Batch-to-batch Steady State Identification via Online Ensemble Empirical Mode Decomposition and Statistical Test

In batch processes, online steady state identification (SSID) is important for ensuring the quality consistence of final products. This paperpresents a robust method for batch process SSID by the use of ensemble empirical mode decomposition (EEMD) and statistical test. First, EEMD and moving-window technique areadoptedto decompose batch process signalsinto multiple intrinsic mode function (IMF) components in real time. Then, by computingthe instantaneous frequencies of each IMFthroughthe generalized zero-crossing (GZC) method, the IMFs are divided into three levels corresponding to high-frequency noise, intra-batch variation, and inter-batch trend, respectively. By utilizing the inter-batch trend instead of the original signal in SSID, the identification results are robustto measurement noise and process disturbance. Injection molding, a typical batch process, is usedto demonstratethe proposed method.