Simultaneous Dynamic Validation/Identification of Mechanistic Process Models and Reconciliation of Industrial Process Data

Process models are subject to parametric uncertainty and raw process-instrumentation data are corrupted by systematic and random errors. In this work, we present a framework for dynamic parameter estimation and data reconciliation aiming at integrating model-centric support tools and industrial process operations. A realistic case-study for the rectification of the mass balance of an industrial continuous pulping system is presented. The incentive for gross-error estimation during model-based production accounting and inventory analysis is demonstrated.