Automatic generation and updating of process industrial digital twins for estimation and control - A review - Birk, Wolfgang; Hostettler, Roland; Razi, Maryam; Atta, Khalid; Tammia, Rasmus (Frontiers in Control Engineering, 2022)
This review aims at assessing the opportunities and challenges of creating and using digital twins for process industrial systems over their life-cycle in the context of estimation and control. The scope is, therefore, to provide a survey on mechanisms to generate models for process industrial systems using machine learning (purely data-driven) and automated equation-based modeling. In particular, we consider learning, validation, and updating of large-scale (i.e., plant-wide or plant-stage but not component-wide) equation-based process models. These aspects are discussed in relation to typical \wb{application cases} for the digital twins creating value for users both on the operational and planning level for process industrial systems. These \wb{application cases} are also connected to the needed technologies and the maturity of those as given by the state of the art. Combining all aspects, a way forward to enable \ka{the} automatic generation and updating of digital twins is proposed, outlining the required research and development activities. The paper is the outcome of \ka{the} research project AutoTwin-PRE funded by Strategic Innovation Program PiiA within the Swedish Innovation Agency VINNOVA and the academic version of an industry report prior published by PiiA.