Future Steel Forum
 
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Diego Diaz

Global R&D Senior Specialist at ArcelorMittal

Biography

Diego has been a researcher at the Business & TechnoEconomic Department (KiN) of ArcelorMittal Global R&D since its inception in 2004. This is a Corporate Division that provides service to ArcelorMittal globally, including Corporate teams. It is a multidisciplinary Team who brings Advanced Analytics and Artificial Intelligence to the business side of the company. Within this team and in collaboration with domain specialists in each field, Diego has developed solutions across the value chain of the steel industry: line scheduling, internal and external logistics, yard management, strategy, purchasing, sales, etc. His main focus is on Mathematical Optimization, Metaheuristics, and Machine Learning; and to a lesser degree on Simulation, Algorithmic Game Theory, and other fields of Artificial Intelligence.

Prior to that, Diego was a postgraduate researcher in the Systems Engineering and Automation department at Oviedo University for two years, working on data-driven predictive models of inclusions in steel stemming from the secondary metallurgy and continuous casting processes.

Diego obtained a master’s degree in Industrial Engineering from Oviedo University in 2002, and completed the Artificial Intelligence and Advanced Control postgraduate program, also at Oviedo University, in 2004. He was Visiting Scholar in the Electrical Engineering department of Stanford University for 8 months in 2015, joining the Convex Optimization group led by Prof. Stephen Boyd.

Abstract - Beyond classical methods: Big Data, Machine Learning and Stochastic Simulation for investment decisions in steel operations

Industry 4.0 is today both a necessity and a big opportunity, but it involves the generation, storage, and analysis of huge amounts of data. ArcelorMittal has developed ARTHUR and CEREBRO platforms to face this challenge, tackling Big Data, High Performance Computing, and Machine Learning.

This presentation overviews these platforms, and showcases their application with an investment analysis relying on a combination of Stochastic Simulation and Machine Learning. This allowed the right-sizing of a slab yard to enable the expected production increase under order-book uncertainty.


  

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