Model-order reduction based on artificial neural networks for accurate prediction of the product quality in a distillation column
Model-order reduction based on artificial neural networks for accurate prediction of the product quality in a distillation column
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DOI：
10.1504/ijaac.2009.026780
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发表时间：
2009-06
期刊：
影响因子：
--
通讯作者：
Y. Chetouani
中科院分区：
文献类型：
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作者：
Y. Chetouani
本文的主要目的是建立一个可靠的模型的过程行为的稳态和非稳态制度。使用这种精确的模型可以区分正常模式和异常模式。因此，选择了基于外生非线性自回归模型的神经黑箱辨识。这项研究显示了另一种神经模型简化技术，考虑到过程的物理知识。的输入选择，时间延迟，隐藏的神经元及其对神经估计器的行为的影响进行了分析。在描述了系统架构之后，为了说明预测和模型简化的可靠性，提出了一个现实而复杂的蒸馏塔应用。识别和实验数据之间的协议令人满意的发现，结果表明，神经模型成功地预测了产品组成的演变。
The main aim of this paper is to establish a reliable model of a process behaviour 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 non-linear auto-regressive with exogenous model has been chosen. This study shows another technique for neural model reduction into account the physical knowledge of the process. An analysis of the inputs choice, time delay, hidden neurons and their influence on the behaviour of the neural estimator is carried out. After describing the system architecture, a realistic and complex application as a distillation column is presented in order to illustrate the reliability of the prediction and model reduction. Satisfactory agreement between identified and experimental data is found and results show that the neural model successfully predicts the evolution of the product composition.