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Novel common and special feature extraction method for modeling multi-grade processes

机译:用于建模多级过程的新颖的公共特征和特殊特征提取方法

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In the processing industries, operating conditions often change to meet the requirements of the market and customers. To cope with the difficulty of on-line quality prediction for such multi-grade processes widely operated in process industries, a novel common and special feature extraction method is proposed for modeling multi-grade processes. A common feature extraction algorithm is proposed to determine the common directions shared by different grades of these processes. After extracting the common features, a partial least-squares modelling algorithm is used to extract the special directions for each grade, respectively. Hence, product quality prediction can be simply conducted by integrating the common and special parts of each grade for model building. A numerical case and an industrial polyethylene process are used to demonstrate the effectiveness and advantage of the proposed method.
机译:在加工业中,操作条件经常发生变化,以满足市场和客户的需求。为了解决在过程工业中广泛使用的这种多级过程的在线质量预测的困难,提出了一种新颖的通用和特殊特征提取方法来建模多级过程。提出了一种共同特征提取算法,以确定这些过程的不同等级所共有的共同方向。在提取共同特征之后,使用偏最小二乘建模算法分别提取每个年级的特殊方向。因此,通过集成每个等级的通用和特殊部分以进行模型构建,可以简单地进行产品质量预测。数值案例和工业聚乙烯工艺被用来证明该方法的有效性和优势。

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