Efficient methodologies for real-time state identification during process transitions

Abstract

Continuous chemical plants have multiple steady state operating modes. Process monitoring, fault diagnosis and state identification during process transitions is an important task for plant operators and engineers. In this thesis, new methodologies for computationally efficient process state identification have been developed. Firstly, a new approach for temporal signal comparison has been developed. Process data is first segmented based on singular points. Dynamic programming and dynamic time warping (DTW) is used to find their optimal match and obtain the signal difference. Secondly, a new signal comparison-based approach, called dynamic locus analysis, for online state identification and fault diagnosis during process transitions has been proposed. A new method called state-specific key variables selection has also been developed for large-scale processes. All the methodologies proposed in this thesis have been tested using data from different kinds of agile operations. Their performance are compared with traditional methods and shown to be superior.