At a time when digitalization is transforming the global steel industry, Danieli has created a new cross-functional business unit called Digi & Met, which aims to develop and implement new plant design concepts based on digital innovation, as well as new business models. based on the principles of servitization and the resulting economy. In an exclusive interview, Enrico Plazzogna Executive Vice President Sales and Marketing Danieli Automation SpA explains the complex operation of a smart steel mill and shows the revolutionary potential of data analysis and smart manufacturing to optimize capacity and improve the competitiveness of the global steel industry.
What are the characteristics of a smart steel mill?
A smart steel plant operates from a single control panel, where multiple operators control a fully automated production process. Dashboard monitors display all key technical parameters, and reports are generated from a huge amount of data and variables from automation systems and smart sensors, highlighting potential quality or process issues and suggesting corrective action. Images from CCTV cameras are used for visual monitoring of equipment. Machines are designed to prevent manual operations, and robots are used in hazardous areas or where operations are repeated. The cranes are completely unmanned and controlled from one panel. Condition monitoring systems help detect mechanical anomalies and offer preventive maintenance.
How does a smart enterprise optimize production volumes?
Many different positive effects result from smart manufacturing decisions. A fully automated system, including robotics, is more efficient and reduces production time. In addition, optimizing the production schedule with the MES system means increased plant productivity. Full knowledge of process parameters and the use of machine learning systems reduce low-quality production, and knowledge of the condition of tools (benches, buckets, gears, rolls, etc.) avoids unwanted stops. The intelligent enterprise makes the most of data analytics, and metrics are continually monitored to meet expected goals.
How does Danieli's Digi & Met platform illustrate the characteristics of Industry 4.0?
At Danieli, we started working on data analytics and smart manufacturing over 10 years ago, and today we have a dedicated division, Digi & Met, where technologists and automation specialists work together to reap the added value that innovative technologies can leverage. ... There has been a clear change in the business model, more and more focused on end-user partnerships and customer support activities based on the Danieli Group metals know-how. A new approach and an innovative mindset, coupled with a focus on data analytics, have given new ideas to improve the products, machines and automation systems that Danieli can bring to the market, so that our customers are more competitive than other market players.
What are the benefits of installing a Manufacturing Execution System in an Integrated Steel Plant?
We measured KPIs on some of our reference rigs where Q3-MET was installed and sometimes the results exceeded our expectations. Performance improvements of up to 2-3%, depending on installations and configurations, are the result of many positive effects resulting from an efficient and structured production schedule. In particular, machine utilization can be improved up to 10%, stocks in warehouses can be reduced up to 15%. Shorter delivery times are another major benefit of Q3-MET, as well as improved product quality control. One less measurable but very important benefit associated with applying Q3-MET to the entire plant is the unification of the operator interface and platform for manufacturing control.
How does Danieli's Q-MELT improve energy efficiency?
Q-MELT is an excellent example of a machine learning system used in the steel industry. In fact, the melt model, which is part of the Q-MELT system, automatically adjusts the setting during the melting process depending on the deviation from the expected statistical behavior of some key variables based on big data collected during the operation of the plant. The Q-MELT model is a controller that implements a soft landing strategy to best achieve thermal targets (steel temperature and carbon). The controller also integrates an estimate of the current bath temperature and carbon content, which is useful for monitoring
