Presed – Predictive Sensor Data mining

Initial situation:

  • Some steelworks experiences problems with product deficiencies like slivers and cracks.
  • Several pre-studies indicated a relationship between the time dependence of a set of measured values and the occurrence frequency of the defect.
  • Methods for predicting, detecting and reducing the defect early in the process are desired.

Working topics:

  • Highly resolved time-series process data is analysed with respect to the root causes for these defects.
  • Influential key factors are derived from the time-series data to quantify the probability of the defects.
  • An event-detection for time-series data will be developed.
  • The know-how about the interdependencies is conserved in an ontological knowledge base.

Results:

  • A prediction system for the defect probability is developed to assist the plant personnel.
  • Novel results on defect appearance and root causes are expected.
  • Reduction of slivers and cracks by operating the processes at optimum working points.

Siehe auch

PreventSecDust – Verminderung von Staubemissionen im Hochofenwerk

Ausgangssituation Die Möllervorbereitung gehört durch den vielfältigen Materialtransport zu den größten Staubquellen im Hochofenwerk. Wichtige Kenntnisse zur Entstehung von sekundären […]

INCERV – Keramische Heißgasventilatoren

Ausgangssituation: Einsatz metallischer Ventilatoren aufgrund hoher Temperaturwerte in Industrieöfen nur begrenzt möglich. Aufgrund der Kriechverformung muss die Drehzahl metallischer Ventilatoren […]

PlantTemp – Plant wide control of steel bath temperature

Initial Situation The aim of steel bath temperature control is to prepare the melt such that it meets the target […]

TopTemp – Schachtofenanalyse mit akustischer Gastemperaturmessung

Ausgangssituation: Schachtöfen wie Hochöfen zur Roheisenerzeugung sind hoch-effektive Aggregate, aber sehr komplexe physikalisch-chemische Wechselwirkungen machen ihren Betrieb sehr anspruchsvoll. B+D […]