Application of qualitative trend analysis in fault diagnosis of entrained-flow coal-water slurry gasifier

Abstract

Entrained-flow coal-water slurry (CWS) gasification is a clean and efficient energy utilization technology and it was difficult to avoid misoperation during gasifier operation process. Qualitative trend analysis (QTA) is a data-driven method based on process history, and has been widely used in process monitoring and fault diagnosis. In this study, a suitable on-line basic primitive sequence processing method combining QTA and the extended sliding window trend extraction (SWTE) method is developed for the operation of entrained-flow CWS gasifier. Based on the optimized method, trend extraction and primitive sequence processing on three sets of data, i.e., gasifier temperature, gasifier pressure, and slag discharge hole pressure difference, were performed in this study. The optimized method could be effectively applied to the real-time monitoring and fault diagnosis of entrained-flow gasifier since its output was simple and accurate.