In order to correlate and optimize experimental data either from the laboratory or industry, one needs a robust method of data regression. Among the non-linear parameter estimation methods it may be pointed out of Levenberg, which applies the conversion of an arbitrary matrix into a positive definite one. Later, Marquardt applied the same procedure, calculating λ parameter in an iterative form. The Levenberg-Marquardt algorithm is described and two routine for correlating vapor-liquid equilibrium data for pure component and mixtures, based on this efficient method, have been applied. The routines have been written with an interface very accessible for both users and programmers, using Python language. The flexibility of the developed programs for introducing the desired details is quite interesting for both process simulators and modeling properties. Furthermore, for mixtures with electrolytes, it was obtained a coherent and compatible relation for the structural parameters of the salt species, with the aid of the method and the graphical interface designed

Sorption experiments using the Aspergillus niger fungus for cadmium removal were carried out to study the factors influencing and optimizing the biosorption of this metal. The effects of pH, time, biomass concentration, and initial concentration of the heavy metal on the rate of metallic biosorption were examined. An experimental design was also used to determine the values of the under study variables that provided the greatest biosorption efficiency. A technique for biomass recovery was also developed with the objective of determining the capacity of the regenerated biomass to biosorb the metals in solution. This research proved that with a pH of 4.75, a biomass concentration of 0.7 g/L, and a heavy metal concentration varying between 5 and 10 mg/L a biosorption process of biosorption with Aspergillus niger could be successfully used for heavy metal removal from oil field water in the oil industry.