Abstract:

Traditional time difference (TD) model may deteriorate as time increases. In order to enhance reliability and prediction accuracy of TD model, an adaptive soft sensor was proposed on the basis of a selective ensemble of local time difference Gaussian process regression (LTDGPR) models and consideration of process time-delay characteristics. First, data for modelling was reconstructed by time delay and dynamic information extracted from database. Then, an adaptive localization step was used to statistically classify the reconstructed time-difference dataset and to establish an LTDGPR model set. For new input samples, prediction of dynamic drift for primary variables at certain time lapse was achieved through selective ensemble of LTDGPR models which had strong generalization capability. Finally, spontaneous online prediction of primary variable was achieved on the basis of TD model theory. Simulation results of a real debutanizer process indicated the effectiveness and accuracy of the proposed soft sensor.

Key words: selective ensemble, TD model, parameter identification, dynamic modelling, chemical processes

摘要：

随着时间的增加，传统时间差（TD）模型会出现性能显著下降的问题。为了提高TD模型的可靠性和预测精度，同时考虑过程的时滞特征，基于一种选择性集成策略，提出一种局部时间差高斯过程回归（LTDGPR）模型的自适应软测量建模方法。首先，提取出数据库中的时滞动态信息，对建模数据进行重构；然后，采取局部化策略对差分后的重构样本进行统计划分，得到LTDGPR模型集。对于新来的输入样本，选择部分泛化能力强的LTDGPR模型进行集成，估计出含一定时间差的主导变量动态偏移值；最后，基于TD模型思想对当前时刻主导变量值进行在线预测。通过脱丁烷塔过程的数据建模仿真研究，验证了所提方法的有效性和精度。

关键词: 选择性集成, 时间差模型, 参数识别, 动态建模, 化学过程