Q-SYM2

Conclusion

The context and trends to automatize the scrap management with an integrated seamless flow of operational data has been presented using the up-to-date software package Q-SYM2 developed by the DIGI&MET Division of the Danieli Automation Company part of the Danieli Group. Q-SYM2 aims at providing the best scrap traceability of the scrap at the scrap facilities to feed EAF Process Control System with accurate information of type and quantity of charged scrap. Therefore, the approach based on large use of AI, Deep and Machine Learning has been applied to provide both the self-learning capabilities and the tailored optimization of operations, considering the scrap characteristics dynamics.

This is a consequence of the inherent Circularity of scrap inside the steel sector, with still a large potential of contributing to the reduction of the environmental footprint.

Based on such premises, the needs coming directly by the steelmakers and the scrap suppliers have been considered as the main requirements for developing a modular and interoperable system, fully compliant with the Industry 4.0 paradigm. In this sense, the strong connection among operational logic, ICT architecture and functionalities have been the driver for the Q-SYM2 implementation.

Credible and provable benefits with its relevant KPIs, both in the systems implementation and operational and monitoring phases, had been analyzed.

From the point of view of the implementation of new features arising from new needs, the CapEx of the system can be a limitation. However, the modularity of the new system leads to the possibility to extend it, with the consequence of a modular CapEx. The automatization and optimization of the decision-making process brings a structural reduction of the OpEx in terms of resources and time needed for the scrap management.

Abstract

A hype topic, one that now has become an established idea, is the possibility to increase plant efficiency by gaining and applying a better awareness of how scrap is performing in the melting process. Scrap management becomes the key point in cost reduction since it could comprise up to the 50% of the overall production costs. Technological innovations promise to be the driver to improving raw material management, shortening its acquisition time and reducing the waste during the metallurgical process. Expensive raw materials require a huge involvement of plant resources, and are highly dependent on the human factor. All the quality and logistics decisions belong to the judgment of the operators, increasing the chance of non-conformities (e.g., erroneous classification, material discharged in the wrong location, error loading material in the buckets). To overcome these issues, online classification of the scrap is the keystone. Starting from the arrival of scrap at the plant, through the acceptance of the delivery note and the check-in of the carriers, Automatic Scrap Classification gives support to inbound-scrap control and classification, enabling real-time traceability of the scrap inside the bays. The Quality Control System will benefit from all the details of the material used in production. Danieli Automation implemented the Q-ASC a system that, leveraging Artificial Intelligence (AI) and deep learning techniques, can assist scrap classification procedures through computer vision and automatic scrap recognition. The goal of scrap identification is to localize and assign a specific class label to a given visual sample of scrap or inert/hazardous material. The classification can be conducted using different methodologies based on material shapes or dimensions. Q-ASC is the entry point for the Scrap Yard Management and can be considered as the central data hub for managing the scrap inbound to the plant, connecting all the systems requiring reliable scrap data.