Control structure design and advanced process control

Graph-theoretic method for integrated design and control

Process monitoring using machine learning techniques

Statistical process monitoring is one of the most important research problems for modern process industry to ensure sustainable operation. The key step of statistical process monitoring is to define normal operating regions by applying statistical techniques to the original data samples obtained from the process systems. Typical examples of such techniques include principal component analysis (PCA), partial least squares (PLS), independent component analysis (ICA). Any data sample which does not lie in the normal operating region is then classified as a fault, and its root cause needs to be identified through fault diagnosis. Recently, deep learning and neural networks are also widely used for the statistical process monitoring, where both supervised and unsupervised learning algorithms are implemented. We are interested in developing efficient process monitoring frameworks by utilizing various machine learning techniques.

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