Steady state identification for on-line data reconciliation based on wavelet transform and filtering

• Online methodology that efficiently detects near steady state operating conditions.
• The wavelet method identifies steady state using three signal processing steps.
• Reduction of type I and type II errors related to the steady state detection.
• The optimum WT cutting scale/parameters are pre-determined from historical data.
• Practical approach to extract multivariable and plant-wide steady-state periods.

In order to derive higher value operational knowledge from raw process measurements, advanced techniques and methodologies need to be exploited. In this paper a methodology for online steady-state detection in continuous processes is presented. It is based on a wavelet multiscale decomposition of the temporal signal of a measured process variable, which simultaneously allows for two important pre-processing tasks: filtering-out the high frequency noise via soft-thresholding and correcting abnormalities by analyzing the maximums of wavelet transform modulus. Wavelet features involved in the pre-processing task are simultaneously exploited in analyzing a process trend of measured variable. The near steady state starting and ending points are identified by using the first and the second order of wavelet transform. Simultaneously a low filter with a probability density function is employed to approximate the duration of a near stationary condition. The method provides an improvement in the quality of steady-state data sets, which will directly improve the outcomes of data reconciliation and manufacturing costs. A comparison with other steady-state detection methods on an example of case study indicates that the proposed methodology is efficient in detecting steady-state and suitable for online implementation.