A plot showing surface tension as function of pH showed quite similar relative changes in pH 5-9. Johlin measured using sessile bubble method with different timing (hours) to expect equilibrium.
However, the pattern isn’t the same as Sugden’ result that was imposed over the same plot.
A Thermodynamic Model for Low Interfacial Tensions in Alkaline Flooding. Sharma, Mukul M.; Yen, T.F. SPE Journal. Volume 23, Number 1. February 1983
Many experimental studies have been undertaken to measure interfacial tensions (IFT’s) as a function of pH, salinity, temperature, and divalent ion concentrations.
The molecular approach involves a statistical mechanical calculation of the intermolecular forces operating at the interfaces between two phases.
This citing is probably too old (1983)
Sensitivity analysis of interfacial tension predictions for hydrocarbon fluids. DANDEKAR Abhijit Y. Petroleum science and technology. 2004, vol. 22, no9-10, pp. 1161-1172 [12 page(s) (article)] (11 ref.)
The interfacial tension (IFT) of hydrocarbon fluids is commonly predicted by either the parachor method or the scaling law. The methods require equilibrium liquid and vapor phase composition and density. An equation of state would normally be required if experimental values are not available. However, the computation of density for simple hydrocarbons and reservoir fluids, despite the important advances achieved by cubic equations of state, still remains a weak link in these types of calculations. Thus, there exists a need to investigate the qualitative and quantitative effects, of such inaccuracies in the density, on IFT predictions. Moreover, the study presented in this work would be useful in reservoir engineering and enhanced oil recovery calculations. The results presented in this work indicate that the methods are highly sensitive to the inaccuracies in the density of both the liquid and the vapor phases. An error of around 10% in the liquid or the vapor density can result in an error of up to 200% in the estimated IFT. Two binary and one ternary mixture for which measured data on IFT, composition and density is reported in the literature form the basis of this study.
Neural Network Prediction of Interfacial Tension at Crystal/Solution Interface. K. Vasanth Kumar. Ind. Eng. Chem. Res., 2009, 48 (8), pp 4160–4164
Using (1) solubility, (2) molecular weight, and (3) density, a three-layer feed-forward neural network was constructed and tested to predict the IFT at the crystal/solution interface. The concentration of solute in liquid phase, (1) concentration of solute in solid phase, (4) temperature, (3) density and (2) molecular weight of crystal were used as inputs to predict the interfacial tension at the crystal/liquid interface (σSL). The network was trained using the solubility information for 28 systems to predict the σSL value and was validated with 29 new systems. Despite the limited number of data used for training, the neural network was capable of predicting σSL successfully for the new inputs, which are kept unaware during the training process. The σSL value that is predicted by the artificial neural network during the training and testing process was compared with σSL predicted from the widely used empirical expression. For most of the systems, ANN better predicts IFT.
Statistical Mechanics Basis of Macleod’s Formula. M.-E. BOUDH-HIR and G.A. MANSOORI. Journal of Physical Chemistry, Volume 94, pp.8362-8364, 1990.
The famous formula of Macleod, relating the surface tension of a liquid in equilibrium with its own vapor to the one-particle densities in the two phases of the system, is derived. Proved using the statistical- mechanical definition of the surface tension.
(Later on Mansoori worked with Escobedo)
Use of a spectrophotometer for biodiesel quality sensing.
Paper number 053133, 2005 ASAE Annual Meeting. Artur Zawadzki, Dev Shrestha, Brian He.
The test procedures to assure ASTM biodiesel quality are not being widely implemented because of the lengthy procedures and laboratory equipment requirements. A critical need in the increasingly emerging biodiesel industry right now is a reliable, affordable and rapid test method for determining the blends of biodiesel in diesel fuel. As an effort to explore a reliable and rapid method, a spectrophotometer was used to scan the blends of #2 fossil diesel and biodiesel for spectrums in the wavelength range of 190-1100 nm.
The shape of the spectrum curve was found to be different for different biodiesel feedstock where as relative absorbance and characteristic peaks of absorbance curve was attenuated with increasing amount of diesel in the blend. Shape characteristics were fed into neural network to predict the biodiesel feedstock and blend level in biodiesel-diesel mixture.
Escobedo and Mansoori (AIChE J. 42(5): 1425, 1996) Found original paper: check Escobedo 1996
The proposed 1996 paper relates the surface tension of mixtures to bulk-phase concentrations and properties (i.e. densities). Surface tension of pure organic fluids σ:
![\sigma=[P(\rho_l-\rho_v)^4]](http://lakm.us/thesit/wp-content/uploads/eq_90c5333f1eb13eb375ab39bad2a55fd3.png)
The two ρ are densities: liquid and vapour.
They introduced parachor (P), a new expression, derived from statistical mechanics (from Macleod equation) which represents the experimental surface tension of 94 different organic compounds within 1.05 AAD% (average absolute deviations).
(Kumar 2005) It expresses the surface tension of a liquid in equilibrium with its own vapour. Historical reference for parachor cited here: Macleod, Sugden, Quayle, Escobedo.
Surface Tension Prediction for Liquid Mixtures
Joel Escobedo and G. Ali Mansoori.AIChE Journal, Vol. 44, No. 10, pp. 2324-2332, 1998.
Surface tension of pure organic fluids was proposed previously also by them in 1996. The same theory is extended to the case of mixtures of organic liquids.
Prediction of Surface Tension of Organic Liquids Using Artificial Neural Networks
D. Kumar, S. Gupta and S. Basu. Indian Chem Engr., Section A, Vol. 47, No. 4, October – December 2005.
A forward-feed back propagation neural network, based on the Levenberg-Marquardt optimization and gradient descent with momentum weight and bias method was used. The input parameters, e.g., density, refractive index and parachor, to the neural network were chosen from the previous studies on theoretical prediction of surface tension.
pstoluann.pdf
Prediction of Emulsion Stability via a Neural Network-Based Mapping Technique
Ubiratan F. de Souza, Frank H. Quina, and Roberto Guardani
Ind. Eng. Chem. Res., 2007, 46 (15), pp 5100–5107
…These tests are time-consuming and subject to visual inaccuracies between different operators…a neural network-based model is tested as a tool for predicting the emulsification properties of mixtures of surfactants, organic solvents,…