(371n) Empirical Model Synthesis From Data through Genetic Programming

Local thermodynamic models are practical alternatives to computationally expensive rigorous models that involve implicit computational procedures and often complement them to accelerate computation for run time optimization and control. Human-centered strategies for development of these models are based on approximation of theoretical models. This paper describes a fully data driven automatic self-evolving algorithm that builds appropriate approximating formulae for local models using genetic programming. No a-priori information on the type of mixture (ideal/non ideal etc.) or assumption is necessary. The level of synthesizing functional form and structure of models can be tailored. The performances of the local models developed by the methodology are compared to those with rigorous thermodynamic models using steady state and dynamic simulation of pertinent separations.