| Bibtype |
Incollection |
| Bibkey |
Stolpe/etal/2016a |
| Author |
Stolpe, Marco and Blom, Hendrik and Morik, Katharina |
| Editor |
Kersting, Kristian and Lässig, Jörg and Morik, Katharina |
| Title |
Sustainable Industrial Processes by Embedded Real-Time Quality Prediction |
| Booktitle |
Computational Sustainability |
| Pages |
201--243 |
| Publisher |
Springer |
| Abstract |
Sustainability of industrial production focuses on minimizing gas house emissions and the consumption of materials and energy. The iron and steel production offers an enormous potential for resource savings through production enhancements. This chapter describes how embedding data analysis (data mining, machine learning) enhances steel production such that resources are saved. The steps of embedded data analysis are comprehensively presented giving an overview of related work. The challenges of (steel) production for data analysis are investigated. A framework for processing data streams is used for real-time processing. We have developed new algorithms that learn from aggregated data and from vertically distributed data. Two real-world case studies are described: the prediction of the Basic Oxygen Furnace endpoint and the quality prediction in a hot rolling mill process. Both case studies are not academic prototypes, but truly real-world applications.
|
| Year |
2016 |
| Projekt |
SFB876-A1, SFB876-B3 |
