Determining a dynamic model for flotation circuits using plant data to implement a Kalman filter for data reconciliation

• A dynamic model is determined to implement a Kalman filter for data reconciliation.
• Model information is extracted from plant operating conditions and historical data.
• A flotation circuit simulator is employed as the case study.
• Implemented Kalman filter gives better estimates than sub-model based observers.

Data reconciliation is extensively applied to improve the accuracy and reliability of plant measurements. It relies on process models ranging from simple mass and energy conservation equations to complete causal models. The precision of reconciled data mainly depends on the complexity and quality of plant models used to develop data reconciliation observers. In practice, the difficulty of obtaining detailed models prevents the application of powerful observers like the Kalman filter. The objective of this study is to propose a methodology to build a model for a flotation circuit to support the implementation of a Kalman filter for dynamic data reconciliation. This modeling approach extracts essential information from the plant topology, nominal operating conditions, and historical data. Simulation results illustrate that applying a Kalman filter based on a rough empirical model that has been correctly tuned gives better estimates than those obtained with sub-model based observers.