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. (15th March 2021)

• Record Type: Journal Article
• Authors: Perré, Patrick; Tian, Yong; Lu, Pin; Malinowska, Barbara; Bekri, Jamila El; Colin, Julien
• Is Part Of: Fuel. Volume 288(2021)
• Publication Date: 2021-03-15
• Subjects: Computational modeling -- Identification -- Poplar -- Prediction -- Spruce -- Validation; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2020.119692
• Languages: English
• ISSNs: 0016-2361

Abstract

Graphical abstract: Highlights: 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. Abstract: 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.