A Dynamically Reconciled Digital Twin for Operations Optimization and Decision Support

Abstract

Operational digital twins bring together digitalization technologies, including machine learning, IIoT, data analytics, process simulation and optimization. A digital twin model reconciled with real-time process data provides the foundation for a range of layered applications that are key to transforming the way that process plants and value chains are operated and managed. After outlining the basic numerical methods, this paper will describe three industrial applications of dynamically reconciled digital twin models in gas processing, refining and olefins production. The focus is on application robustness and high availability, delivering increased operating margins and plant flexibility. An operational digital twin model can provide a versatile tool for real-time process optimization, and what-if analysis, as well as operations planning and scheduling. To be effective, the digital twin model needs to be reconciled with real-time process data frequently. In a dynamic operating environment where feed composition, process conditions and product demand may change continuously, this challenging data reconciliation task is solved using a combination of numerical methods: Equation-oriented model structures and solvers provide the flexibility required to solve simulation, parameter estimation and optimization cases. Extended Kalman filter and moving horizon estimation enable reconciliation using real-time dynamic data without the need for steady-state operation. Process models are developed using both first principles and data-driven surrogate modelling methods. A dynamically reconciled digital twin model provides the basis for an equation-oriented multi-period optimizer that can be solved for facility-wide steady state optimization, short-range dynamic optimization and long-range planning and scheduling problems within a single unified modelling and optimization framework. The use of hybrid models that include both data-driven and first principles components can significantly reduce the deployment and maintenance effort, the resulting low-order models facilitate model reuse across the different optimization horizons and objectives and enable large-scale optimization problems to be solved across an entire process plant or value chain. Three industrial applications of these methods will be outlined. The first is a gas plant optimizer that provides real-time process optimization and what-if analysis. The reconciled gas plant model tracks the process under significant feed and ambient variations. The second application is a refinery-wide optimizer that encompasses multiple crude distillation, vacuum and reaction units. The third application is an olefins process scheduling application that optimizes feed distribution, cracking furnace operating conditions and decoke cycle over an extended future time horizon.