Artificial neural network predictions of polycyclic aromatic hydrocarbon formation in premixed n-heptane flames

İnal, Fikret

doi:10.1016/j.fuproc.2006.08.002

Cited by 20 publications

(3 citation statements)

References 36 publications

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“…Modeling Using an ANN. A recent study reports possible prediction of PAH formation in premixed n-heptane flames using such models (23). So, this model was expected to be efficient in predicting the formation of some PAHs during coffee roasting.…”

Section: T T)]mentioning

confidence: 99%

Modeling the Formation of Some Polycyclic Aromatic Hydrocarbons During the Roasting of Arabica Coffee Samples

Houessou

Goujot

Heyd

et al. 2008

J. Agric. Food Chem.

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Roasting is a critical process in coffee production, as it enables the development of flavor and aroma. At the same time, roasting may lead to the formation of nondesirable compounds, such as polycyclic aromatic hydrocarbons (PAHs). In this study, Arabica green coffee beans from Cuba were roasted under controlled conditions to monitor PAH formation during the roasting process. Roasting was performed in a pilot-spouted bed roaster, with the inlet air temperature varying from 180 to 260 degrees C, for roasting conditions ranging from 5 to 20 min. Several PAHs were determined in both roasted coffee samples and green coffee samples. Different models were tested, with more or less assumptions on the chemical phenomena, with a view to predict the system global behavior. Two kinds of models were used and compared: kinetic models (based on Arrhenius law) and statistical models (neural networks). The numbers of parameters to adjust differed for the tested models, varying from three to nine for the kinetic models and from five to 13 for the neural networks. Interesting results are presented, with satisfactory correlations between experimental and predicted concentrations for some PAHs, such as pyrene, benz[a]anthracene, chrysene, and anthracene.

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Section: T T)]mentioning

confidence: 99%

Modeling the Formation of Some Polycyclic Aromatic Hydrocarbons During the Roasting of Arabica Coffee Samples

Houessou

Goujot

Heyd

et al. 2008

J. Agric. Food Chem.

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“…In another study, Inal [39] developed a network to estimate PAH concentrations in laminar, premixed n-heptane/oxygen/argon and n-heptane/oxygen/oxygenate/argon flames. For data acquisition, an experimental study was performed.…”

Section: Methodsmentioning

confidence: 99%

An Artificial Neural Network for the Low-Cost Prediction of Soot Emissions

et al. 2020

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Soot formation in combustion systems is a growing concern due to its adverse environmental and health effects. It is considered to be a tremendously complicated phenomenon which includes multiphase flow, thermodynamics, heat transfer, chemical kinetics, and particle dynamics. Although various numerical approaches have been developed for the detailed modeling of soot evolution, most industrial device simulations neglect or rudimentarily approximate soot formation due to its high computational cost. Developing accurate, easy to use, and computationally inexpensive numerical techniques to predict or estimate soot concentrations is a major objective of the combustion industry. In the present study, a supervised Artificial Neural Network (ANN) technique is applied to predict the soot concentration fields in ethylene/air laminar diffusion flames accurately with a low computational cost. To gather validated data, eight different flames with various equivalence ratios, inlet velocities, and burner geometries are modeled using the CoFlame code (a computational fluid dynamics (CFD) parallel combustion and soot model) and the Lagrangian histories of soot-containing fluid parcels are computed and stored. Then, an ANN model is developed and optimized using the Levenberg-Marquardt approach. Two different scenarios are introduced to validate the network performance; testing the prediction capabilities of the network for the same eight flames that are used to train the network, and for two new flames that are not within the training data set. It is shown that for both of these cases the ANN is able to predict the overall soot concentration field very well with a relatively low integrated error.

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“…In the recent years, ANN modeling technique has been used to predict various toxic emissions and other environmental issues related to combustion processes [2,3], as well as combustion of coal. Chong et al [4] modeled the gaseous emissions emanating from the combustion of lump coal on a chain-grate stoker-fired boiler using ANNs; and obtained encouraging results for prediction of pollutant emissions, as an alternative to the mathematical modeling of the physical process.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks

Bekat

Erdogan²,

İnal³

et al. 2012

Energy

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a b s t r a c tThe amount of bottom ash formed in a pulverized coal-fired power plant was predicted by artificial neural network modeling using one-year operating data of the plant and the properties of the coals processed. The model output was defined as the ratio of amount of bottom ash produced to amount of coal burned (Bottom ash/Coal burned). The input parameters were the moisture contents, ash contents and lower heating values of the coals. The total 653 data were divided into two groups for the training (90% of the data) and the testing (10% of the data) of the network. A three-layer, feed-forward type network architecture with back-propagation learning was used in the modeling study. The activation function was sigmoid function. The best prediction performance was obtained for a one hidden layer network with 29 neurons. The learning rate and the tolerance value were 0.2 and 0.05, respectively. R 2 (coefficient of determination) values between the actual (Bottom ash/Coal burned) ratios and the model predictions were 0.988 for the training set and 0.984 for the testing set. In addition, the sensitivity analysis indicated that the ash content of coals was the most effective parameter for the prediction of the ratio of bottom ash to coal burned.

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Artificial neural network predictions of polycyclic aromatic hydrocarbon formation in premixed n-heptane flames

Cited by 20 publications

References 36 publications

Modeling the Formation of Some Polycyclic Aromatic Hydrocarbons During the Roasting of Arabica Coffee Samples

Modeling the Formation of Some Polycyclic Aromatic Hydrocarbons During the Roasting of Arabica Coffee Samples

An Artificial Neural Network for the Low-Cost Prediction of Soot Emissions

Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks

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