Multi-Block Predictive Monitoring of Continuous Processes

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Abstract To ensure safe operation of continuous processes and to produce consistently high quality products, 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 to monitor process behavior. In this paper, a monitoring approach utilizing multi-way principal component analysis (MPCA) is studied. The method overcomes the assumption that the system is at steady state and it provides a predictive monitoring approach for continuous processes. The proposed approach using MPCA models can predict faults in advance of traditional monitoring approaches. A multi-block extension of the basic MPCA method is presented. The main focus of this paper is on the monitoring of multi-block continuous dynamic processes. The Tennessee Eastman process is used for illustrating the new approach.