This paper presents a case-study from an actual chemical plant involving twenty process variables, one of which reflects product quality. The analysis of the data was performed using MATLAB with the PLS_Toolbox, which includes a library of statistically-based chemometrics analysis routines, augmented with software developed at the University of Louisville to enhance the amount of diagnostic information available for fault analysis. In addition, a newly developed graphical user interface provides this information in a more comprehensive and understandable manner to the user. The enhanced algorithms are fully automated which make it much easier for non-technical and semi-technical people, such as plant operators, to use this methodology. In the case study, in several instances, the revised methodology was able to detect faults earlier than the basic chemometric approach, which would be a significant advantage in actual plant applications.
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