Automatic steady state identification for batch processes by nonparametric signal decomposition and statistical hypothesis test

• The ensemble empirical mode decomposition technique is adopted for the decomposition of batch process signals.
• The inter-batch sub-signals are extracted for the steady state identification (SSID) of batch process.
• A multivariate SSID algorithm is proposed based on the statistical test for the equality of covariance matrices.
• The proposed SSID method can be utilized in the situation that process disturbances exist.

In chemical batch processes, online identification of the batch-to-batch steady state is important for ensuring consistency of final product quality and satisfactory process control. In this paper, an automatic steady state identification (SSID) method is developed for batch processes, which utilizes a nonparametric signal decomposition technique named ensemble empirical mode decomposition (EEMD) to extract related information contained in variable trajectories and then conducts a statistical hypothesis test. In the proposed method, EEMD is combined with a moving window procedure to decompose the signal of each variable trajectory into a finite number of intrinsic mode functions (IMFs) in real-time. Then, the inter-batch trend information is extracted by computing the instantaneous frequencies of each IMF. Using the variance ratio test, batch-to-batch steady state can be identified from the inter-batch trend of each process variable. Since most of the disturbance and noise information have been removed through EEMD, robust SSID result can be expected. To deal with the multiple process variables, a multivariate SSID algorithm is proposed based on the statistical test for the equality of covariance matrices. The effectiveness of the proposed method is demonstrated with an injection molding process.