A genetic algorithm approach for parameter estimation in vapour-liquid thermodynamic modelling problems

Parameter estimation of semi-empirical models for vapour - liquid equilibrium (VLE) data modelling, plays an important role in the design, optimization, and control of separation units. Conventional optimisation methods are very sensitive to the initial guesses of the unknown parameters and often fail to converge to the global optimum of the parameter estimation nonlinear mathematical programming problems. In this work we present an alternative evolutionary algorithm approach, based on genetic algorithm (GA) technologies, which can solve efficiently the nonlinear parameter estimation problem, finds the global optimum with high probability and most importantly is not sensitive to the initial estimates of the unknown parameters and the tuning parameters of the method. The proposed approach is evaluated and compared with conventional optimisation methods in nine VLE modelling problems of increased complexity. The results illustrate the efficiency and robustness of the proposed method. (C) 2019 Elsevier Ltd. All rights reserved.