Review of Multiscale Methods for Process Monitoring, With an Emphasis on Applications in Chemical Process Systems

Abstract

Process monitoring has played an increasingly significant role in ensuring safe and efficient manufacturing operations in process industries over the past several years. Chemical process data is highly correlated and has multiscale characteristics in general. Extensive work has been carried out to overcome this concern for multiscale process monitoring of process plants during the past two decades. The recent success of multiscale methods in monitoring and controlling manufacturing processes has sparked interest in investigating these methods for process monitoring. This article aims to present a concise and critical overview of the applications of multiscale process monitoring methods in chemical processes. First objective is to identify the importance of multiscale methods for process monitoring. The second and main objective is the statistical and critical analysis of methods implementation, application area, types of data used, and various issues mentioned by previous researchers. In addition, the most important critical issues have been identified, and the capabilities and limitations of each method are discussed and highlighted. The reported literature focus mainly on fault detection and do not investigate the root-cause diagnosis of the detected faults. Further, the challenges and prospects in multiscale process monitoring in the chemical process industry have been discussed for advancement.

Keywords

• Chemical process systems
• feature extraction
• fault detection
• fault diagnosis
• multivariate statistical process monitoring
• multiscale process monitoring