Practical observations on the use of Shuffled Complex Evolution (SCE) algorithm for kinetic parameters estimation in pyrolysis modeling

Hasalová, Lucie; Ira, Jiří; Jahoda, M.

doi:10.1016/j.firesaf.2016.01.007

Cited by 25 publications

(10 citation statements)

References 22 publications

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“…In addition, this reaction order can also change according to the reaction mechanism function. Reaction order larger than 3 and lower than 1 cases can also be found in other pyrolysis literatures [ 19 , 20 , 21 ]. The kinetics results between Kissinger method (model free) and genetic algorithm method have inconsistent results because model free method is the apparent kinetics result and the genetic algorithm result is step reaction results.…”

Section: Resultssupporting

confidence: 73%

Pyrolysis Kinetics and Flammability Evaluation of Rigid Polyurethane with Different Isocyanate Content

2021

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Polyurethane (PU) is a typical product of the reaction between isocyanate and polyol, whose ratio would greatly influence material properties. In this paper, to investigate the influence of isocyanate on PU thermal stability and flammability, three kinds of rigid polyurethanes (RPUs) with different isocyanate ratio (1.05, 1.1, and 2.0) were manufactured in a laboratory and employed to have a series of TG (thermogravimetry), DSC (differential scanning calorimetry), and cone calorimetry tests. Kissinger's method was used to calculate the activation energy and judge their stabilities. However, for such a complex degradation which consists of five reactions, it does not make sense by Kissinger method to obtain only two peak active energies. Considering complexity of PU degradation in air, genetic algorithm (GA) was employed to calculate kinetic triplets of five sub-reactions. The effects of isocyanate contents on each sub-reaction stability were obtained and then analyzed. By cone calorimeter testing, we found that great differences in heat release rate data. However, DSC analysis showed a complete opposite changed trend. Such difference is caused by DSC and calorimeter’s sample morphology, the former using grinded polyurethane powders but the latter polyurethane foam block.

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Section: Resultssupporting

confidence: 73%

Pyrolysis Kinetics and Flammability Evaluation of Rigid Polyurethane with Different Isocyanate Content

2021

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“…Given the results of [42], where the reaction scheme is concluded to be as simple as possible, but taking into account a certain level of complexity that allows the model to represent the decomposition process, this study employed an approach similar to that proposed in [36], where the cardboard undergoes a four-reaction decomposition process (non-competitive reactions), creating three intermediate fictitious materials and a final residue. Each reaction produces a residue and releases burning gases, following the same idea as that proposed in [20,43,44]. In FDS, the generic reaction can be defined as…”

Section: Creation Of the Reference Casementioning

confidence: 99%

“…Because most of the input thermal and kinetic properties of fire computer models cannot be obtained directly from thermal analysis tests, a numerical approaching method combined with a pyrolysis model will enable us to obtain the numerical values of all variables. This methodology has been extensively applied in the recent years to estimate the kinetic properties in an STA test in [20,45,46] and in bench-scale tests such as fire propagation apparatus (FPA) and cone calorimetric [8,47,48]. The most widely used mathematical methods include genetic algorithms [6,49,50], stochastic hill-climber method [48,51], and shuffle complex evolution (SCE) [8,20,52].…”

Section: Creation Of the Reference Casementioning

confidence: 99%

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Assessing the influence of the input variables employed by fire dynamics simulator (FDS) software to model numerically solid-phase pyrolysis of cardboard

Alonso

Lázaro

et al. 2019

J Therm Anal Calorim

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Understanding a material's fire behaviour implies to know the thermal decomposition processes. Thermal analysis techniques are widely employed to study thermal decomposition processes, especially to calculate the kinetic and thermal properties. Cardboard boxes are widely employed as rack-storage commodities in industrial buildings. Hence, the characterization of the cardboard is considered a key factor for fire safety engineering, because it enables the determination of its thermal behaviour at high temperatures. The employment of mathematical or computational models for modelling the thermal decomposition processes is commonly used in fire safety engineering (FSE). The fire dynamics simulator (FDS) software is one of the most commonly used computational fluid dynamics software in FSE to address thermal analysis. To properly set up FDS and obtain accurate results, the numerical values of the thermal and kinetic properties are needed as input data. Owing to the large number of variables to be determined, a preliminary study is bound to be helpful, which can well-assess the influence of each variable over the pyrolysis model, discarding or restricting their influence. This study, based on the Monte Carlo method, presents a sensitivity analysis for the variables utilized as input data by the FDS software. The results show the conversion factor α, i.e. the mass involved in each reaction, and the triplet kinetic has a major impact on the reproduction of the thermal decomposition process in fire computer modelling.

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“…By employing these techniques, the SCE-UA algorithm provides a robust optimization framework and has shown numerically to be competitive and efficient comparing to other algorithms, such as GA, for calibrating rainfall-runoff models (Beven, 2011;Gan and Biftu, 1996;Wagener et al, 2004;Wang et al, 2010). The SCE-UA algorithm has been widely used in water resources management (Barati et al, 2014;Eckhardt andArnold, 2001, K. Ajami et al, 2004;Lin et al, 2006;Liong and Atiquzzaman, 2004;Madsen, 2000;Sorooshian et al, 1993;Toth et al, 2000;Yang et al, 2015;Yapo et al, 1996), as well as other fields of study, such as pyrolysis modeling (Ding et al, 2016;Hasalov a et al, 2016) and Artificial Intelligence (Yang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework

Naeini

Yang

Sadegh

et al. 2018

Environmental Modelling & Software

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Simplicity and flexibility of meta-heuristic optimization algorithms have attracted lots of attention in the field of optimization. Different optimization methods, however, hold algorithm-specific strengths and limitations, and selecting the best-performing algorithm for a specific problem is a tedious task. We introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme. SC-SAHEL explores performance of different EAs, such as the capability to escape local attractions, speed, convergence, etc., during population evolution as each individual EA suits differently to various response surfaces. The SC-SAHEL algorithm is benchmarked over 29 conceptual test functions, and a real-world hydropower reservoir model case study. Results show that the hybrid SC-SAHEL algorithm is rigorous and effective in finding global optimum for a majority of test cases, and that it is computationally efficient in comparison to algorithms with individual EA.

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Practical observations on the use of Shuffled Complex Evolution (SCE) algorithm for kinetic parameters estimation in pyrolysis modeling

Cited by 25 publications

References 22 publications

Pyrolysis Kinetics and Flammability Evaluation of Rigid Polyurethane with Different Isocyanate Content

Pyrolysis Kinetics and Flammability Evaluation of Rigid Polyurethane with Different Isocyanate Content

Assessing the influence of the input variables employed by fire dynamics simulator (FDS) software to model numerically solid-phase pyrolysis of cardboard

Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework

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