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.

Recommendation

BOFdePhos – Control of Dephosphorisation in BOF process

Initial situation: End-point control of the Oxygen Steelmaking Converter (BOF) process is mainly based on results of a static charge […]

Optimal Residual Stress Control – ORSC

Situation: Trend in product development where higher quality standards with tighter material property tolerances are demanded by clients. Quality problems […]

TempKorroSchu

Situation: During re-heating of material for the production of special long products and forged screws 1 to 2 % of […]

LowCarbonFuture – Exploitation of Projects for Low-Carbon Future Steel Industry

Initial situation The iron and steel industry is one of the most energy intensive sectors EU Commissions Low Carbon Roadmap […]