Optimizing integrated steelworks process off-gas distribution through Economic Hybrid Model Predictive Control and Echo State Networks. Issue 40 (2022)

• Record Type: Journal Article
• Authors: Dettori, S.; Matino, I.; Colla, V.; Wolff, A.; Neuer, M.; Baric, V.; Schroeder, D.; Utkin, V.; Schaub, F.
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 40(2022)
• Page Start: 301
• Page End: 306
• Publication Date: 2022
• Subjects: Economic Hybrid Model Predictive Control -- Artificial Intelligence -- Machine Learning -- Process off-gas distribution -- Integrated Steelworks -- Reservoir computing; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2023.01.089
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Steel production in integrated steelworks involves the simultaneous production of various byproducts, including process off-gases that are usually exploited for generating electricity in the internal power plant, heat and steam. Their discontinuous production is managed through complex network, gasholders and torches, which must be managed with stringent operational constraints. In this paper we present a supervision and control system designed to optimize the economic management of the distribution of process off-gases that also allows minimizing the environmental impact. The system implements a digital twin based mainly on machine learning techniques, including Echo State Networks, and a hierarchical optimization system, which first level is based on an economic model predictive approach and the second level is based on the economic hybrid model predictive control. This system allows to effectively maximize the use of off-gases while minimizing the environmental impact of their use up to 97%.