Abstract:

Expert systems are a broad range of advanced control algorithms, such as advanced regulatory control, model predictive control, fuzzy logic, neural network or artificial intelligent algorithms, which play an important role in optimizing metallurgical plant performances. In the era of Industry 4.0, we are seeing a rise in the deployment and installation of these systems in metallurgical complexes, since information from big data can be leveraged to enhance their decision-making process. On the other hand, their performances are highly dependant on low-level assets: basic PID control strategies, and instrumentation measurement, signal and data quality.

This article presents an analysis of the various aspects which contribute to the successful implementation of an expert system. The types of advanced control algorithms, their advantages and disadvantages, and their implementation options will be discussed. The low-level process requirements will be established and tips on how to optimize and maintain these assets will be given. The effects of poor signal quality on system performances and how to mitigate their impact on the expert system’s decision-making process will be discussed. Finally, tips on how to track system performances in order to ensure the sustainability of the system through time will be given.

Biography of the presenter: Carole Prévost, Ph.D., has 15 years of experience working as a consultant. She joined BBA in 2016 as team leader, where she offers services in process control & optimization, alarm management and training. Her expertise lies in designing advanced control strategies or expert systems to optimize mineral processing plants.

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Mrs. Carole Prévost

Biography