Synthesis of local thermo-physical models using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Ying Zhang",
  title =        "Synthesis of local thermo-physical models using
                 genetic programming",
  school =       "Department of Chemical and Biomedical Engineering,
                 College of Engineering, University of South Florida",
  year =         "2008",
  address =      "USA",
  month =        "11 " # dec,
  keywords =     "genetic algorithms, genetic programming, Matlab, Data
                 mining, Symbolic regression, Function identification,
                 Parameter regression, Statistic analysis, Process
  URL =          "",
  URL =          "",
  URL =          "",
  size =         "139 pages",
  abstract =     "Local thermodynamic models are practical alternatives
                 to computationally expensive rigorous models that
                 involve implicit computational procedures and often
                 complement them to accelerate computation for real-time
                 optimization and control. Human-centred strategies for
                 development of these models are based on approximation
                 of theoretical models. Genetic Programming (GP) system
                 can extract knowledge from the given data in the form
                 of symbolic expressions. This research 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 assumptions are necessary. The approach
                 involves synthesis of models for a given set of
                 variables and mathematical operators that may relate
                 them. The selection of variables is automated through
                 principal component analysis and heuristics.

                 For each candidate model, the model parameters are
                 optimized in the inner integrated nested loop. The
                 trade-off between accuracy and model complexity is
                 addressed through incorporation of the Minimum
                 Description Length (MDL) into the fitness (objective)
                 function. Statistical tools including residual analysis
                 are used to evaluate performance of models. Adjusted
                 R-square is used to test model's accuracy, and F-test
                 is used to test if the terms in the model are
                 necessary. The analysis of the performance of the
                 models generated with the data driven approach depicts
                 theoretically expected range of compositional
                 dependence of partition coefficients and limits of
                 ideal gas as well as ideal solution behaviour. Finally,
                 the model built by GP integrated into a steady state
                 and dynamic flow sheet simulator to show the benefits
                 of using such models in simulation. The test systems
                 were propane-propylene for ideal solutions and
                 acetone-water for non-ideal.

                 The result shows that, the generated models are
                 accurate for the whole range of data and the
                 performance is tunable. The generated local models can
                 indeed be used as empirical models go beyond
                 elimination of the local model updating procedures to
                 further enhance the utility of the approach for
                 deployment of real-time applications.",
  notes =        "Supervisor: Aydin K. Sunol",

Genetic Programming entries for Ying Zhang