Rationalization and Prediction of the Equivalent Alkane Carbon Number (EACN) of Polar Hydrocarbon Oils with COSMO-RS σ-Moments

Abstract

The equivalent alkane carbon numbers (EACNs) of 20 polar hydrocarbon oils are determined by the fishtail method. These values supplemented by 43 already reported EACNs of other hydrocarbons are rationalized by using the COSMO-RS σ-moments as descriptors for a QSPR analysis. A reliable model, with only two meaningful physicochemical parameters, namely the surface area (M0(X)) and the overall polarity (M2(X)) of the oil X, is able to predict the EACN values of a large variety of oils including (cyclo)alkanes, (cyclo)alkenes, terpenes, aromatics, alkynes, and chloroalkanes and to rationalize structural effects on EACNs. Furthermore, the dependence of the EACN of homologous oils on the chain length provides some molecular insight into how the different oils penetrate into the interfacial film of surfactants.

Extended Surfactants Including an Alkoxylated Central Part Intermediate Producing a Gradual Polarity Transition—A Review of the Properties Used in Applications Such as Enhanced Oil Recovery and Polar Oil Solubilization in Microemulsions

“…The results of research carried out in the first decade of the 21st century (Do et al, ) corroborated the polar oil EACN with different kinds of extended surfactants, for instance, of soya oil at EACN = 17.7 ± 0.5, in striking agreement with the originally reported value (Miñana‐Pérez et al, ) of EACN = 18 ± 1. This kind of result clearly indicates that an ester group decreases the EACN by about 10–12 units with respect to the total number of carbon atoms in a tail, a clear consequence of its polarity contribution to the oil (Engelskirchen et al, ; Lai et al, ; Lukowicz et al, , ; Ontiveros et al, ).…”

How to Attain Ultralow Interfacial Tension and Three‐Phase Behavior with Surfactant Formulation for Enhanced Oil Recovery: A Review. Part 4: Robustness of the Optimum Formulation Zone Through the Insensibility to Some Variables and the Occurrence of Complex Artifacts

“…Decades of studies have reported more detailed effects and thus more precise variable contributions, including the non-alkane oil equivalent ACN effect (so-called EACN) [2,[12][13][14][15][16][17][18][19][20][21][22][23], and details on the alcohol co-surfactant effect f(A) [3-5, 24, 25] or the pressure effect [26][27][28][29][30], and even the influence of molecular variation in the surfactant structure characteristics such as the n-alkyl tail length, tail branching, ionic head group, polyethylene oxide or polypropylene oxide length [10,16,[31][32][33][34][35][36], or effect of a surfactant mixture composition [37,38]. There is no need to deal with all these details for the subject of this article, so only the variables indicated in Eq.…”

Equivalent alkane carbon number of crude oils: A predictive model based on machine learning

“…Bouton et al proposed a Quantitative Structure Property Relationship (QSPR) for the prediction of the EACN of hydrocarbons by means of two theoretical descriptors, i.e., the average negative softness and the Kier A3 [9], with EACN values in between À4 and +35. More recently, Lukowicz et al proposed a QSPR based on COSMO-RS r-moments to predict EACN of polar hydrocarbon oils [10] and then extended their model to the case of aprotic polar oils [11]. To determine EACN of hydrocarbon mixtures, Cayias et al [12] and Cash et al [13] proposed the use of a mixing rule in which individual hydrocarbon EACNs are weighted according to corresponding mole fractions.…”