| Summary: | Owing to the numerous benefits of process monitoring, the subject has attracted a lot of attention in the last two decades. Process monitoring is an art of identifying abnormal deviations in a process from the normal operating condition using various techniques. Generally, the development of these monitoring techniques is geared towards applying these techniques to industrial processes. In addition, most industrial processes are dynamic and non-linear in nature. Therefore, in the development of the monitoring algorithms, the dynamic as well as the non-linear properties of the plant should be taken into consideration. Process monitoring techniques like the Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression analysis were developed based on the assumption that the process data is normally distributed. Nevertheless, this assumption of normality is invalid for most industrial processes due to the non-linear nature of these plants. For such processes, the distribution of the process variables in general will be non-Gaussian, therefore making the widely applied PCA and PLS approaches inappropriate for the monitoring of plants. To address this limitation of the PCA and PLS for Dynamic processes, the Dynamic PCA (DPCA) and dynamic PLS (DPLS) approaches were developed. The challenge of efficiently monitoring process plants with dynamic and non-linear characteristics is the motivation for this study. The overall aim of this study is to develop process monitoring strategies that are able to take the dynamic and nonlinear properties of the plant into account. With these strategies, more efficient performance monitoring of the plant can be achieved. Cont/d.
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