Molar enthalpy of vaporization of ethanol–gasoline mixtures and their colloid state

Balabin, Roman M.; Syunyaev, R. Z.; Karpov, Sergey A.

doi:10.1016/j.fuel.2006.08.008

Cited by 70 publications

(37 citation statements)

References 21 publications

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“…The 0% PEG MDA displayed the most rapid weight loss from 10 s to 1 min after the last spray was actuated at −197±14 mg/min and the 10% PEG MDA was the slowest at −112±15 mg/min (linearity of these gradients were R 2 >0.9 for both). This period of rapid weight loss post dose actuation was considered to be a consequence of HFA 134a evaporation as it has a lower enthalpy of vaporisation (19.6 kJ/mol) (26) compared to other volatile solvent EtOH (42.3 kJ/mol) (27). The slower rate of weight loss between 10 s to 1 min for the 10% PEG MDA was probably a result of the different formulation composition, as the addition of 10% PEG in this formulation was compensated by a 10% reduction in HFA 134a.…”

Section: Resultsmentioning

confidence: 99%

Manipulation of Corticosteroid Release from a Transiently Supersaturated Topical Metered Dose Aerosol Using A Residual Miscible Co-Solvent

2008

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Section: Resultsmentioning

confidence: 99%

Manipulation of Corticosteroid Release from a Transiently Supersaturated Topical Metered Dose Aerosol Using A Residual Miscible Co-Solvent

2008

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“…Each experiment was repeated ten times. The neuron number in an input layer (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) and the hidden neuron number (5-15) were varied. The number of iterations was 250.…”

Section: Validation Set Percentagementioning

confidence: 99%

“…Time means the time of ANN training-the individual factor that directly does not affect the user, whereas convergence and reproducibility of the received network are essentially important parameters of the given product for the customer [16][17][18][19][20]. Earlier, the advantages of TIC as dimensionless factors have been mentioned.…”

Section: Number Of Training Iterationsmentioning

confidence: 99%

Universal technique for optimization of neural network training parameters: gasoline near infrared data example

Balabin

Safieva

Lomakina

2008

Neural Comput & Applic

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The universal technique of finding optimum training parameters for multi-layer perceptron-such as percentage of samples in a cross-validation set and quantities of training iterations with various initial values-is offered. This technique is aimed at the searching of optimum values of two complex factors depending on accuracy and convergence of a network, and also on the time of its training. Their conventional names are ''cross-validation coefficient'' and ''training iteration coefficient''. Near infrared spectroscopy data for gasoline samples are used to evaluate the efficiency of the method.

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“…[1][2][3] As a fuel for spark-ignition engines, ethanol has some advantages over gasoline, such as better anti-knock characteristics and the reduction of CO and UHC emissions. 4,5 Although alcohol has these advantages, due to limitations in the technology, economic and regional considerations, still it can't be used extensively.…”

Section: Introductionmentioning

confidence: 99%

Hydrogenation of Ethyl Acetate to Ethanol over Bimetallic Cu-Zn/SiO₂Catalysts Prepared by Means of Coprecipitation

Zhu¹,

Shi²

2014

Bulletin of the Korean Chemical Society

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A series of bimetallic Cu-Zn/SiO 2 catalysts were prepared via thermal decomposition of the as-synthesized CuZn(OH) 4 (H 2 SiO 3 ) 2 ·nH 2 O hydroxides precursors. This highly dispersed Cu-solid base catalyst is extremely effective for hydrogenation of ethyl acetate to ethanol. The reduction and oxidation features of the precursors prepared by coprecipitation method and catalysts were extensively investigated by TGA, XRD, TPR and N 2 -adsorption techniques. Catalytic activity by ethyl acetate hydrogenation of reaction temperatures between 120 and 300 o C, different catalyst calcination and reduction temperatures, different Cu/Zn loadings have been examined extensively. The relation between the performance for hydrogenation of ethyl acetate and the structure of the Cu-solid base catalysts with Zn loading were discussed. The detected conversion of ethyl acetate reached 81.6% with a 93.8% selectivity of ethanol. This investigation of the Cu-Zn/SiO 2 catalyst provides a recently proposed pathway for ethyl acetate hydrogenation reaction to produce ethanol over Cusolid base catalysts.

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Molar enthalpy of vaporization of ethanol–gasoline mixtures and their colloid state

Cited by 70 publications

References 21 publications

Manipulation of Corticosteroid Release from a Transiently Supersaturated Topical Metered Dose Aerosol Using A Residual Miscible Co-Solvent

Manipulation of Corticosteroid Release from a Transiently Supersaturated Topical Metered Dose Aerosol Using A Residual Miscible Co-Solvent

Universal technique for optimization of neural network training parameters: gasoline near infrared data example

Hydrogenation of Ethyl Acetate to Ethanol over Bimetallic Cu-Zn/SiO₂Catalysts Prepared by Means of Coprecipitation

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Molar enthalpy of vaporization of ethanol–gasoline mixtures and their colloid state

Cited by 70 publications

References 21 publications

Manipulation of Corticosteroid Release from a Transiently Supersaturated Topical Metered Dose Aerosol Using A Residual Miscible Co-Solvent

Manipulation of Corticosteroid Release from a Transiently Supersaturated Topical Metered Dose Aerosol Using A Residual Miscible Co-Solvent

Universal technique for optimization of neural network training parameters: gasoline near infrared data example

Hydrogenation of Ethyl Acetate to Ethanol over Bimetallic Cu-Zn/SiO2Catalysts Prepared by Means of Coprecipitation

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Product

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Hydrogenation of Ethyl Acetate to Ethanol over Bimetallic Cu-Zn/SiO₂Catalysts Prepared by Means of Coprecipitation