Plant-Wide Industrial Process Monitoring: A Distributed Modeling Framework

With the growing complexity of the modern industrial process, monitoring large-scale plant-wide processes has become quite popular. Unlike traditional processes, the measured data in the plant-wide process pose great challenges to information capture, data management, and storage. More importantly, it is difficult to efficiently interpret the information hidden within those data. In this paper, the road map of a distributed modeling framework for plant-wide process monitoring is introduced. Based on this framework, the whole plant-wide process is decomposed into different blocks, and statistical data models are constructed in those blocks. For online monitoring, the results obtained from different blocks are integrated through the decision fusion algorithm. A detailed case study is carried out for performance evaluation of the plant-wide monitoring method. Research challenges and perspectives are discussed and highlighted for future work.

I. Introduction

Plant-wide process monitoring has caught lots of attention from researchers in the industrial process systems engineering field in the past few years [1]–[3]. A typical plant-wide process consists of several different operation units, workshops, and manufacturing plants, or it is even operated in several factories across different areas. In order to provide a safe operating environment and produce products of high quality, monitoring plant-wide processes is quite necessary. While traditional principle model-based methods have been employed for monitoring industrial processes in the past few years, getting those models becomes very difficult and time consuming in plant-wide processes because of the large equipment structure, complex operation conditions, etc.