Development of Digital Twins for Continuous Processes: Concept Description of Virtual Mass Balance Based on the Tennessee Eastman Process

In this work, the authors describe a concept called Virtual Mass Balance and its applicability to oil and gas fields. This study relies on data modelling and data engineering to enrich operational data and achieve digitized Material Balance Tables for monitoring and benchmarking purposes. A data-driven set of stream properties allow for the calculation of newly proposed soft sensors, which are subsequently used for predictive functionalities. The implementation of this concept has proven popular in an industry set of tools, AVEVA PI. The concept could be classified as an Enriched Digital Twin. Incorporating the DEXPI Process model and deploying proposed soft sensors could, on the one hand, combine both asset and operational characteristics. On the other hand, its simplicity and moderate level of detail can be much easier to implement and would show flexible applicability at various corporate levels. The application of the Tennessee Eastman Process proves its flexibility. The presented soft sensors exhibit good monitoring capabilities and, furthermore, allow for the application of data-driven artificial intelligence methods without a reduction in model clarity for operational staff.