Real-time reconciled simulation as decision support tool for process operation

Decision support tools in the process industry have been gaining relevance, especially for operation under uncertain conditions. This study describes real-time reconciled simulation (RTRS), and analyzes its usefulness as decision-making tool for process operators, especially under unexpected process changes. The proposed methodology is implemented in two case studies in the context of an oil refinery hydrogen network, where both plant and network levels are considered. A what-if analysis is conducted on two case studies, assessing two feasible mitigation actions for each case baseline condition. The focus of the discussion is, nevertheless, on the methodology itself and its general features as decision support tool. The relative error of RTRS for estimation of states and parameters, considering unmeasured disturbances, is satisfactory aligned with industrial standards for online measurements. In terms of mitigation actions, these are assessed with regards to its economic impact on the system in question. It is shown how actions at plant level may be disadvantageous when facing hydrogen demand changes, compared to network-wide mitigation actions. At plant level, it is pointed out the importance of purification units, prevailing over hydrogen make-up for mitigation of demand change. It is highlighted the fact that RTRS complements in a straightforward manner other control operation tools such as model predictive controllers (MPC) and real-time optimizers (RTO). Therefore, it may add to any decision support framework an open-loop component with parameter estimation and forecasting capabilities. Moreover, its potential for training and integration within other tools packages is discussed. Future directions of research are commented such as fully integrated decision support frameworks, including RTRS, MPC and RTO. Additionally, how RTRS may relate to digital twins, including an example of a suitable architecture is introduced, and RTRS role in enterprise-wide decision-making solutions is commented. (C) 2021 The Author(s). Published by Elsevier Ltd.