Flash point prediction for the binary mixture of phosphatic solvents and n-dodecane from UNIFAC group contribution model

Balasubramonian, S.; Srivastav, Ravi Kant; Kumar, Shekhar; Sivakumar, D.; Sampath, M.; Mudali, U. Kamachi; Natarajan, R.

doi:10.1016/j.jlp.2014.12.012

Cited by 16 publications

(4 citation statements)

References 21 publications

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“…These deviations stayed in the range of 0.06 to 1 K. The variation coefficients of these mea surements are within the range of 0.1 -0.6 %. These values are Jower than those observed for FP data already reported in the literature [11,49,50).…”

Section: Flash Point Of Pure Saturated Faeescontrasting

confidence: 49%

Flash point prediction with UNIFAC type models of ethylic biodiesel and binary/ternary mixtures of FAEEs

Nascimento

Carareto

Neto

et al. 2020

Fuel

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In order to guarantee safe handling of a combustible liquid, such as biodiesel, it is important to be aware of its Flash Point (FP). Thus, the present study focused on FP experimental measurement and thermodynamic correlation of binary and ternary mixtures of saturated or unsaturated fatty acid ethyl esters (FAEEs) as surrogates for biodiesel. FP of different types of ethylic biodiesel in terms of feedstock (canola, corn, cotton and soy oils) were also measured and compared with the predicted values. Experiments were carried out in agreement with the standard closed cup procedure ASTM D6450. Liaw's model was applied to FP prediction, with liquid phase non-ideality description accounted for by UNIFAC group contribution models (Original UNIFAC, UNIFAC-Dortmund and UNIFAC-Bessa with revised parameters for fats). FP prediction accounting for non-ideality proved to be accurate, with some difference between the UNIFAC type models and Root Mean Square Deviations (RMSD) varying from 0.27 K to 3.95 K for binary and ternary mixtures.

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Section: Flash Point Of Pure Saturated Faeescontrasting

confidence: 49%

Flash point prediction with UNIFAC type models of ethylic biodiesel and binary/ternary mixtures of FAEEs

Nascimento

Carareto

Neto

et al. 2020

Fuel

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“…The limit for successful flash point detection was set at a pressure increase of 20 kPa, which corresponded to approximately a flame volume of 1.5 mL above barometric pressure. Further detailed testing process can be referred in previous literature 40 . Accelerating rate calorimetry (ARC, THT) was utilized to monitor the real-time temperature changes of pouch cells.…”

Section: Methodsmentioning

confidence: 99%

Hybridizing carbonate and ether at molecular scales for high-energy and high-safety lithium metal batteries

Chen,

Zhang,

Zhu

et al. 2024

Nat Commun

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Commonly-used ether and carbonate electrolytes show distinct advantages in active lithium-metal anode and high-voltage cathode, respectively. While these complementary characteristics hold promise for energy-dense lithium metal batteries, such synergy cannot be realized solely through physical blending. Herein, a linear functionalized solvent, bis(2-methoxyethyl) carbonate (BMC), is conceived by intramolecularly hybridizing ethers and carbonates. The integration of the electron-donating ether group with the electron-withdrawing carbonate group can rationalizes the charge distribution, imparting BMC with notable oxidative/reductive stability and relatively weak solvation ability. Furthermore, BMC also offers advantages including the ability to slightly dissolve LiNO3, excellent thermostability and nonflammability. Consequently, the optimized BMC-based electrolyte, even with typical concentrations in the single solvent, demonstrates high-voltage tolerance (4.4 V) and impressive Li plating/stripping Coulombic efficiency (99.4%). Moreover, it fulfills practical lithium metal batteries with satisfactory cycling performance and exceptional tolerance towards thermal/mechanical abuse, showcasing its suitability for safe high-energy lithium metal batteries.

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“…The experimental dataset consists in 650 flash points extracted from literature and detailed in Supporting Information (Table S1). It represents an update of the database used in a previous work by taking into account new data published after its constitution . The database concerns 60 binary mixtures constituted of 47 different pure compounds (hydrocarbons, alcohols, ketones, esters, acids) in various concentrations (to be compared to 435 data for 34 compounds in 43 mixtures, in the previous work).…”

Section: Methodsmentioning

confidence: 99%

New QSPR Models to Predict the Flammability of Binary Liquid Mixtures

Fayet

Rotureau

2019

Molecular Informatics

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This paper is dedicated to Prof. Paola Gramatica on the occasion of her retirement.Abstract: New Quantitative Structure-Property Relationships (QSPR) are presented to predict the flash point of binary liquid mixtures, based on more than 600 experimental flash points for 60 binary mixtures. Two models are proposed based on a GA-MLR approach that uses a genetic algorithm (GA) variable selection in multilinear regressions (MLR). In these models, mixtures were characterized by a series of mixture descriptors calculated from various mixture formula combining the molecular descriptors of the single com-pounds constituting the mixtures and their respective molar fractions in the mixture. The best model demonstrated good predictive capabilities with a mean absolute error of only 7.3 8C estimated for an external validation set. Moreover, this model is focused on mixture descriptors applicable to more complex mixtures, i. e. constituted of more than 2 components, and already demonstrated interesting predictions for a series of ternary mixtures.(4 of 8) 1800122 a Details on the exact composition of each set were not available; n D : number of mixture descriptors; n: number of molecules in the whole dataset; n TR : number of molecules in the training set; n EXT : number of molecules in the validation set; NA: not applicable.

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Flash point prediction for the binary mixture of phosphatic solvents and n-dodecane from UNIFAC group contribution model

Cited by 16 publications

References 21 publications

Flash point prediction with UNIFAC type models of ethylic biodiesel and binary/ternary mixtures of FAEEs

Flash point prediction with UNIFAC type models of ethylic biodiesel and binary/ternary mixtures of FAEEs

Hybridizing carbonate and ether at molecular scales for high-energy and high-safety lithium metal batteries

New QSPR Models to Predict the Flammability of Binary Liquid Mixtures

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