Validation of MATLAB algorithm to implement a two‑step parallel pyrolysis model for the prediction of maximum %char yield (original) (raw)
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Biofuel Research Journal
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Experimental and Modelling Studies of Biomass Pyrolysis
Chinese Journal of Chemical Engineering, 2012
The analysis on the feedstock pyrolysis characteristic and the impacts of process parameters on pyrolysis outcomes can assist in the designing, operating and optimizing pyrolysis processes. This work aims to utilize both experimental and modelling approaches to perform the analysis on three biomass feedstocks wood sawdust, bamboo shred and Jatropha Curcas seed cake residue, and to provide insights for the design and operation of pyrolysis processes. For the experimental part, the study investigated the effect of heating rate, final pyrolysis temperature and sample size on pyrolysis using common thermal analysis techniques. For the modelling part, a transient mathematical model that integrates the feedstock characteristic from the experimental study was used to simulate the pyrolysis progress of selected biomass feedstock particles for reactor scenarios. The model composes of several sub-models that describe pyrolysis kinetic and heat flow, particle heat transfer, particle shrinking and reactor operation. With better understanding of the effects of process conditions and feedstock characteristics on pyrolysis through both experimental and modelling studies, this work discusses on the considerations of and interrelation between feedstock size, pyrolysis energy usage, processing time and product quality for the design and operation of pyrolysis processes.
Fuel, 2021
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.
Characterization and prediction of biomass pyrolysis products
Progress in Energy and Combustion Science, 2011
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Biomass Fast Pyrolysis: Experimental Analysis and Modeling Approach †
Energy & Fuels, 2010
A single particle model able to predict the evolution of the products yields during biomass fast pyrolysis is developed. Mass balances equations based on a kinetic scheme of solid phase pyrolysis are coupled to heat transfer phenomena. This model is solved by the finite volume method. The model results are compared to experimental data obtained in an image furnace where biomass pellets are submitted to a controlled and concentrated radiation. Heat fluxes used are similar to those encountered in fluidized bed (0.2 -0.8 x 10 6 W.m -2 ). The comparison between the experimental and simulated data shows that the model correctly simulates the time-evolution of the products formation, but the kinetic parameters should be optimized in order to represent precisely the final products yields.
Energies
Biomass pyrolysis is considered as a thermochemical conversion system that is performed under oxygen-depleted conditions. A large body of literature exists in which thermodynamic equilibrium (TE) and kinetic approaches have been applied to predict pyrolysis products. However, the reliability, accuracy and predictive power of both modeling approaches is an area of concern. To address these concerns, in this paper, two new simulation models based on the TE and kinetic approaches are developed using Aspen Plus, to analyze the performance of each approach. Subsequently, the results of two models are compared with modeling and experimental results available in the literature. The comparison shows that, on the one hand, the performance of the TE approach is not satisfactory and cannot be used as an effective way for pyrolysis modeling. On the other hand, the results generated by the new model based on the kinetic approach suggests that this approach is suitable for modeling biomass pyroly...
Numerical and experimental modelling of biomass pyrolysis with the depollution of pyrolysis products
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Modelling of Biomass Pyrolysis
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Pyrolysis is an essential preliminary step in a gasifier. The first step in modelling the pyrolysis process of biomass is creating a model for the chemical processes taking place. This model should describe the used fuel, the reactions taking place and the products created in the process. The numerous different polymers present in the organic fraction of the fuel are generally divided in three main groups. So, the multistep kinetic model of biomass pyrolysis is based on conventional multistep devolatilization models of the three main biomass components - cellulose, hemicelluloses, and lignin. Numerical simulations have been conducted in order to estimate the influence of the heating rate and the temperature of pyrolysis on the content of the virgin biomass, active biomass, liquid, solid and gaseous phases at any moment.