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Wiener Model Identification Of Blast Furnace Ironmaking Process

机译：高炉炼铁工艺的维纳模型辨识

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摘要

To account for the nonlinearity of blast furnace ironmaking process, a nonlinear Wiener model identification algorithm is presented. The system consists of a linear time invariant (LTD subsystem followed by a static nonlinearity. The inverse of the nonlinearity is assumed to be a linear combination of known nonlinear basis functions and the linear subspace algorithm is used to identify the model. The inputs to the model are parameters regarded to be most responsible for the fluctuation of thermal state in blast furnace while the output to the model is silicon content in hot metal. The identified Wiener model is then tested on datasets obtained from No. 6 Blast Furnace from Baotou Steel. It is found that the blast furnace of concern is a short memory system, so that for each prediction the Wiener method is retrained. It is shown that the retrained model well improves the predictive accuracy.
机译：为解决高炉炼铁过程的非线性问题，提出了一种非线性维纳模型辨识算法。该系统由一个线性时不变（LTD子系统）和一个静态非线性组成。非线性的逆假设是已知非线性基函数的线性组合，并且使用线性子空间算法来识别模型。该模型的参数被认为是导致高炉热态波动最主要的参数，而模型的输出是铁水中的硅含量，然后对确定的维纳模型进行测试，该模型是从包头钢铁公司的6号高炉获得的。发现所关注的高炉是一个短存储系统，因此对于每个预测，维纳方法都需要重新训练，表明重新训练的模型可以很好地提高预测精度。

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