Predictive on-line monitoring of continuous processes

For safety and product quality, it is important to monitor process performance in real time. Since traditional analytical instruments are usually expensive to install, a process model can be used instead to monitor process behavior. In this paper, a monitoring approach using a multivariate statistical modeling technique, namely multi-way principal component analysis (MPCA), is studied. The method overcomes the assumption that the system is at steady state and it provides a real time monitoring approach for continuous processes. The monitoring approach using MPCA models can detect faults in advance of other monitoring approaches. Several issues which are important for the proposed approach, such as the model input structure, data pretreatment, and the length of the predictive horizon are discussed. A multi-block extension of the basic methodology is also treated and this extension is shown to facilitate fault isolation. The Tennessee Eastman process is used for demonstrating the power of the new monitoring approach.