In previous papers, we have proposed specific correlations to reproduce the surface tension values for several sets of fluids and for wide ranges of temperatures. In this paper, we focus our attention on alcohols and consider the available data for 152 fluids grouped into five families. We use the most recent versions of the DIPPR and DETHERM databases and also Wohlfarth and Wohlfarth’s (1997) book as our main sources of data. In some cases we have also considered new data. All the data have been carefully filtered in order to discard those that are in clear disagreement with most of the available data for the same fluid. In some cases, two or more different data sets versus temperature trends were observed, and where possible we considered only one of these trends. To fit the finally selected data, we used the model currently implemented in National Institute of Standards and Technology’s refprop program, calculating two, four, or six adjustable coefficients for each fluid. As a result, we proposed recommended correlations for 147 alcohols, providing mean average percentage deviations below 1.7% for each fluid, with only two exceptions: 1,2-butanediol (2.77%) and benzyl alcohol (3.20%).

In the parameter estimation of limit extreme value distributions, most employed methods only use some of the available data. Using the peaks-over-threshold method for Generalized Pareto Distribution (GPD), only the observations above a certain threshold are considered; therefore, a big amount of information is wasted. The aim of this work is to make the most of the information provided by the observations in order to improve the accuracy of Bayesian parameter estimation. We present two new Bayesian methods to estimate the parameters of the GPD, taking into account the whole data set from the baseline distribution and the existing relations between the baseline and the limit GPD parameters in order to define highly informative priors. We make a comparison between the Bayesian Metropolis–Hastings algorithm with data over the threshold and the new methods when the baseline distribution is a stable distribution, whose properties assure we can reduce the problem to study standard distributions and also allow us to propose new estimators for the parameters of the tail distribution. Specifically, three cases of stable distributions were considered: Normal, Lévy and Cauchy distributions, as main examples of the different behaviors of the tails of a distribution. Nevertheless, the methods would be applicable to many other baseline distributions through finding relations between baseline and GPD parameters via studies of simulations. To illustrate this situation, we study the application of the methods with real data of air pollution in Badajoz (Spain), whose baseline distribution fits a Gamma, and show that the baseline methods improve estimates compared to the Bayesian Metropolis–Hastings algorithm.

In previous papers, we have proposed specific correlations to reproduce the surface tension values for several sets of fluids and for wide ranges of temperatures. In this paper, we focus our attention on organic fatty (aliphatic, carboxylic, and polyfunctional) acids. We have taken into account the available data and values in the DIPPR and DETHERM databases and also Wohlfarth and Wohlfarth’s (1997) book. In some cases we have also considered new data published elsewhere. All the data and values have been carefully filtered and subsequently fitted with the use of the model currently implemented in NIST’s REFPROP program, calculating two or four adjustable coefficients for each fluid. As a result, we propose recommended correlations for 99 acids, providing mean absolute percentage deviations below 1.6% in all cases.