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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.

Mean voidage is a global structural property of packed beds and its accurate prediction is therefore of great significance in any plug‐flow type model. A general correlation has been developed which enables this parameter to be evaluated. The work reported here deals with mono‐size non‐porous spherical packing, but also tackles related issues such as size distribution and the way in which they influence the mean voidage. In addition, an attempt has been made to discuss the merits of alternative packing arrangements.

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