This paper presents a new approach to near-infrared spectral (NIR) data analysis that is based on independent component analysis (ICA). The main advantage of the new method is that it is able to separate the spectra of the constituent components from the spectra of their mixtures. The separation is a blind operation, since the constituent components of mixtures can be unknown. The ICA based method is therefore particularly useful in identifying the unknown components in a mixture as well as in estimating their concentrations. The approach is introduced by reference to case studies and compared to other techniques for NIR analysis including principal component regression (PCR), multiple linear regression (MLR), and partial least squares (PLS) as well as Fourier and wavelet transforms.

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In process plant operation and control, modern distributed control and automatic data logging systems create large volumes of data that contain valuable information about normal and abnormal operations, significant disturbances, and changes in operational and control strategies. These data have tended to be underexploited for a variety of reasons, including the large volume and lack of effective automatic computer-based support tools. This paper considers a data mining system that is able to automatically cluster the data into classes corresponding to various operational modes and thereby provide some structure for analysis of behavioral responses. The method is illustrated by reference to a case study of a refinery fluid catalytic cracking process.

Multivariate statistics and unsupervised machine learning have recently been studied by many researchers for process monitoring and fault diagnosis. These approaches often depend on calculating a similarity or distance measure to group data sets into clusters. Apart from giving predictions, they are not able to give causal explanations on why a specific set of data is assigned to a particular cluster. In this work, a conceptual clustering approach is presented for designing state space based monitoring systems, which is able to generate conceptual knowledge on the major variables which are responsible for clustering, as well as projecting the operation to a specific operational state. A critical issue in this approach is how to conceptually represent dynamic trend signals. For this purpose, principal component analysis is used for concept extraction from real-time dynamic trend signals. The method is introduced using a continuous stirred tank reactor as a case study. Application of the approach to a refinery methyl tert-butyl ether process is also presented.