Application of Big Data technologies in downstream steel process

In steel manufacturing, the rolling step defines the major properties of final products of the steel industry which are delivered to a wide range of different industrial sectors. Modern rolling mills, to reach high degree in process supervision and efficiency, installed sensor equipment that delivers masses of data and information about the process, the product and its quality at high sample rate and at high spatial resolution. But there is a misalignment between the applied high-tech equipment and the online exploitation of such measurements to describe their impact into the product properties. To make use of the high amount of online available data, on 2018 started the RFCS project NewTech4Steel aimed to enhance process stability and product quality in steel production by exploitation of break-through technologies for real-time monitoring, control and forecasting inspired by Big Data concepts. Within this project, on the basis of the strict correlation between the cold rolled strip flatness and downstream process productivity (HDG & painting line), it has been investigated at Marcegaglia Ravenna plant the case-study of managing the massive amount of production data and making them operational by leveraging machine learning algorithm in order to optimize the global productivity. The project allows fast and online data processing and in turn fulfills the plant line needs for: prediction of defect related to manifested coil flatness imperfections and real-time prediction of process parameters during the zinc coating to avoid break/sideslip on critical conditions. The solution created in New Tech4Steel is inspired by Lambda Architecture as the unified platform for data and analytics which is composed of three layers: batch processing layer for offline data, serving layer for preparing indexes and views and speed layer for realtime processing. The work compiled the necessary measures to integrate the new technologies into the existing IT systems especially under the aspects of brown-field implementation, the connection of newly developed systems to existing ones, and the requirements for HMIs for user interaction and visualization. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)