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• W2488397973 abstract "The benefits of applying nonlinear control methods for control of certain types of nonlinear processes are paramount. In recent years, the acceptance of neural networks as a useful tool for modeling nonlinear processes has steadily increased. This paper introduces the use of neural networks in an Internal Model Control (IMC) strategy and a modified procedure for control action calculation. Usually, the controller is replaced by an on-line numerical inversion of the neural network model of the process dynamics. This method may lead to convergence problems especially when the process model shows poor dynamic prediction. In this research it is proposed to invert the process model in a static manner. This approach has been applied to two nonlinear chemical processes, an exothermic Continously Stirred Tank Reactor (CSTR) and a pH neutralization process. The newly proposed method yields stable responses over the entire (nonlinear) operating region, and can be further improved by the use of multistep ahead prediction calculations." @default.
• W2488397973 created "2016-08-23" @default.
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• W2488397973 date "1996-01-01" @default.
• W2488397973 modified "2023-09-23" @default.
• W2488397973 title "Neural network based control of chemical processes" @default.
• W2488397973 hasPublicationYear "1996" @default.
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