2024 Franz Edelman Award finalist: Tata Steel Limited

The Franz Edelman Award is awarded each year to an organization that has made a transformative contribution to society, all with the power of advanced analytics. In the leadup to the 2024 INFORMS Analytics Conference in sunny Orlando this April, we’ll be highlighting the incredible contributions of this year’s finalists. The winner will be announced at the conference, during the Edelman Gala. To kick off this very special series, which just so happens to feature my favorite episodes throughout the entire year, I have the pleasure of speaking with the team from Tata Steel Limited to share some special insight into their finalist project.

As the practice is in automotive steel ecosystem, the demonstrated capability of the steel manufacturer to ensure quality of the product steel in a consistent manner with use of best in technology and practices is what helps to win over customers. It goes without saying that the same process prevails in other premium segments like whitegoods and advanced engineering applications. From that perspective, this solution is making us not only stronger in current business environment but also making future ready.

Interviewed this episode:

Jose Martin Korath, Sujit Anandrao Jagnade and Sachin C. Patwardhan

Tata Steel Limited and Indian Institute of Technology Bombay

Sujit Anandrao Jagnade is a Senior Technologist in the mathematical modeling and intelligent system group of the Automation division at Tata Steel Limited, India. With over 7 years of experience, he specializes in developing process optimization solutions through applied research. He obtained his master’s degree (M. Tech) in Chemical Engineering from the Indian Institute of Technology (IIT) Kanpur in 2017. Before pursuing his master’s degree, he worked for 1 year at Finolex Industries Limited in Ratnagiri, India, as a process control engineer. His work focuses on process modeling using physics-based and data-driven modeling techniques such as AI/ML and time series methods. Additionally, he has extensive experience in developing interfaces, integrating, and implementing optimization and advanced process control solutions in real-time metallurgical, chemical, and petrochemical processes.

Jose Martin Korath is Chief (Intelligent Systems and Mathematical Modelling) at Automation Division, Tata Steel, Jamshedpur, India. Prior to joining Tata Steel, he worked with Research & Development Center for Iron and Steel, Steel Authority of India. He received his PhD in Process System Engineering from the University of Sydney, Australia in 2009. He primarily works on development of various analytics solutions for manufacturing process using classical modelling techniques as well as advanced AI/ML algorithms. He has more than twenty-five years of experience in design, development and implementation of Supervisory control solutions for various metallurgical processes in integrated steel plants. His research interests include Process Modelling, Optimization, Image Processing and AI/ML. He maintains strong research collaborations with academia and other Research & Technology Organizations.

Sachin C. Patwardhan is a professor of Chemical Engineering and an associate faculty of Systems and Control Engineering at the Indian Institute of Technology Bombay. Before moving to IIT Bombay in 2002, he served as a faculty member at IIT Madras. He received his Ph.D. in Systems and Control Engineering from IIT Bombay in 1994.  His research interests are in application of AI/ML techniques for developing data driven dynamic models, adaptive and model predictive control, online fault diagnosis and real time economic optimization. He has supervised twenty-five doctoral students and worked on multiple industrial projects in these areas. He has held a visiting faculty position at the University of Alberta, Canada in 2001 and was a visiting research scholar at Carnegie Mellon University, USA, during 2008-09. He is an associate editor for the Journal of Process Control and a fellow of the Indian National Institute of Engineering.


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