Role of optimisation method on kinetic inverse modelling of biomass pyrolysis at the microscale

Purnomo, Dwi; Richter, F.; Bonner, Matthew; Vaidyanathan, Ravi; Rein, Guillermo

doi:10.1016/j.fuel.2019.116251

Cited by 40 publications

(14 citation statements)

References 33 publications

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“…Many objective functions have been employed in previous studies for inverse modeling problems. 33 Bustamante Valencia tested and compared different objective functions, and then, he developed a new objective function considering the phase difference and distance error between curves. This objective function can be expressed as follows where and are vectors of experimental and estimated mass loss rate (MLR) as a function of temperature.…”

Section: Experimental and Methodsmentioning

confidence: 99%

Inverse Modeling of Thermal Decomposition of Flame-Retardant PET Fiber with Model-Free Coupled with Particle Swarm Optimization Algorithm

Wang

Zhao

et al. 2021

ACS Omega

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The thermal decomposition model of flame-retardant polyethylene terephthalate (FRPET) fiber is essential for predicting its fire behavior and do relevant fire simulation. In this work, the thermal decomposition character of FRPET is investigated via thermogravimetric analysis at four heating rates. Two kinetic schemes are proposed based on the analysis of experimental data and model-free methods. The model-free methods (Friedman and advanced Vyazovkin methods) are employed to determine possible search range for particle swarm optimization algorithm with constriction factor (CFPSO). Thus, this coupled method could evaluate the kinetic parameters for two proposed schemes without initial guess. Both models could reasonably predict the experimental data with obtained parameters, and the second two-step consecutive model shows better performance. The performance of CFPSO on the second model is further compared with improved generalized simulated annealing algorithm, and CFPSO was found to be more effective. Furthermore, global sensitivity analysis was conducted via the Sobol method to investigate the influence of kinetic parameters for the second model on predicted results. The most influential parameters are ln A and E α of the second reaction and reaction order n of the third reaction.

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Section: Experimental and Methodsmentioning

confidence: 99%

Inverse Modeling of Thermal Decomposition of Flame-Retardant PET Fiber with Model-Free Coupled with Particle Swarm Optimization Algorithm

Wang

Zhao

et al. 2021

ACS Omega

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“…The adequacy of the results can many times be attributed to the way the experimental data are fitted to the model to extract the relevant constants. Indeed, the kinetic models are optimized with data obtained at a scale free of transport processes (≈mg typically TGA scale) and using inverse methods, which can only partially guarantee the good results obtained (for more details about inverse modeling, see [15,48]). In the case of fire models, data fits used to optimize kinetic models will be used under conditions that drastically differ from those of instruments, such as a TGA.…”

Section: Applying the Kinetic Model To A Solid Reactionmentioning

confidence: 99%

Origin and Justification of the Use of the Arrhenius Relation to Represent the Reaction Rate of the Thermal Decomposition of a Solid

et al. 2021

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Degradation models are commonly used to describe the generation of combustible gases when predicting fire behavior. A model may include many sub-models, such as heat and mass transfer models, pyrolysis models or mechanical models. The pyrolysis sub-models require the definition of a decomposition mechanism and the associated reaction rates. Arrhenius-type equations are commonly used to quantify the reaction rates. Arrhenius-type equations allow the representation of chemical decomposition as a function of temperature. This representation of the reaction rate originated from the study of gas-phase reactions, but it has been extrapolated to liquid and solid decomposition. Its extension to solid degradation needs to be justified because using an Arrhenius-type formulation implies important simplifications that are potentially questionable. This study describes these simplifications and their potential consequences when it comes to the quantification of solid-phase reaction rates. Furthermore, a critical analysis of the existing thermal degradation models is presented to evaluate the implications of using an Arrhenius-type equation to quantify mass-loss rates and gaseous fuel production for fire predictions.

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“…The inverse modelling was conducted using the evolutionary algorithm AMALGAM [17]. AMALGAM has been used to study the kinetics of different materials [16] and is particularly suitable for the inverse modelling of multi-step reaction schemes [18]. Eq.…”

Section: Inverse Modelling Of Microscale Experimentsmentioning

confidence: 99%

A multi-step reaction scheme to simulate self-heating ignition of coal: Effects of oxygen adsorption and smouldering combustion

Han

Richter

Rein

2021

Proceedings of the Combustion Institute

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Role of optimisation method on kinetic inverse modelling of biomass pyrolysis at the microscale

Cited by 40 publications

References 33 publications

Inverse Modeling of Thermal Decomposition of Flame-Retardant PET Fiber with Model-Free Coupled with Particle Swarm Optimization Algorithm

Inverse Modeling of Thermal Decomposition of Flame-Retardant PET Fiber with Model-Free Coupled with Particle Swarm Optimization Algorithm

Origin and Justification of the Use of the Arrhenius Relation to Represent the Reaction Rate of the Thermal Decomposition of a Solid

A multi-step reaction scheme to simulate self-heating ignition of coal: Effects of oxygen adsorption and smouldering combustion

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