Discussion on Time Difference Models and Intervals of Time Difference for Application of Soft Sensors
Discussion on Time Difference Models and Intervals of Time Difference for Application of Soft Sensors
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DOI：
10.1021/ie302582v
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发表时间：
2013-01
影响因子：
4.2
通讯作者：
H. Kaneko;K. Funatsu
中科院分区：
文献类型：
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作者：
H. Kaneko;K. Funatsu
在化工厂中，软测量被广泛用于估计难以在线测量的过程变量。由于化工装置的状态变化，软测量的预测精度随着时间的推移而下降，因此已经构建了基于时间差（TD）的软测量模型。然而，基于TD的模型的许多细节仍有待澄清。本文从数据的噪声、过程变量的自相关性、预测精度等方面对TD模型进行了讨论，从理论上阐明了TD模型与正态模型预测精度的差异，以及噪声、自相关性、TD区间等因素对TD模型预测精度的影响。通过对仿真数据的分析，验证了这些关系式和公式的正确性.通过对动态模拟数据和真实的工业数据的分析，证实了TD模型的预测精度随着TD间隔的优化而提高。
In chemical plants, soft sensors are widely used to estimate process variables that are difficult to measure online. The predictive accuracy of soft sensors decreases over time because of changes in the state of chemical plants, and soft sensor models based on time difference (TD) have been constructed. However, many details of models based on TD remain to be clarified. In this study, TD models are discussed in terms of noise in data, autocorrelation in process variables, predictive accuracy, and so on. We theoretically clarify and formulate the differences of predictive accuracy between normal models and TD models and the effects of noise, autocorrelation, TD intervals, and so on on the predictive accuracy. The relationships and the formulas were verified by analyzing simulation data. Furthermore, we analyzed dynamic simulation data and real industrial data and confirmed that the predictive accuracy of TD models increased when TD intervals were optimized.