CH3CO + O2 + M (M = He, N2) Reaction Rate Coefficient Measurements and Implications for the OH Radical Product Yield

2015 ◽  
Vol 119 (28) ◽  
pp. 7481-7497 ◽  
Author(s):  
Vassileios C. Papadimitriou ◽  
Emmanuel S. Karafas ◽  
Tomasz Gierczak ◽  
James B. Burkholder
Keyword(s):  
Reaction Rate ◽  
Rate Coefficient ◽  
Product Yield ◽  
Oh Radical ◽  
Reaction Rate Coefficient ◽  
Radical Product

scholarly journals Reaction Rate Coefficient of OH Radicals with d9-Butanol as a Function of Temperature

ACS Omega ◽  
2021 ◽  
Author(s):  
Amira Allani ◽  
Yuri Bedjanian ◽  
Dimitrios K. Papanastasiou ◽  
Manolis N. Romanias
Keyword(s):  
Reaction Rate ◽  
Rate Coefficient ◽  
Oh Radicals ◽  
Reaction Rate Coefficient

Investigation of chlorine wall decay in an old, decommissioned metallic pipe using a pipe section reactor

2020 ◽  
Vol 20 (3) ◽  
pp. 953-962
Author(s):  
R. Tonev ◽  
G. Dimova
Keyword(s):  
Reaction Rate ◽  
Rate Coefficient ◽  
Tap Water ◽  
Decay Rates ◽  
Free Chlorine ◽  
Reaction Rate Coefficient ◽  
Pipe Section ◽  
Reaction Coefficient ◽  
Bulk Reaction ◽  
Series Of Experiments

Abstract The study investigates the kinetics of free chlorine depletion in tap water from the Sofia distribution network. The overall decay rates, the bulk reaction rate coefficient, the wall reaction rate coefficient and the influence of mass transfer have been determined in a laboratory pipe section reactor (PSR), testing an old decommissioned metallic pipe. In total, 23 series of experiments were performed under different initial free chlorine concentrations and different hydraulic conditions. The applicability of different chlorine decay mathematical models has been investigated. A new model was proposed, combining zero order bulk reactions and first order wall reactions, describing the laboratory results with Nash-Sutcliffe efficiency coefficients over 0.99. The obtained values for the wall reaction coefficient vary in the range 0.008–0.030 m/h, decreasing exponentially with increasing initial chlorine concentration.

Simulation of Swirl Combustion and NO Formation Using Various Second Order Moment Turbulent Combustion Models and DNS Verification

2008 ◽  
Author(s):  
F. Wang ◽  
Y. Huang ◽  
L. X. Zhou ◽  
C. X. Xu ◽  
J. Cao
Keyword(s):  
Turbulent Combustion ◽  
Reaction Rate ◽  
Rate Coefficient ◽  
Second Order ◽  
Concentration Fluctuation ◽  
Order Moment ◽  
Combustion Model ◽  
Reaction Rate Coefficient ◽  
No Formation ◽  
Swirl Combustion

If the instantaneous chemistry reaction rate is taken as ws = Bρ2Y1Y2 exp(−E/RT) = ρ2Y1Y2K, here K is a contraction for the exponential term. Then, ignoring the three order fluctuation correlation term, the average reaction rate could be ws = ρ2(Y1Y2K + Y1′Y2′K + Y1K′Y2′ + Y2K′Y1′). The authors have simulated jet combustion and swirl combustion using this kind of second order moment (SOM) turbulent combustion model. The predictions are close to experimental data in most regions. In order to improve the SOM turbulent combustion model, the effect of various correlation moments in the simulation of turbulent swirl combustion and NO formation is studied by comparing different SOM turbulence-chemistry models, including the unified second-order moment (USM) model, the model accounting for only the time-averaged reaction-rate coefficient, the model accounting for only the concentration fluctuation and the model accounting for both the time-averaged reaction-rate coefficient and the concentration fluctuation. These models are incorporated into the FLUENT code for a methane-air swirling combustion and NO formation under various swirl numbers. The magnitude of various correlations and their effect on the time-averaged reaction rate are analyzed, and the simulation results are compared with the corresponding measurement results. The results showed that the USM model gives the best agreement with the experimental results and among various correlation moments the correlation of reaction-rate coefficient fluctuation with the concentration fluctuation is most important. Additionally, a direct numerical simulation (DNS) of three-dimensional channel turbulent reacting flows with consideration of buoyancy effect using a spectral method was carried out. The statistical results are shown that K′Y′ are larger than Y1′Y2′.

