Process data analytic methods rely on the notion of sensor fusion whereby data from many sensors and alarm tags are combined with process information, such as physical connectivity of process units, to give a holistic picture of health of an integrated plant. The discovery and learning from process data refers to a set of tools and techniques for modeling and understanding of complex data sets.
The process industry is awash with all types of data archived over many years: sensor data, alarm data with operator actions to ‘navigate’ the process to operate at desired conditions and process models that are used for advanced control. The fusion of information from such disparate sources of process data is the key step in devising strategies for a smart analytics platform for safe and autonomous process operation. The purpose of this talk is to present results and strategies that will ultimately lead us to safe and optimal autonomous or semi-autonomous process operation.