A new QSPR-based prediction model for biofuel lubricity

Weinebeck, Alexander; Kamiński, Sebastian; Murrenhoff, Hubertus; Leonhard, Kai

doi:10.1016/j.triboint.2017.05.005

Cited by 22 publications

(6 citation statements)

References 24 publications

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“…Many test methods are used to assess the lubricating properties of fuels under laboratory conditions; the most well-known of which are the HFRR, Lucas dwell test, Lucas four-ball test, Thornton aviation fuel lubricity evaluator (TAFLE), ball on three seats (BOTS), ball on three discs (BOTD), ball on cylinder lubricity evaluator (BOCLE), scuffing load BOCLE (SLBOCLE), and Cameron–Plint test (roller on plate) [ 5 , 32 , 33 , 34 ]. Alternatively, a four-ball machine can be used to evaluate the lubricating properties [ 35 , 36 , 37 ]. Particularly in [ 36 ], the authors used a four-ball tribo-tester to measure wear and friction characteristics of some biodiesel samples.…”

Section: Introductionmentioning

confidence: 99%

Lubricity of Ethanol–Diesel Fuel Blends—Study with the Four-Ball Machine Method

2021

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Due to the increasing consumption of fuels in heavy industries, especially in road transportation, significant efforts are being made to increase the market participation of renewable fuels, including ethanol. In diesel engines, however, ethanol cannot be used as a pure fuel, primarily due to its very low cetane number and lubricity. For this reason, greater attention is being paid to blended fuels containing diesel and varying percentages of ethanol. Tests of lubricating properties carried out in accordance with the standard HFRR (high frequency reciprocating rig) method for ethanol–diesel fuel blends have long durations, which leads to ethanol evaporation and changes in the composition of the tested fuel sample under elevated temperatures. Therefore, this study presents an alternative lubricity assessment criterion based on the measurement of the scuffing load with a four-ball machine. Lubricity tests of blends of typical diesel fuel and ethanol, with ethanol volume fractions up to 14% (v/v), were conducted using a four-ball machine with a continuous increase of the load force of the friction node. In this method the lubrication criterion was the scuffing load of the tribosystem. The obtained results provided insights into the influence of the addition of ethanol to diesel fuel on lubricating properties, while limiting the ethanol evaporation process. The results also showed that an increase in the fraction of ethanol up to 14% (v/v) in diesel fuel resulted in a decrease in the scuffing load and a corresponding deterioration in the lubricating properties of the diesel–ethanol blend. For an ethanol volume fraction of 6–14%, the changes in the scuffing load were smaller than in ethanol volume fractions of 0–6%.

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Section: Introductionmentioning

confidence: 99%

Lubricity of Ethanol–Diesel Fuel Blends—Study with the Four-Ball Machine Method

2021

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“…Boiling point Wang et al [62] Wang et al [53] Vapor pressure Lin et al [52] Wang et al [53] Nakajoh et al [63] Shimoyama et al [64] Eckert et al [65] Klamt et al [66] Water solubility Klamt et al [67] Puzyn et al [68] Schro ¨der et al [69] Oleszek et al [70] Kholod et al [71] Partition coefficient Buggert et al [72] Klamt et al [73] Wille et al [74] Dissociation constant Eckert and Klamt [75] Klamt et al [76] Reaction kinetics Zhou et al [77] Liu et al [78] Austin et al [79] Gertig et al [80] Zhang et al [81] Lubricity Weinebeck et al [82] Masuch et al [83] Melting point Preiss et al [84] Preiss et al [85] Heat capacity Zhao et al [86] Jacquemin et al [87] Dai et al [88] Density Palomar et al [89] Surface tension Kondor et al [90] Ja ´rva ´s et al [91] Gaudin [92] Goussard et al [93] Viscosity Alcalde et al [94] Kurnia et al [95] Zhao et al [96]…”

Section: Properties Referencesmentioning

confidence: 99%

An Overview of Computer‐aided Molecular and Process Design

Iftakher

Monjur

Hasan

2023

Chemie Ingenieur Technik

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Identifying sustainable chemical processes often depends on the choice of enabling materials that directly influence the overall performance. Matching property targets while incorporating adequate process knowledge is essential for optimal material selection. Multi-scale decisions need to be taken simultaneously to determine the optimal process configurations, operating conditions, and material structures. Integrating molecular to process scale decisions within an equation-oriented optimization framework leads to large-scale mixed-integer nonlinear programs (MINLP). Over the years, several solution approaches have been suggested to tackle this issue. Here, the current state-of-the-art in the field of computer-aided molecular and process design (CAMPD) is discussed and key challenges and open questions are highlighted that may stimulate future research.

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“…Quantitative structure-property relationship (QSPR) modeling is one of the most effective approaches for estimation of a variety of physicochemical [1][2][3][4][5][6] and toxicological properties [7][8][9] which can provide significant information on the molecular features determining the properties of chemical compounds. The first step in a QSPR study is quantifying chemical structure of the molecules by numerical codes named descriptors that can show structural similarity and diversity of the molecules.…”

Section: Introduction *mentioning

confidence: 99%

QSPR study on the boiling points of aliphatic esters using the atom-type-based AI topological indices

Osaghi¹,

Safa²

2019

Rev.Roum.Chim.

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In this work, normal boiling points (NBPs) for a group of aliphatic esters were modeled using a combination of the modified Xu (m Xu) and the atom-type-based AI topological indice. The multiple linear regression model consisting of m Xu, AI(-CH 3) and AI(-O-) showed the squared correlation coefficient, Fisher ratio and standard error values of 0.994, 6705.7 and 4.54, respectively. Statistical validity of the model was verified by the external validation technique. Based on the results, fraction contribution of the topological indices entered the model decreased in the order of m Xu > AI(-CH 3) > AI(-O-) showing that NBPs of aliphatic esters are mainly dominated by molecular size, and degree of branching and polarity of the molecules have smaller contributions to the normal boiling points.

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A new QSPR-based prediction model for biofuel lubricity

Cited by 22 publications

References 24 publications

Lubricity of Ethanol–Diesel Fuel Blends—Study with the Four-Ball Machine Method

Lubricity of Ethanol–Diesel Fuel Blends—Study with the Four-Ball Machine Method

An Overview of Computer‐aided Molecular and Process Design

QSPR study on the boiling points of aliphatic esters using the atom-type-based AI topological indices

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