Characterization of the Reaction Rate Coefficient of DNA with the Hydroxyl Radical

1996 ◽  
Vol 146 (5) ◽  
pp. 510 ◽  
Author(s):  
J. R. Milligan ◽  
C. C. L. Wu ◽  
J. Y-Y. Ng ◽  
J. A. Aguilera ◽  
J. F. Ward
Keyword(s):  
Hydroxyl Radical ◽  
Reaction Rate ◽  
Rate Coefficient ◽  
Reaction Rate Coefficient

Kinetics of heat-induced browning in concentrated milk with sucrose as affected by pH and temperature / Cinética del pardeamiento inducido por calor en sistemas de leche concentrada con alto contenido de sacarosa. Influencia del pH y de la temperatura

1999 ◽  
Vol 5 (5) ◽  
pp. 407-413 ◽  
Author(s):  
M.S. Pauletti ◽  
E.J. Matta ◽  
S. Rozycki
Keyword(s):  
Reaction Rate ◽  
Rate Coefficient ◽  
Milk Powder ◽  
Model System ◽  
Reaction Rate Coefficient ◽  
Dry Milk ◽  
Color Response ◽  
Concentrated Milk ◽  
Kinetics Of ◽  
Arrhenius Expression

A model system constituted by whole dry milk powder, sucrose, and distilled water was used to establish kinetic parameters of the heat-induced browning process (HIBP). Second-order central com posite design was chosen with pH and temperature as selected variables. Color response was the Kubelka-Munk index (K/S) calculated from reflectance colorimetric measures as a function of time using the self-backing reflectance transformation (SBRT) procedure. Results showed that HIBP pseudo- order reactions were variable between 0 and 0.6. Reaction rate coefficient (k) was strongly dependent on pH and temperature. In general, k increased as both pH and temperature increased but the influ ence of temperature was higher than that of pH. Q10 was dependent on temperature, ranging be tween 2.15 at 130-140 °C and 2.44 at 100-110 °C, whatever the pH. k values were analyzed with the Arrhenius expression. Values of Ea, calculated for different pH, were coincident (25.34 kcal/M).

scholarly journals Modeling organic aerosol from the oxidation of α-pinene in a Potential Aerosol Mass (PAM) chamber

2013 ◽  
Vol 13 (1) ◽  
pp. 2759-2793
Author(s):  
S. Chen ◽  
W. H. Brune ◽  
A. Lambe ◽  
P. Davidovits ◽  
T. Onasch
Keyword(s):  
Sensitivity Analysis ◽  
Reaction Rate ◽  
First Generation ◽  
Rate Coefficient ◽  
Organic Aerosol ◽  
Oxidation Products ◽  
Basis Set ◽  
Model Framework ◽  
Reaction Rate Coefficient ◽  
Aerosol Mass

Abstract. A model has been developed to simulate the formation and evolution of secondary organic aerosol (SOA) and was tested against data produced in a Potential Aerosol Mass (PAM) flow reactor and a large environmental chamber. The model framework is based on the two-dimensional volatility basis set approach (2D-VBS), in which SOA oxidation products in the model are distributed on the 2-D space of effective saturation concentration (Ci*) and oxygen-to-carbon ratio (O : C). The modeled organic aerosol mass concentrations (COA) and O : C agree with laboratory measurements within estimated uncertainties. However, while both measured and modeled O : C increase with increasing OH exposure as expected, the increase of modeled O : C is rapid at low OH exposure and then slows as OH exposure increases while the increase of measured O : C is initially slow and then accelerates as OH exposure increases. A global sensitivity analysis indicates that modeled COA values are most sensitive to the assumed values for the number of Ci* bins, the heterogeneous OH reaction rate coefficient, and the yield of first-generation products. Modeled SOA O : C values are most sensitive to the assumed O : C of first-generation oxidation products, the number of Ci* bins, the heterogeneous OH reaction rate coefficient, and the number of O : C bins. All these sensitivities vary as a function of OH exposure. The sensitivity analysis indicates that the 2D-VBS model framework may require modifications to resolve discrepancies between modeled and measured O : C as a function of OH exposure.

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