1. Data Analytics in Metal Industry - Case studies
Datalähtöinen liiketoiminta VTT, Espoo
Dr. Heli Helaakoski
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2. Process plants producing huge amount of information
Over measurement points/ industrial site
Monitored parameters thousands per process point
Measurements made 24/7

3. 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

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

6. 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

7. 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

8. 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

9. 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

10. Ask us more! Dr. Heli Helaakoski, VTT Principal Scientist
Lifecycle solutions
Tel

11. TECHNOLOGY FOR BUSINESS
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