Contextual data modeling and dynamic process intervention for industrial plants

Abstract

Systems and methods are disclosed herein for contextual data analysis and proactive intervention in industrial plant processes. Each of multiple data streams in an industrial plant are mapped to a common hierarchical data structure, wherein the data streams correspond to respective values or states associated with unit operations, assets, and process streams in the plant. The mapped data streams define hierarchical process relationships between subsets of the unit operations, assets, and process streams. Real-time data is collected to populate at least one level of the hierarchical data structure for certain data streams, wherein future outcomes are predicted for downstream operations based on the collected real-time data for at least one data stream, and at least one other data stream having a defined hierarchical process relationship therewith. Upon ascertaining that predicted future outcomes correspond to issues requiring intervention, output signals are generated based thereon for operator alerts and/or automated control.