Algerian Journal of Engineering Research
Volume 6, Numéro 1, Pages 44-54
2022-07-17

Interaction Parameters For Ternary Systems And Quaternary Using Particle Swarm Optimization With Two Cases: The Objective Functions In Terms Of Activities And The Objective Functions In Terms Of Mole Fractions

Authors : Hebboul Sabrina . Korichi Mourad . Hebboul Amel .

Abstract

In liquid-liquid equilibrium (ELL) calculations and modelling, there are several thermodynamic models to estimate binary interaction parameters from thermodynamic systems, such as an activity coefficient model (γ) based on the concept of local composition: Nonrandom Two-Liquid (NRTL) using experimental equilibrium data and minimizing by an objective function (OF). There are two types of the functions, namely: the objective functions in terms of activities 〖(OF〗_a ) and the objective functions in terms of mole fractions (〖OF〗_x). In this research study, a comparative study in the LLE calculation with modelling by the NRTL model using a stochastic optimization method, which is Particle Swarm Optimization (PSO), it is used to estimate the parameters from ternary and quaternary thermodynamic systems included a diluent {water}, a solute {ethanol} and an individual solvent or mixed solvents that are {dichloromethane (DCM), chloroform (CHCl3) or diethyl ether (DEE)}. Results had be affected at different objective functions previously mentioned by the Root-Mean-Square Error (RMS) that is calculated to determine the accuracy of the fit between experimental equilibrium data and calculate data predicted by the activity coefficient model. Consequence, the parameters of thermodynamic model have influenced by the chosen objective function. Therefore, the best results were been when using the hybrid (PSO-〖OF〗_x). In addition, the best individual solvent has been diethyl ether (DEE) and the best-mixed solvent has been {50% dichloromethane (DCM) +50% diethyl ether (DEE)} in the ternary and quaternary systems respectively.

Keywords

Nonrandom Two-Liquid model ; Objective Function ; Particle Swarm Optimization ; Thermodynamic systems