Experimental and theoretical investigation of water removal from DMAZ liquid fuel by an adsorption process

Ghanbari, Shahram; Vaferi, Behzad

doi:10.1016/j.actaastro.2015.03.011

Cited by 36 publications

(13 citation statements)

References 44 publications

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“…Amine azides are low molecular weight organic amine compounds containing at least one tertiary nitrogen and one functional group that can be used as the fuel composition in hypergolic fuel propulsion systems or gelled propellants [4]. Such compounds are preferred to replace hydrazines due to the advantages of synthesized and evaluated as potentional hypergolic fuel compounds, such as dimethylaminoethylazide (DMAZ), pyrollidiylethylazide (PYAZ) and bis(ethylazide)methylamine (BAZ) [5][6][7][8]. DMAZ appears to be one of the better candidates for hydrazine compounds.…”

Section: Commentmentioning

confidence: 99%

Crystal structure of 1,4-bis(2-azidoethyl)piperazine-1,4-diium dichloride, C₈H₁₈N₈Cl₂

Gao¹,

Wang²,

Chen³

et al. 2018

Zeitschrift Für Kristallographie - New Crystal Structures

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Section: Commentmentioning

confidence: 99%

Crystal structure of 1,4-bis(2-azidoethyl)piperazine-1,4-diium dichloride, C₈H₁₈N₈Cl₂

Gao¹,

Wang²,

Chen³

et al. 2018

Zeitschrift Für Kristallographie - New Crystal Structures

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“…This study focuses on five artificial neural networks (ANN), four hybrid neuro-fuzzy types, and three kinds of support vector regression (SVR) to simulate anti-inflammatory drug solubility in supercritical CO 2 . The considered ANN models are multilayer perceptron neural network (MLPNN) 46 , 47 , cascade feedforward neural network (CFFNN) 48 , recurrent neural network (RNN) 49 , 50 , general regression neural network (GRNN) 48 , and radial basis function neural networks (RBFNN) 51 . The efficiency of the support vector regression with the linear kernel (LSSVR-L) 52 , polynomial kernel (LSSVR-P) 52 , and Gaussian kernel (LSSVR-G) 53 are also evaluated over the considered purpose.…”

Section: Methodsmentioning

confidence: 99%

A universal methodology for reliable predicting the non-steroidal anti-inflammatory drug solubility in supercritical carbon dioxide

Rezaei

Nazarpour

Shahini

et al. 2022

Sci Rep

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Understanding the drug solubility behavior is likely the first essential requirement for designing the supercritical technology for pharmaceutical processing. Therefore, this study utilizes different machine learning scenarios to simulate the solubility of twelve non-steroidal anti-inflammatory drugs (NSAIDs) in the supercritical carbon dioxide (SCCO2). The considered NSAIDs are Fenoprofen, Flurbiprofen, Ibuprofen, Ketoprofen, Loxoprofen, Nabumetone, Naproxen, Nimesulide, Phenylbutazone, Piroxicam, Salicylamide, and Tolmetin. Physical characteristics of the drugs (molecular weight and melting temperature), operating conditions (pressure and temperature), and solvent property (SCCO2 density) are effectively used to estimate the drug solubility. Monitoring and comparing the prediction accuracy of twelve intelligent paradigms from three categories (artificial neural networks, support vector regression, and hybrid neuro-fuzzy) approves that adaptive neuro-fuzzy inference is the best tool for the considered task. The hybrid optimization strategy adjusts the cluster radius of the subtractive clustering membership function to 0.6111. This model estimates 254 laboratory-measured solubility data with the AAPRE = 3.13%, MSE = 2.58 × 10–9, and R2 = 0.99919. The leverage technique confirms that outliers may poison less than four percent of the experimental data. In addition, the proposed hybrid paradigm is more reliable than the equations of state and available correlations in the literature. Experimental measurements, model predictions, and relevancy analyses justified that the drug solubility in SCCO2 increases by increasing temperature and pressure. The results show that Ibuprofen and Naproxen are the most soluble and insoluble drugs in SCCO2, respectively.

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“…An adsorption process includes one or more than one adsorbate which is fixed through physical or chemical bonds onto an adsorbent [43,44]. This process as an efficient and environmentally friendly technique has attracted much attention in the wastewater treatment process [45,46], and gas separation and purification [47][48][49][50], to remove impurities from water or gas. In this way, it is required to develop the adsorption kinetics to evaluate the predicted adsorption parameters with experimental adsorbent values.…”

Section: Kinetic Measurementmentioning

confidence: 99%

Analyses of Adsorption Behavior of CO₂, CH₄, and N₂ on Different Types of BETA Zeolites

et al. 2019

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The adsorption equilibrium and kinetics of CO 2 , CH 4 , and N 2 on three types of BETA zeolites were investigated at different temperatures and a defined partial pressure range from dynamic breakthrough experiments. The adsorbed amount followed the decreasing order of CO 2 > CH 4 > N 2 for all studied materials. For the same ratio of SiO 2 /Al 2 O 3 , the Na-BETA-25 zeolite showed a higher uptake capacity than H-BETA-25, due to the presence of a Na + cationic center. Comparing the same H + compensation cation, zeolite H-BETA-25 expressed a slightly higher adsorption capacity than H-BETA-150. Regarding the selectivity of gases, based on their affinity constants, H-BETA-150 displayed the best ability. The adsorption kinetics was considered using the zero-length-column (ZLC) technique. Response surface methodology (RSM) was applied to evaluate the interactions between adsorption parameters and to describe the process.

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Experimental and theoretical investigation of water removal from DMAZ liquid fuel by an adsorption process

Cited by 36 publications

References 44 publications

Crystal structure of 1,4-bis(2-azidoethyl)piperazine-1,4-diium dichloride, C₈H₁₈N₈Cl₂

Crystal structure of 1,4-bis(2-azidoethyl)piperazine-1,4-diium dichloride, C₈H₁₈N₈Cl₂

A universal methodology for reliable predicting the non-steroidal anti-inflammatory drug solubility in supercritical carbon dioxide

Analyses of Adsorption Behavior of CO₂, CH₄, and N₂ on Different Types of BETA Zeolites

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Experimental and theoretical investigation of water removal from DMAZ liquid fuel by an adsorption process

Cited by 36 publications

References 44 publications

Crystal structure of 1,4-bis(2-azidoethyl)piperazine-1,4-diium dichloride, C8H18N8Cl2

Crystal structure of 1,4-bis(2-azidoethyl)piperazine-1,4-diium dichloride, C8H18N8Cl2

A universal methodology for reliable predicting the non-steroidal anti-inflammatory drug solubility in supercritical carbon dioxide

Analyses of Adsorption Behavior of CO2, CH4, and N2 on Different Types of BETA Zeolites

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Crystal structure of 1,4-bis(2-azidoethyl)piperazine-1,4-diium dichloride, C₈H₁₈N₈Cl₂

Crystal structure of 1,4-bis(2-azidoethyl)piperazine-1,4-diium dichloride, C₈H₁₈N₈Cl₂

Analyses of Adsorption Behavior of CO₂, CH₄, and N₂ on Different Types of BETA Zeolites