The CELSA Group is comprised of eight large steel and metal plate rolling companies in various countries with a high degree of instrumentation. The production process is complex, and thousands of variables are registered in separate locations, which makes real-time modelling and analysis a challenge. Information from these industrial plants is dispersed in different control and monitoring systems, so that multivariable analyses cannot be undertaken to detect faults or identify problems easily.
The development of innovative analytical capacity enables the automatization of tasks with little added value, the improvement of production process efficiency, and maintenance (or even improvement) of the quality of manufactured products. In turn, this increases productivity, reduces costs and, in short, enhances competitiveness in the relevant markets.
In relation to these activities, the Motion Control and Industrial Applications Center (MCIA UPC) has developed analytical algorithms that are incorporated in the IIoT MIMETIQ platform of connections, called Data-Driven Steel 4.0, implemented by IThinkUPC. This Industry 4.0 program can gather and centralize information generated in the systems of various production plants. It can implement data-based models for the real-time diagnosis and prognosis of anomalies in production processes, using artificial intelligence algorithms. It can also determine the impact of these anomalies, to facilitate and speed up the decisions of engineers or plant supervisors through interfaces for the visualization of algorithm results that facilitate interpretation. Finally, this system can be used for advanced analytical projects with different objectives and suppliers, as part of a continuous improvement process. Therefore, the new system centralizes, relates and integrates the processes of different plants and information systems.
The development of the project involved the introduction of this shared IIoT platform in four of the group’s production plants (two in Barcelona and two in Santander), complemented for each area of business, by specific multivariable smart monitoring applications that resolve specific problems identified by the groups for the internal improvement of each of these plants.
1. Organization, standardization and centralization of information from the plants’ production processes.
2. An environment for the progressive implementation of industrial analysis processes within a strategy of continuous improvement.
3. Greater knowledge of the behaviour of processes in each plant and their impact on production line processes (smelting, continuous casting, and rolling).
4. Improvement and increased speed of decision-making, due to real-time visualization of the results of analytical algorithms.
IMPACT ON THE COMPANY
Cost cuts due to energy savings and a reduction in scraps, an increase in the quality of manufactured products, and deeper knowledge of how production processes function so that they can be optimized. Mitigation of problems in the production chain (when the final product of one plant is the raw material of another plant in the group).