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 AbstractReducing 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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