Robust and reliable modelling for a distillation column
نویسنده
چکیده
The main aim of this paper is to establish a reliable model of a process behavior both for the steady-state and unsteady-state regimes. The use of this accurate model allows distinguishing a normal mode from an abnormal one. Therefore the neural black-box identification by means of a NARX (Nonlinear Auto-Regressive with eXogenous) model has been chosen. It shows the choice and the performance of the neural network. The model is implemented by training a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) with input-output experimental data. After describing the system architecture, a realistic and complex application as a distillation column is presented in order to illustrate the neural model reliability.
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