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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Veröffentlicht in:Fuel (Guildford) 2021-03, Vol.288, p.119692, Article 119692
Hauptverfasser: Perré, Patrick, Tian, Yong, Lu, Pin, Malinowska, Barbara, Bekri, Jamila El, Colin, Julien
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container_issue
container_start_page 119692
container_title Fuel (Guildford)
container_volume 288
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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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. 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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. 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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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source Elsevier ScienceDirect Journals Complete
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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