Prediction of NOx emissions from a simplified biodiesel surrogate by applying stochastic simulation algorithms (SSA)

A stochastic simulation algorithm (SSA) approach is implemented with the components of a simplified biodiesel surrogate to predict NOx (NO and NO 2 ) emission concentrations from the combustion of biodiesel. The main reaction pathways were obtained by simplifying the previously derived skeletal mech...

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Vydané v:Combustion theory and modelling Ročník 21; číslo 2; s. 346 - 357
Hlavní autori: Omidvarborna, Hamid, Kumar, Ashok, Kim, Dong-Shik
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Abingdon Taylor & Francis 04.03.2017
Taylor & Francis Ltd
Predmet:
Algorithms
Biodiesel
Biodiesel fuels
biodiesel surrogate
chemical kinetic reaction
Combustion
Combustion temperature
Complexity
Computer simulation
Cooking
Diesel fuels
Emission
Emissions
Esters
Heating
low-temperature combustion (LTC)
Mathematical models
Mean square values
Nitrogen dioxide
Nitrogen oxides
NOx emission
Predictions
Probability theory
Randomness
Root-mean-square errors
Simplification
stochastic simulation algorithm (SSA)
Sulfur
ISSN:1364-7830, 1741-3559
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Abstract A stochastic simulation algorithm (SSA) approach is implemented with the components of a simplified biodiesel surrogate to predict NOx (NO and NO 2 ) emission concentrations from the combustion of biodiesel. The main reaction pathways were obtained by simplifying the previously derived skeletal mechanisms, including saturated methyl decenoate (MD), unsaturated methyl 5-decanoate (MD5D), and n-decane (ND). ND was added to match the energy content and the C/H/O ratio of actual biodiesel fuel. The MD/MD5D/ND surrogate model was also equipped with H 2 /CO/C 1 formation mechanisms and a simplified NOx formation mechanism. The predicted model results are in good agreement with a limited number of experimental data at low-temperature combustion (LTC) conditions for three different biodiesel fuels consisting of various ratios of unsaturated and saturated methyl esters. The root mean square errors (RMSEs) of predicted values are 0.0020, 0.0018, and 0.0025 for soybean methyl ester (SME), waste cooking oil (WCO), and tallow oil (TO), respectively. The SSA model showed the potential to predict NOx emission concentrations, when the peak combustion temperature increased through the addition of ultra-low sulphur diesel (ULSD) to biodiesel. The SSA method used in this study demonstrates the possibility of reducing the computational complexity in biodiesel emissions modelling.
AbstractList A stochastic simulation algorithm (SSA) approach is implemented with the components of a simplified biodiesel surrogate to predict NOx (NO and NO2) emission concentrations from the combustion of biodiesel. The main reaction pathways were obtained by simplifying the previously derived skeletal mechanisms, including saturated methyl decenoate (MD), unsaturated methyl 5-decanoate (MD5D), and n-decane (ND). ND was added to match the energy content and the C/H/O ratio of actual biodiesel fuel. The MD/MD5D/ND surrogate model was also equipped with H2/CO/C1 formation mechanisms and a simplified NOx formation mechanism. The predicted model results are in good agreement with a limited number of experimental data at low-temperature combustion (LTC) conditions for three different biodiesel fuels consisting of various ratios of unsaturated and saturated methyl esters. The root mean square errors (RMSEs) of predicted values are 0.0020, 0.0018, and 0.0025 for soybean methyl ester (SME), waste cooking oil (WCO), and tallow oil (TO), respectively. The SSA model showed the potential to predict NOx emission concentrations, when the peak combustion temperature increased through the addition of ultra-low sulphur diesel (ULSD) to biodiesel. The SSA method used in this study demonstrates the possibility of reducing the computational complexity in biodiesel emissions modelling.
A stochastic simulation algorithm (SSA) approach is implemented with the components of a simplified biodiesel surrogate to predict NOx (NO and NO 2 ) emission concentrations from the combustion of biodiesel. The main reaction pathways were obtained by simplifying the previously derived skeletal mechanisms, including saturated methyl decenoate (MD), unsaturated methyl 5-decanoate (MD5D), and n-decane (ND). ND was added to match the energy content and the C/H/O ratio of actual biodiesel fuel. The MD/MD5D/ND surrogate model was also equipped with H 2 /CO/C 1 formation mechanisms and a simplified NOx formation mechanism. The predicted model results are in good agreement with a limited number of experimental data at low-temperature combustion (LTC) conditions for three different biodiesel fuels consisting of various ratios of unsaturated and saturated methyl esters. The root mean square errors (RMSEs) of predicted values are 0.0020, 0.0018, and 0.0025 for soybean methyl ester (SME), waste cooking oil (WCO), and tallow oil (TO), respectively. The SSA model showed the potential to predict NOx emission concentrations, when the peak combustion temperature increased through the addition of ultra-low sulphur diesel (ULSD) to biodiesel. The SSA method used in this study demonstrates the possibility of reducing the computational complexity in biodiesel emissions modelling.
A stochastic simulation algorithm (SSA) approach is implemented with the components of a simplified biodiesel surrogate to predict NOx (NO and NO sub(2)) emission concentrations from the combustion of biodiesel. The main reaction pathways were obtained by simplifying the previously derived skeletal mechanisms, including saturated methyl decenoate (MD), unsaturated methyl 5-decanoate (MD5D), and n-decane (ND). ND was added to match the energy content and the C/H/O ratio of actual biodiesel fuel. The MD/MD5D/ND surrogate model was also equipped with H sub(2)/CO/C sub(1) formation mechanisms and a simplified NOx formation mechanism. The predicted model results are in good agreement with a limited number of experimental data at low-temperature combustion (LTC) conditions for three different biodiesel fuels consisting of various ratios of unsaturated and saturated methyl esters. The root mean square errors (RMSEs) of predicted values are 0.0020, 0.0018, and 0.0025 for soybean methyl ester (SME), waste cooking oil (WCO), and tallow oil (TO), respectively. The SSA model showed the potential to predict NOx emission concentrations, when the peak combustion temperature increased through the addition of ultra-low sulphur diesel (ULSD) to biodiesel. The SSA method used in this study demonstrates the possibility of reducing the computational complexity in biodiesel emissions modelling.
Author Omidvarborna, Hamid
Kim, Dong-Shik
Kumar, Ashok
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Snippet A stochastic simulation algorithm (SSA) approach is implemented with the components of a simplified biodiesel surrogate to predict NOx (NO and NO 2 ) emission...
A stochastic simulation algorithm (SSA) approach is implemented with the components of a simplified biodiesel surrogate to predict NOx (NO and NO2) emission...
A stochastic simulation algorithm (SSA) approach is implemented with the components of a simplified biodiesel surrogate to predict NOx (NO and NO sub(2))...
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SubjectTerms Algorithms
Biodiesel
Biodiesel fuels
biodiesel surrogate
chemical kinetic reaction
Combustion
Combustion temperature
Complexity
Computer simulation
Cooking
Diesel fuels
Emission
Emissions
Esters
Heating
low-temperature combustion (LTC)
Mathematical models
Mean square values
Nitrogen dioxide
Nitrogen oxides
NOx emission
Predictions
Probability theory
Randomness
Root-mean-square errors
Simplification
stochastic simulation algorithm (SSA)
Sulfur
Title Prediction of NOx emissions from a simplified biodiesel surrogate by applying stochastic simulation algorithms (SSA)
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