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The control system performs poor in characteristics and even it becomes unstable, if improper values of the controller tuning constants are used. So it becomes necessary to tune the controller parameters to achieve good control performance with the proper choice of tuning constants. Many control problems involve simultaneous optimization of multiple variables that competing with each other. In this paper, the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) has been successfully applied to optimization of dynamic state of t-amyl-methyl-ether (TAME) reactive distillation process. This paper presents the tuning of Proportional-Integral-Derivative (PID) controllers by minimizing of two objective functions (overshoot and Integral of Absolute Error (IAE)) through the NSGA-II. Results show that genetic algorithm is more suitable method for optimal control of the TAME reactive distillation columns than traditional methods such as Tyreus-Luyben.  相似文献   
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Many control problems involve simultaneous optimization of multiple performance measures that are often non-commensurable and competing with each other. The presence of multiple objectives in a problem usually gives rise to one set of optimal solutions, largely known as Pareto-optimal solutions. In this paper, the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) has been successfully applied to optimization of dynamic state of simple distillation process. This paper presents the tuning of Proportional-Integral-Derivative (PID) controllers by minimizing of three objective functions (overshoot, response time, and Integral of Absolute Error (IAE)) through NSGA-II. A MATLAB code for real-parameter NSGA-II has been coupled with HYSYS v.3.1 process simulator for simulation and optimization of process. Optimization numerical results show that genetic algorithm is more suitable method for optimal control of distillation columns than traditional methods.  相似文献   
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