject><subject>Hemicellulose</subject><subject>Identification</subject><subject>Lignocellulose</subject><subject>Mathematical models</subject><subject>Normal distribution</subject><subject>Packed beds</subject><subject>Parameters</subject><subject>Poplar</subject><subject>Prediction</subject><subject>Pyrolysis</subject><subject>Robustness</subject><subject>Spruce</subject><subject>Static tests</subject><subject>Temperature</subject><subject>Thermal runaway</subject><subject>Validation</subject><issn>0016-2361</issn><issn>1873-7153</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kU2O1DAQhS0EEs3ABVhZYoVEGv8lThCbVjMwIzViA2vLicvTbiVxYzst9W7uwAU4AuIIcxROgjMZsUSy5KryV0-uegi9pGRNCa3eHtZ2gn7NCMsF2lQNe4RWtJa8kLTkj9GKZKpgvKJP0bMYD4QQWZdihX5vcPDtFBPWo8E2TDe6x4M30GNvcev8oGPEx3Pw_Tm6iN2I0x5w0OMNYErIn9sfNSF3P-9-bd_h6_EEIUKW0gudJT5sLj_jow56gJQf3-CT7p3RyfkR5xNTDjucIKZ4_wUzY4MbHwiL96DTvVIA3c3F5-iJ1X2EFw_3Bfr28fLr9qrYffl0vd3sio4zyopWEsLLSlJoOWeNyHsQxDQVrS0Q3dY5lrIWOae2tlIw2bCWGUpLayQXDb9Arxfdve7VMbhBh7Py2qmrzU7NtSwvJBXliWb21cIeg_8-5WHUwU8hryEqJuqGsKoWPFNsobrgYwxg_8lSomYf1UHNPqrZR7X4mJveL02QZz05CCp2DsYOjAvQJWW8-1_7X3Pip0g</recordid><startdate>20210315</startdate><enddate>20210315</enddate><creator>Perré, Patrick</creator><creator>Tian, Yong</creator><creator>Lu, Pin</creator><creator>Malinowska, Barbara</creator><creator>Bekri, Jamila El</creator><creator>Colin, Julien</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><general>Elsevier</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0003-0419-4810</orcidid><orcidid>https://orcid.org/0009-0006-6939-3991</orcidid></search><sort><creationdate>20210315</creationdate><title>A robust and frugal model of biomass pyrolysis in the range 100–800 °C: Inverse analysis of DAEM parameters, validation on static tests and determination of heats of reaction</title><author>Perré, Patrick ; Tian, Yong ; Lu, Pin ; Malinowska, Barbara ; Bekri, Jamila El ; Colin, Julien</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3212-b70035671eb3329415340d9618fe0ab80d977846181f8f742792b2d115fd73493</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Biomass</topic><topic>Cellulose</topic><topic>Computational modeling</topic><topic>Computer applications</topic><topic>Engineering Sciences</topic><topic>Exponential functions</topic><topic>Gamma function</topic><topic>Gaussian distribution</topic><topic>Heat flux</topic><topic>Heat of reaction</topic><topic>Hemicellulose</topic><topic>Identification</topic><topic>Lignocellulose</topic><topic>Mathematical models</topic><topic>Normal distribution</topic><topic>Packed beds</topic><topic>Parameters</topic><topic>Poplar</topic><topic>Prediction</topic><topic>Pyrolysis</topic><topic>Robustness</topic><topic>Spruce</topic><topic>Static tests</topic><topic>Temperature</topic><topic>Thermal runaway</topic><topic>Validation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Perré, Patrick</creatorcontrib><creatorcontrib>Tian, Yong</creatorcontrib><creatorcontrib>Lu, Pin</creatorcontrib><creatorcontrib>Malinowska, Barbara</creatorcontrib><creatorcontrib>Bekri, Jamila El</creatorcontrib><creatorcontrib>Colin, Julien</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Fuel (Guildford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Perré, Patrick</au><au>Tian, Yong</au><au>Lu, Pin</au><au>Malinowska, Barbara</au><au>Bekri, Jamila El</au><au>Colin, Julien</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A robust and frugal model of biomass pyrolysis in the range 100–800 °C: Inverse analysis of DAEM parameters, validation on static tests and determination of heats of reaction</atitle><jtitle>Fuel (Guildford)</jtitle><date>2021-03-15</date><risdate>2021</risdate><volume>288</volume><spage>119692</spage><pages>119692-</pages><artnum>119692</artnum><issn>0016-2361</issn><eissn>1873-7153</eissn><abstract>[Display omitted]
•A DAEM model of pyrolysis identified on a database with a wide range of temperature and heating rates.•Choice of 3 distributions to get a frugal but robust model, validated on demanding isothermal tests.•Heats of reaction determined using a fast test to limit the drift of heat flux baseline.•Expressions proposed to predict elemental composition for any time-temperature pathway.•The potential of the model (kinetics + heats of reaction) to control the process is highlighted.
This paper presents a robust and frugal Distributed Activation Energy Model to simulate pyrolysis of lignocellulosic biomass (spruce and poplar) over a wide range of temperature and residence time. The learning database consists of dynamic TGA-DSC experiments performed up to 800 °C at four heating rates (1, 2, 5 and 10 K/min). By employing one non-symmetrical distribution, three distributions and only 9 independent parameters were needed to correctly fit the experimental data: a Gaussian distribution for hemicelluloses, a Gaussian function degenerated into a Dirac function for cellulose and a gamma function degenerated into an exponential function for lignins. The robustness of the model was successfully validated with 2-h isothermal tests (250 °C to 500 °C with increments of 50 °C). The heats of reaction were determined using the heat flux measured under fast dynamic conditions, thus reducing the crucial problem of baseline drift. The prediction potential of the model is highlighted by two examples: pathway in the Van Krevelen’s diagram and control of the temperature rise to limit the heat source due to reactions. The model equations, the discretization and computational implementation, as well as the complete set of model parameters are presented in great detail, so that the reader can use them for process modelling, including the crucial concern of thermal runaway occurring in large particles or packed beds.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.fuel.2020.119692</doi><orcidid>https://orcid.org/0000-0003-0419-4810</orcidid><orcidid>https://orcid.org/0009-0006-6939-3991</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Biomass Cellulose Computational modeling Computer applications Engineering Sciences Exponential functions Gamma function Gaussian distribution Heat flux Heat of reaction Hemicellulose Identification Lignocellulose Mathematical models Normal distribution Packed beds Parameters Poplar Prediction Pyrolysis Robustness Spruce Static tests Temperature Thermal runaway Validation |
title | A robust and frugal model of biomass pyrolysis in the range 100–800 °C: Inverse analysis of DAEM parameters, validation on static tests and determination of heats of reaction |
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