If the Digital Revolution of the second half of the twentieth century brought a shift from analogue and mechanical technology to digital technology, Industry 4.0 describes the linking of production and automation technology with ICT. Originating from a project in the high-tech strategy of the German government, which promotes the computerisation of manufacturing, the term refers to the interaction of the real and virtual worlds.
As explained by Petra Hartmann-Bresgen in a recent Stainless Steel World magazine article, Industry 4.0 has manifested itself particularly in the increased automation of various industrial processes. Working only under the development of intelligent, autonomous monitoring and decision-making processes, so that the relevant routines can be controlled and optimised in real time, its implementation in practice requires large volumes of data which should be carefully analysed and processed.
One example of successful implementation comes from Germany. SFB 876, a unit within the IT Department of TU Dortmund University, is working on the development of data stream algorithms which allow an analysis of incoming data streams in real time. Their theoretical foundation has been implemented in practice together with SMS Siemag AG and a working group of Dillinger Hütte under a real-time forecasting project at a steel mill. This innovative system is able to learn and therefore to fine-tune a production process based on the data which it receives from the manufacturing process, so that it can then improve the industrial process.
The products of Dillinger Hüttenwerke, a major European heavy plate manufacturer, are used, among other things, for the production of large-diameter pipes. The central furnace at the smelting plant in Dillingen is a BOF converter (Basic Oxygen Furnace) where pig iron and scrap steel are fed in and where slag-forming agents are then added. Using a blower lance, oxygen is subsequently blown into the molten mass at supersonic speed, burning up any undesirable elements and ensuring their disposal in the form of slag and waste gas. The purpose of the BOF process is to obtain melted steel with certain defined properties at the end of the oxygen blowing process. The target variables are the tapping temperature, the carbon content and the phosphorus content of the molten mass as well as the iron content of the slag.
The data-driven forecasting model for the BOF converter was developed with the aim of improving predictability of the four target variables at the blowing end point. To record the process data, a computer was integrated into process automation with the capability of detecting 90 static process variables. To increase predictive accuracy even further, 36 dynamic process variables were collected. In all, the data-driven forecasting model can deal with 126 process variables.
Besides its ability to learn independently and make real-time predictions, the newly developed forecasting model can also control the blowing process by identifying suggested corrections. Compared to the forecast target values of a conventional metallurgical model, the data-driven model is not only far more accurate in predicting the temperature at the blowing endpoint, but can also predict all other target variables.
The economic benefits of the model are manifold. Whereas steel production is increased through the reduction of the after-blowing and over-blowing rates, process costs and the costs of input materials are reduced. Also, the fire-proof lining of the converter is less subject to wear and tear, the converter produces more steel, and the company has lower personnel expenses. In the case of the BOF converter in Dillingen, potential annual savings are estimated at around EUR 500,000.
Since data-driven forecasting models are very flexible, they can easily be transferred to other applications, with relatively few adjustments. In this respect Industry 4.0 has every potential to revolutionise the stainless steel industry. Whether this will actually happen, however, remains to be seen.
To learn more about Industry 4.0, read the full article by Petra Hartmann-Bresgen.