一种带过程变量时滞估计的在线软测量建模方法

An Online Soft-sensor Modeling Method Including Process Variable Time-delay Estimation

为了有效地将时滞信息引入到软测量建模过程中,同时实时跟踪过程动态,本文提出一种基于模糊曲线分析(FCA)估计过程时滞参数的新方法,用离线条件下得到的时滞参数集对软测量建模的数据进行重构;对于新的输入数据,基于一定时刻之前采集的历史变量值,采用时间差—高斯过程回归(TDGPR)模型对当前时刻主导变量值进行在线预测.通过对脱丁烷塔过程的仿真研究,验证了所提方法的有效性和精度.

We propose a novel method based on fuzzy curve analysis （FCA） in order to effectively introduce delay information into the soft-sensor model and track real-time process dynamics. The proposed method can estimate the process time delay parameter set, which is achieved offline and is then used to reconstruct the whole modeling sample set. When new input samples are available, a time difference Gaussian process regression （TDGPR） model is employed for current time online predictions based on historical variable values collected at certain moments. The proposed method is applied to a real debutanizer column process, and its effectiveness and accuracy are verified by the simulation results.