From controlling single processes to the complex automation of process chains by artificially intelligent control systems: the Control In Steel project. Issue 40 (2022)

• Record Type: Journal Article
• Authors: Neuer, Marcus J.; Loos, Moritz; Marchiori, Francesca; Colla, Valentina; Dettori, Stefano; Ordieres, Joaquin; Wolff, Andreas
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 40(2022)
• Page Start: 295
• Page End: 300
• Publication Date: 2022
• Subjects: Knowledge-based control -- Data-fusion -- Cyber-physical systems -- Adaptive system -- control; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2023.01.088
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: The Control In Steel project, a cooperation of four research institutes, revisited research projects of the last 20 years focusing on automation and control solutions applied to the downstream steel production route. During this investigation we found hints to those solutions, which were beneficial for specific problems. For our analysis, 46 projects were systematically reviewed. Taxonomies for the problem space, the solution space, the barriers and issues and the impact were developed and each project categorized along these taxonometrical dimensions. As a result, the interdependencies between solutions and impact could be analysed in a quantifiable way, which led to a new way of evaluating project success. It also brought new insights about the most promising techniques already applied and those techniques, that have been apparently not yet been applied to steel production, although being highly successful in other domains. This leads to potential future research chances for the steel production and their complex process chains. The paper will also finally demonstrate how a similar taxonometrical approach can be used to conserve expert knowledge in automation to feed a truly artificially intelligent control solution – not only exploiting machine learning methods but essentially using machine reasoning on top of the digitized expert knowledge to achieve improved process automation.