A dynamic optimization framework for basic oxygen furnace operation. (21st September 2021)

• Record Type: Journal Article
• Authors: Dering, Daniela; Swartz, Christopher L.E.; Dogan, Neslihan
• Is Part Of: Chemical engineering science. Volume 241(2021)
• Publication Date: 2021-09-21
• Subjects: Basic oxygen furnace -- Dynamic optimization -- First-principles model; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2021.116653
• Languages: English
• ISSNs: 0009-2509

Abstract

Highlights: Dynamic optimization framework with fundamental modeling for a steelmaking process. Determines economically optimal initial states and control profiles. Collocation approximation applied to scrap melting PDE. A hybrid approach proposed for solving ensuing dynamic optimization problem with steep fronts. Increasing scrap usage shown to decrease steel production cost and carbon dioxide emissions. Abstract: About 70% of the steel used worldwide is produced via the Basic Oxygen Furnace (BOF). This process has limited automation, and normal operation relies on invaluable operators' knowledge and past experience. In this paper, we present a framework for dynamic optimization of BOF operation. The dynamic optimization problems utilize a first-principles based dynamic model, and are solved using a hybrid method in which the states are integrated using a differential–algebraic equation (DAE) solver over an initial time interval and full discretization over the remainder of the time horizon. Optimization case studies are presented in which the impact of constraints and different objective formulations are explored. The results suggest that the proposed framework can potentially aid steelmakers to significantly reduce process costs while meeting production and quality targets. The framework is implemented in Python using the open-source software tool, CasADi.