The analysis of connectivity and causality is becoming challenging as new technologies for advanced automation and smart use of energy create complex interactions between plant components. Currently, engineers need to combine information from disparate sources in order to make inferences about these connections, but as the scale of the facility increases, such an approach is not affordable.
My research makes use of concepts and theory from the analysis of graph networks and data-driven methods to develop tools for visualization of plant and equipment dependencies and representation of causality information. By including informatics in process operations, I am aiming to provide visualization techniques that are compact and interactive, and that retain relevant features that engineers need in order to infer and understand causality.
The outcome will be a visual analytics and information visualization platform, so the main results are intended to be software prototypes that derive connectivity information from a series of schematics and process data. These new visualization methods will provide an insight and a better understanding of plant and equipment topology with applications in plant supervision and control, process engineering, maintenance, and planning.
The picture above presents the use of Circos®, a tool originally designed for the analysis of genome connectivity, for the presentation of material and information connections between items in a chemical process. Such information visualization techniques can turn connectivity matrices into images, in a way, transforming zeroes and ones into an informative and visually compelling data graphics. In this approach, the matrix’s columns and rows are represented by segments around the circle, and links between them are represented by the colored ribbons.