【摘要】 现代工业过程通常具有规模大、流程长和工序多的特点,导致传统的集中式建模方法会淹没过程的局部变化信息,从而无法及时识别早期的非优运行状态.此外,闭环控制的广泛应用使得过程变量普遍存在时序相关性.针对以上问题,提出一种基于慢特征分析(Slow feature analysis, SFA)的分布式动态工业过程运行状态评价方法.首先,结合动态时间规整(Dynamic time warping, DTW)和K-medoids聚类算法对过程进行分解;然后,对每一变量子块建立相应的动态慢特征分析(Dynamic slow feature analysis, DSFA)模型;最后,利用贝叶斯推理获得全局的综合评价指标.通过在数值案例和金湿法冶金过程的仿真应用,验证了该方法的有效性.更多还原

【Abstract】 The modern industrial processes are generally characterized by large scale, long processes and multiple procedures. In this case, the traditional centralized model may submerge the local change information of the processes, thus failing to identify the early non-optimal operation status in time. In addition, the wide application of closed-loop control brings the universal existence of temporal correlations of process variables. In view of the above problem, a distributed operating performance assessment scheme of dynamic industrial processes based on slow feature analysis(SFA) is proposed. First, the process decomposition is realized by combining dynamic time warping(DTW) and K-medoids clustering algorithms. Second, the corresponding dynamic slow feature analysis(DSFA)model is established for each sub-block. Finally, the overall comprehensive assessment index is obtained through Bayesian inference. The effectiveness of the scheme is verified by numerical examples and gold hydrometallurgy process.更多还原