Estimation of beech pyrolysis kinetic parameters by Shuffled Complex Evolution

Ding, Yanming; Wang, Changjian; Chaos, Marcos; Chen, Ruiyu; Lu, Shouxiang

doi:10.1016/j.biortech.2015.10.082

Cited by 102 publications

(36 citation statements)

References 30 publications

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“…Later, Lautenberger studied the performance of four optimization algorithms on synthetic data and concluded that the SCE algorithm gave the best results. Other researchers obtained the input properties of different materials through optimization using a similar approach as well. In general, the optimization algorithm has demonstrated, it can successfully determine the material properties that reproduce the input experimental data.…”

Section: Introductionmentioning

confidence: 99%

Methodology for material property determination

et al. 2019

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Summary Predicting solid material pyrolysis requires numerous parameters/properties that are time consuming and difficult to measure. Multiobjective optimization techniques have been used to determine these parameters; however, a methodology for the types of tests needed for the optimization and parameters that should be optimized versus measured has not been fully established. This study investigated combinations of testing protocols and parameters for optimization to determine a testing and optimization methodology that results in the accurate parameters. A Shuffled Complex Evolution (SCE) optimization routine was used to determine parameters based on some parameters being known (ie, measured) and others being determined through optimization and different combinations of types of input data. The results indicate that material testing should be done at three different heating rates in a thermogravimetric analyzer (TGA) and at three different heat fluxes in the cone calorimeter. The TGA tests and two cone calorimeter tests are input into the SCE optimization routine to determine the remaining parameters. The third cone calorimeter test is used to validate the parameters determined using the methodology. The method provides parameters within 20% of the actual parameters input into the Fire Dynamics Simulator (FDS) virtual experiment models. This method is applied to Polymethyl methacrylate (PMMA) experimental data to prove effectiveness.

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Section: Introductionmentioning

confidence: 99%

Methodology for material property determination

et al. 2019

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“…Carty et al[23] compared the thermal decomposition of one unplasticized CPVC and three plasticized CPVC. However, the above researches are not enough to reveal the pyrolysis dynamics of engineering plastic waste CPVC, so the aim of current paper is to explore its pyrolysis behaviors and obtained appropriate kinetic parameters.Knowledge of pyrolysis kinetics can help provide better understanding and planning of important industrial processes [24], such as their application in pyrolysis model [25] and direct combustion [26].Model-fitting and model-fitting methods are the common ways to explore the kinetic parameters. Model-fitting method consists of fitting different models to the experimental data for the best statistical fit but with the inability to determine the reaction model [27].…”

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confidence: 99%

Thermal Decomposition Mechanism and Kinetics Study of Plastic Waste Chlorinated Polyvinyl Chloride

et al. 2019

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Chlorinated polyvinyl chloride (CPVC), as a new type of engineering plastic waste, has been used widely due to its good heat resistance, mechanical properties and corrosion resistance, while it has become an important part of solid waste. The pyrolysis behaviors of CPVC waste were analyzed based on thermogravimetric experiments to explore its reaction mechanism. Compared with polyvinyl chloride (PVC) pyrolysis, CPVC pyrolysis mechanism was divided into two stages and speculated to be dominated by the dehydrochlorination and cyclization/aromatization processes. A common model-free method, Flynn-Wall-Ozawa method, was applied to estimate the activation energy values at different conversion rates. Meanwhile, a typical model-fitting method, Coats-Redfern method, was used to predict the possible reaction model by the comparison of activation energy obtained from model-free method, thereby the first order reaction-order model and fourth order reaction-order model were established corresponding to these two stages. Eventually, based on the initial kinetic parameter values computed by model-free method and reaction model established by model-fitting method, kinetic parameters were optimized by Shuffled Complex Evolution algorithm and further applied to predict the CPVC pyrolysis behaviors during the whole temperature range.

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“…A reliable description of the thermal degradation kinetics of cellulose is of interest for controlling the combustion processes and for the thermal characterisation of cellulose-reinforced biocomposites. Numerous studies based on the main components (cellulose, hemicellulose, and lignin) have been carried out, most focused on developing kinetic models for predicting the behaviour of biomass pyrolysis (Bradbury et al 1979;Antal and Varhegyi 1995;Várhegyi et al 1997;Orfão et al 1999;Manyà et al 2003;Yang et al 2004;Sfakiotakis and Vamvuka 2015;Ding et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Kinetics of Thermal Degradation of Cellulose: Analysis Based on Isothermal and Linear Heating Data

López-Beceiro¹,

Álvarez-García²,

Sebio-Puñal³

et al. 2016

BioResources

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In spite of the many studies performed, there is not yet a kinetic model to predict the thermal degradation of cellulose in isothermal and nonisothermal conditions for the full extent of conversion. A model proposed by the authors was tested on non-oxidising thermogravimetric data. The method consisted of initially fitting several isothermal and non-isothermal curves, then obtaining a critical temperature and an energy barrier from the set of fittings that resulted from different experimental conditions. While the critical temperature, approximately 226 °C, represented the minimum temperature for the degradation process, the degradation rate at a given temperature was related to both the critical temperature and the energy barrier. These results were compared with those observed in other materials. The quality of fittings obtained was superior to any other reported to date, and the results obtained from each single curve were in line with each other. The peak area. Represents the amount of sample involved in each transformation process, in linear heating conditions Fitting parameter, related to the peak shape in linear heating conditions. If =1, then is 4 times the maximum transformation rate per unit of sample mass KeywordsThe time elapsed from the beginning of the experiment to the instant where the maximum mass loss rate is observed, in linear heating experiments Fitting parameter related to the peak asymmetry ( =1 for a symmetric peak) yiso(t) Transformation rate, as a function of time, in isothermal conditionsThe peak area. Represents the amount of sample involved in each transformation process, in isothermal conditions

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Estimation of beech pyrolysis kinetic parameters by Shuffled Complex Evolution

Cited by 102 publications

References 30 publications

Methodology for material property determination

Methodology for material property determination

Thermal Decomposition Mechanism and Kinetics Study of Plastic Waste Chlorinated Polyvinyl Chloride

Kinetics of Thermal Degradation of Cellulose: Analysis Based on Isothermal and Linear Heating Data

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