Nonlinear-parameter-estimation-in-thermodynamic-models

The reliable solution of nonlinear parameter estimation problems is an essential computational and mathematical problem in process systems engineering, both in on-line and off-line applications. Parameter estimation in semi-empirical models for vapor – liquid equilibrium (VLE) data modelling plays an important role in design, optimization and control of separation units. Conventional optimisation methods may not be reliable since they do not guarantee convergence to the global optimum sought in the parameter estimation problem. In this work we demonstrate a technique, based on genetic algorithms (GA), that can solve the nonlinear parameter estimation problem with complete reliability, providing a high probability that the global optimum is found. Two versions of stochastic optimization techniques are evaluated and compared for nine vapour - liquid equilibrium problems: our genetic base algorithm and a hybrid algorithm. Reliable experimental data from the literature on vapor - liquid equilibrium systems were correlated using the UNIQUAC equation for activity coefficients. Our results indicate that this method, when properly implemented, is a robust procedure for nonlinear parameter estimation in thermodynamic models. Considering that new globally optimal parameter values are found by using the proposed method we can surmise by our results that several sets of parameter values published in the DECHEMA VLE Data Collection correspond to local instead of global minima.