Experimental studies and artificial neural network modeling of surface tension of aqueous sodium l-prolinate solutions and piperazine blends

Abstract

Abstract The surface tension study is very crucial for the design of CO2 gas absorption contacting equipment. The significance of the surface tension has been increasing due to its consideration in various technological fields. This property influences the mass transfer and hydrodynamics of gas absorption systems, mainly multiphase systems, in which the interface between gas and liquid exists. Therefore, in this study, surface tension of aqueous solutions of sodium l -prolinate (SP) and piperazine (PZ) blends were measured at ten different temperatures from (298.15 to 343.15) K. The SP mass fractions were 0.10, 0.20, and 0.30; while the mass fractions of PZ were 0.02 and 0.05. The experimental results showed that the surface tension increase with increasing the mass fractions of SP and PZ in aqueous blends, and decrease linearly with rising temperature. The experimental data of surface tension were correlated by two empirical correlations as a function of temperature and mass fractions for estimating the predicted data using the optimized correlation coefficients. Moreover, the modeling of surface tension data was carried out using Artificial Neural Network (ANN) approach. The results obtianed from ANN modeling were compared with applied empirical correlation. It was found that the ANN approach outperformed the empirical correlation used in this study. Besides, a quantitative analysis of variation (ANOVA) was performed in order to determine the significance of data. The surface tension of aqueous SP and SP + PZ was also compared with various conventional solvents.