Data Analytics in Metal Industry - Case studies Datalähtöinen liiketoiminta 17.11.2016 VTT, Espoo Dr. Heli Helaakoski You can use cover page with one or two photos or without photos

Process plants producing huge amount of information Over 100 000 measurement points/ industrial site Monitored parameters thousands per process point Measurements made 24/7

Case SSAB and Outokumpu – Online Quality Monitoring Tool Need for practical tools for quality prediction to support production planning and to decrease manual work Manual quality monitoring require lot’s of resources, causing risk of delays for the corrective actions Material that is not expected to fulfill quality criteria is manually allocated Quality is defined by producer - it can be roughness, stripe model, hardness model, negative profile, profile deviation Benefits in online quality management cost savings in early detection of product/process failure optimal process adjustments support for expert knowledge support for production planning

Case SSAB and Outokumpu – Online Quality Monitoring Tool Model development by University of Oulu, BISG and Indalgo specialized in steel domain

Case SSAB and Outokumpu - Online Quality Monitoring Tool Technical Details Properties Installation Plant server, Azure Operating system Linux, Windows Data sources Oracle, MySQL, Microsoft SQL server Configurable for data, process layout, models, user interface, settings Web-based visualisation Overview Quality information for process phases Raw data Process measurement data Model output Visual information generated by models Models Supported models R, rules Other possible models Python, C++ SSAB Profile deviation, negative profile, input explanation plot (University of Oulu), Chebyshev polynomials for transversal profile Outokumpu Roughness, input explanation plot (University of Oulu), hardness (Outokumpu), stripe (Indalgo) General Scatter plot, histogram, FDA, SPC Xbar, CUSUM, cluster analysis, PCA, Sammon plot Contact Heli Helaakoski, VTT Esa Puukko, Outokumpu Jarkko Vimpari, SSAB

Case SSAB - Quality monitoring for liquid steel making process Benefits Detection of process deviation from normal state Quick analysis how a melting succeeded Give overall understanding of whole liquid steel making quality Tool to improve steel cleanness for strengthened customer demands eg. automotive industry Help and support product development and operators in their work process quality deviations Fasten development work Monitoring tool can be used as learning tool Contact Heli Helaakoski, VTT Agne Bogdanoff, SSAB

Case Outotec – Recognizing the Status of Electric Furnace Off-Gas Line Need to monitor the status of the electric furnace off-gas line by recognizing the changes in gas flow dynamics beforehand, dust accumulation in line can be identified 8 hours before it becomes critical Method comprehensive data analysis - data pre-processing, data classification, statistical analysis and correlation analysis Contact Kai Anttila, Outotec Jere Backman, VTT

Conclusion Case studies can be adopted into other sectors as well Data analytics has a lot of possibities in conventional industries Predicting quality, risks Identifying operational risks in equipment Supporting operative staff Optimising logistics, sheduling , use of energy and raw materials How data analytics and process engineers find the common understanding, speak the same language, fill the gap We need interpreters, domain specialists, define the way of working

Ask us more! Dr. Heli Helaakoski, VTT Principal Scientist Lifecycle solutions Tel. +358 40 510 8619 Heli.Helaakoski@vtt.fi

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