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  • Victor Souza

Digital twins in mining and steel

How process data management can improve accuracy and reliability

The digital twin is a virtual representation of a physical asset, which can be used to monitor, analyze and optimize its performance. In mining and steelmaking, digital twins have the potential to improve efficiency, safety and productivity. This is a disruption in the role of engineering that is already underway in players in these segments who are attentive to the digital transformation of their operations.

One of the main challenges for implementing digital twins is process data management. This data is collected from various sources such as sensors, control systems and documentation. The fragmentation of these sources makes effective integration difficult, leading to errors, delays and a lack of credibility in the information.

Data security is also a concern, especially in sensitive projects. Data analysis is crucial, but it depends on quality data and adequate systems. Emerging technology solutions like AI and cloud are helping, but standardization is essential for efficient data management in engineering.

Process data management is essential to ensure the accuracy and reliability of digital twins. An effective approach involves standardizing data, integrating data sources, and implementing security measures.

BLOSSOM CONSULT, as a multidisciplinary industrial engineering company, uses a process data management system to develop digital twins for industrial assets of its mining and steel customers. The system standardizes data, integrates data sources and implements security measures.


In BLOSSOM CONSULT engineering processes, this data is created and managed through one of the EDA® (Engineering Data) modules, a powerful system for managing materials, quantitative and process data.

The interface built through permissions and hierarchical structures ensures that data is created and reviewed only by those responsible for the data at that stage of the process, guaranteeing integrity and traceability of information.

This data is then passed on to BIM models through integration with the main solution providers on the market (as demonstrated in the video below), this way everyone involved in the project's life cycle accesses a single piece of information.

Much of the data is generated during the engineering phases: conceptual, basic and detailed. This data will cover the entire life cycle of industrial assets through the BIM model, which needs to be a faithful representation of the real asset (hence, a digital “twin”, in the metaverse) integrating with other systems or processes, such as plant sensing. and its automation to increase performance and optimize maintenance routines for industrial assets.

Therefore, it is essential that this data is structured in a single source architecture, guaranteeing data integrity for data generation (big data) for subsequent stages, using machine learning and analytics for data-based decision making (data driven decision making).

Process data management is an essential component for implementing digital twins in mining and steelmaking. By adopting an effective approach, companies can improve the accuracy and reliability of digital twins, which can lead to significant benefits in terms of efficiency, security and productivity.


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