多模态过程中新模态过程建模方法

Modeling algorithm for new mode in multi-mode process

多模态过程中新出现的模态过程短期内无法获得充足的建模数据,且传统统计控制方法无法有效地估计过程特性.鉴于此,提出一种基于历史模型数据相关特性建立初步模型的方法,充分利用已有多模态历史数据的相关特性,从历史数据中寻找与当前数据特征相似的数据进行补充,建立初始模型,并利用新积累的数据迭代初步模型,逐步实现准确描述过程特性的算法.通过在田纳西-伊斯曼过程中的大量仿真,表明了所提出方法的可行性和有效性.

The new mode in multi-mode process can not get enough modeling data in short-term process. And it is hard for traditional statistical methods to extract process characteristics effectively. Therefore, a new algorithm is proposed to solve this problem. The initial model is established based on the characteristics of history models. Historical data with the similar characteristics are found to supply the new modeling data. The iterative model based on the initial model is performed to fit the process more accurately. A large number of simulations in Tennessee Eastman process show the feasibility and effectiveness of the proposed method.