Title page for ETD etd-072099-122832


Type of Document Master's Thesis
Author Sozio, John Charles
URN etd-072099-122832
Title Intelligent Parameter Adaptation for Chemical Processes
Degree Master of Science
Department Electrical Engineering
Advisory Committee
Advisor Name Title
VanLandingham, Hugh F. Committee Chair
Bay, John S. Committee Member
Rony, Peter R. Committee Member
Keywords
  • Tennessee Eastman
  • decentralized process control
  • genetic algorithm
  • fuzzy logic
Date of Defense 1999-07-08
Availability unrestricted
Abstract
Reducing the operating costs of chemical processes is very beneficial in decreasing a company's bottom line numbers. Since chemical processes are usually run in steady-state for long periods of time, saving a few dollars an hour can have significant long term effects. However, the complexity involved in most chemical processes from nonlinear dynamics makes them difficult processes to optimize. A nonlinear, open-loop unstable system, called the Tennessee Eastman Chemical Process Control Problem, is used as a test-bed problem for minimization routines. A decentralized controller is first developed that stabilizes the plant to set point changes and disturbances.

Subsequently, a genetic algorithm calculates input parameters of the decentralized controller for minimum operating cost performance. Genetic algorithms use a directed search method based on the evolutionary principle of "survival of the fittest". They are powerful global optimization tools; however, they are typically computationally expensive and have long convergence times. To decrease the convergence time and avoid premature convergence to a local minimum solution, an auxiliary fuzzy logic controller was used to adapt the parameters of the genetic algorithm. The controller manipulates the input and output data through a set of linguistic IF-THEN rules to respond in a manner similar to human reasoning. The combination of a supervisory fuzzy controller and a genetic algorithm leads to near-optimum operating costs for a dynamically modeled chemical process.

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