Knowledge discovery through mining process operational data

In process plant operation and control, modem computer control and automatic data logging systems create large volumes of data, which contain valuable information about normal and abnormal operations, significant disturbances and changes in operational and control strategies. The data unquestionably provide a useful source of information for supervisors and engineers to monitor the performance of the plant and identify opportunities for improvement and causes of poor performance. This contribution describes the use of data mining and knowledge discovery techniques for automatic analysis and interpretation of process operational data both in real time and over the operating history. Techniques studied include data pre-processing using wavelets and principal component analysis, multivariate statistical analysis, and unsupervised machine learning approaches as well as inductive learning for conceptual clustering. Examples and industrial case studies are used to illustrate these methods.