Big Data-Driven Contextual Processing Methods for Electrical Capacitance Tomography

This paper presents a new approach to analyzing measurement records from industrial processes. The proposed methodology is based on the model of contextual processing and uses big data from experimental process tomography datasets. Electrical capacitance tomography is used for monitoring noninvasive flow and for data acquisition. The measurement data are collected, stored, and processed to identify process regimes and process threats. A specific physical modification was introduced into the pneumatic conveying flow rig in order to study flow behavior under extreme conditions, extending the available knowledge base. A support vector machine was applied for data classification. This study illustrates how contextual processing can facilitate data interpretation and opens the way for the development of methods for detecting pre-emergency flow patterns.