Discuss the heat release capacity of polymer derived from microscale combustion calorimeter

Xu, Qiang; Jin, Cong; Majlingová, Andrea; Restás, Ágoston

doi:10.1007/s10973-017-6866-3

Cited by 23 publications

(15 citation statements)

References 14 publications

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“…The instrument has a readability of 0.01 mg and a weighing range of 81 g was used to weigh the samples. The material properties are listed in Table 1 [5,11].…”

Section: Methodsmentioning

confidence: 99%

“…Microscale combustion calorimetry (MCC) is a small-scale fire experiment operated on the principles of oxygen consumption calorimetry. MCC has received significant attention in recent years and remains the most commonly cited fire experiment for polymer flammability assessments due to the wealth of data measured from it [5,6]. Amongst the data is the heat release capacity (HRC), a flammability parameter and material property measured exclusively from the 'Method A' [6] of MCC.…”

Section: Introductionmentioning

confidence: 99%

“…HRC integrates thermal stability and combustion properties, hence rendering it the best predictor of a materials response to fire. HRC is defined as the ratio of the maximum value of the specific heat release rate to the average heating rate of a sample [5][6][7][8][9]. The total heat released (THR) is a measure from the total heat released when a sample undergoes a complete combustion in an oxygen atmosphere.…”

Section: Introductionmentioning

confidence: 99%

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Application of Adaptive Neuro-Fuzzy Inference System in Flammability Parameter Prediction

et al. 2020

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The fire behavior of materials is usually modeled on the basis of fire physics and material composition. However, significant strides have been made recently in applying soft computing methods such as artificial intelligence in flammability studies. In this paper, multiple linear regression (MLR) was employed to test the degree of non-linearities in flammability parameter modeling by assessing the linear relationship between sample mass, heating rate, heat release capacity (HRC) and total heat release (THR). Adaptive neuro-fuzzy inference system (ANFIS) was then adopted to predict the HRC and THR of the extruded polystyrene measured from microscale combustion calorimetry experiments. The ANFIS models presented excellent predictions, showing very low mean training and testing errors as well as reasonable agreements between experimental and predicted datasets. Hence, it can be inferred that ANFIS can handle the non-linearities in flammability modeling, making it apt as a modeling technique for accurate and effective flammability assessments.Several chemistry-based models, such as inverse modeling, quantitative structure-property relationship (QSPR), quantitative structure-activity relationship (QSAR) and additive molar group contribution methods, have been developed over the years to estimate these important fire safety parameters (HRC and THR). Although effective, some disadvantages, like large prediction errors, have been identified in the traditional modeling approaches and need to be addressed.Until now, correlation analysis and statistical prediction models have been appropriate analytical tools in material flammability [11]. The significance of the prediction models lies in the employment of robust techniques and strategies for the accurate estimation of flammability parameters, whereas correlation analysis assesses the relationship between the predicted or measured parameters. Statistical analysis is mostly empirical, devoid of the chemical compositions and physical structure of the material under consideration [12]. It is imperative to note that fire experiments are demanding, expensive and time consuming. Similarly, traditional flammability parameter predictions involve sophisticated fire modeling and calibration, requiring a great deal of expertise and computing power. It is, therefore, quite convenient for researchers to opt for these system theoretical models as opposed to conceptual models. Statistical models in recent times largely embrace the artificial neural network (ANN) due to its ability to capture complex nonlinearities in a system when compared to linear regression methods. ANN mimics the operation of the human brain by processing information available to the input layers to achieve a desirable output.Generalized regression and the ANN's feed-forward back propagation methods have been applied in flammability studies to predict peak heat release rate, heat release capacity, total heat released, etc., with a high level of accuracy [13,14]. Deviation of the predicted results from the actual or ex...

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“…The instrument has a readability of 0.01 mg and a weighing range of 81 g was used to weigh the samples. The material properties are listed in Table 1 [5,11].…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Adaptive Neuro-Fuzzy Inference System in Flammability Parameter Prediction

et al. 2020

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“…The optimized model equation is the weighted sum of the "basis functions" as expressed in Equation. (12). A regression equation (13) for predicting pHRR of PMMA was generated as a function of (β, HRC, pTemp) from…”

Section: Fig 1 Curves Of Plotted Specific Heat Release Rate Versus mentioning

confidence: 99%

“…From the combustion outcomes, innumerable flammability characteristics and material properties are measured with ASTM D7309-13 standards, which includes, heat release rate (HRR), peak pyrolysis temperature (Tp), total heat released (THR), and heat release capacity (HRC) [6,7,2,8]. Several researchers [9][10][11][12]13] have performed and used MCC experimental results to screen and evaluate the flammability of pure polymers. It is worth affirming that the database obtained from MCC experiments can be modelled to predict the flammability and fire safety parameters with developed estimation methods.…”

Section: Introductionmentioning

confidence: 99%

Novel Approaches to Modelling Flammability Characteristics of Polymethyl Methacrylate (PMMA) via Multivariate Adaptive Regression Splines and Random Forest Methods

Alhassan

Jiang

Mensah

et al. 2020

AJRCoS

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Soft-computing techniques for fire safety parameter predictions in flammability studies are essential for describing a material fire behaviour. This study proposed, two novel Artificial Intelligence developed models, Multivariate Adaptive Regression Splines (MARS) and Random Forest (RF) methods, to model and predict peak heat release rate (pHRR) of Polymethyl methacrylate (PMMA) from Microscale Combustion Calorimetry (MCC) experiment. From the statistical analysis, MARS presented the highest coefficient of determination (R 2 ) values of (0.9998) and (0.9996) for training and testing respectively, with low MAD, MAPE and RMSE values. Comparatively, MARS Alhassan et al.; AJRCOS, 4(4): 1-14, 2019; Article no.AJRCOS.53584 2 outperformed RF in the predictions of pHRR, through its model algorithms that generated optimized equations for pHRR predictions, covering all non-linearity points of the experimental data. Amongst the input variables (sample mass, THR, HRC, pTemp and pTime), heating rate (β), highly influenced pHRR outcome predictions from MARS and RF models. However, to validate the performance and applicability of the proposed models. Results of MARS and RF were benchmarked with that from Artificial Neural Network (ANN) methods. The MARS and RF models observed the least error deviation when compared with pHRR results for PMMA from the ANN models. This study therefore, recommends the adoption of MARS and RF in the predictions of flammability characteristics of polymeric materials. Original Research Article

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Experimental investigation of effects of variation in heating rate, temperature and heat flux on fire properties of a non-charring polymer

Abu-Bakar

Cran

Moinuddin

2018

J Therm Anal Calorim

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Discuss the heat release capacity of polymer derived from microscale combustion calorimeter

Cited by 23 publications

References 14 publications

Application of Adaptive Neuro-Fuzzy Inference System in Flammability Parameter Prediction

Application of Adaptive Neuro-Fuzzy Inference System in Flammability Parameter Prediction

Novel Approaches to Modelling Flammability Characteristics of Polymethyl Methacrylate (PMMA) via Multivariate Adaptive Regression Splines and Random Forest Methods

Experimental investigation of effects of variation in heating rate, temperature and heat flux on fire properties of a non-charring polymer

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