Engineering Optimization

Appendix DSteady‐State Identification in Noisy Signals

D.1 Introduction

Identification of both steady state (SS) and transient state (TS) in noisy process signals is important. Steady‐state models are widely used in process control, online process analysis, and process optimization; and since manufacturing and chemical processes are inherently nonstationary, selected model parameter values need to be adjusted frequently to keep the models true to the process and functionally useful. But either the use or data‐based adjustment of SS models should only be triggered when the process is at SS. Additionally, detection of SS triggers the collection of data for process fault detection, the data reconciliation, the neural network training, the end of an experimental trial (collect data and implement the next set of conditions), etc.

Often, process owners, scientists, and engineers run a sequence of experiments to collect data throughout a range of operating conditions, and process operators sequence the next stage of a trial. Each sampling event is initiated when the operator observes that steady conditions are met. Then the operator implements the new set of operating conditions. Similarly, in batch operations the end of a stage is evidenced by signals reaching their equilibrium or completion values, and when operators observe that the stage is complete, they initiate the next step in the processing sequence. However, this visual method of triggering requires continual human attention, ...