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Anomaly Detection Approaches Applied to Process

Authors: 

The enrichment and sintering plant of the Industrial Complex of Moanda (CIM) enables the valorization of Mn-rich fines generated during the production of MMA and MMD ores.

Thanks to the analysis of historical data of the sintering plant, a data-driven Digital Twin of the process was developed. The goal behind the development of this Digital Twin is to precisely mimic the behavior of the industrial process and enable the identification of optimal setpoints in the sintering process.

Certain key parameters of the process, such as the permeability of the load and its temperature during sintering, are nevertheless difficult to measure online and therefore missing in the digital twin. To gain information on these parameters, a First-Principle model of the sintering strand was developed. Such a physics-driven model enriches the Digital Twin to deduce the behavior of the process beyond its conventional operating ranges. Based on kinetics of coke combustion, thermodynamics of the sintering reaction, and aeraulics of the gas flow within the load, the advance of the flame front and the temperature profile in the load were simulated. When run in an inverse manner coupled to an optimization algorithm, the model enabled the calculation of the load’s permeability. Thereafter, the permeability values were used to enrich the Digital Twin.

The Digital Twin and the physical model have thus been interconnected. This provides a powerful tool for optimizing CIM performance. In the future, this digital tool developed at Eramet Ideas will be deployed in the plant. On-site missions will allow us to receive user feedback and work directly on improving the user interface.