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This book addresses digitalization and the steel industry. It is written from the perspective of both industrial engineering and data science and discusses digitalization problems, novel production planning and scheduling algorithms in ironmaking and steelmaking, machine learning applications and opportunities in steel plants and quality-related algorithms in steel plants.
Even though digitalization is an important trend in steel industry, current contributions mainly focus on lower automation levels, i.e., field and control levels, that are mainly covered in mechanical, electrical and material engineering domains. On higher planning levels, there are hardly comprehensive scientific contributions on intelligent digitalization issues available. This book fills this gap.
Inhaltsverzeichnis
Introduction.- Planning and Scheduling of Electric Arc Furnace based Steelmaking.- Systematic review of steel surface defect detection methods on the open access datasets of Severstal and the Northeastern University (NEU).- Decision Support Systems for Steel Production Planning - State of the Art and Open Questions.- Volatility and Synchronization in Steel Manufacturing - A Simulation Study of a Modern Steel Mill.- A Comparison of Crossover Operators in Genetic Algorithms for Steel Domain.- Comparative Study of Two Genetic Algorithms for Steel Production Planning under Different Order Backlog Circumstances.- Effect of PolyLoss function on Steel defect detection.- Novel genetic algorithm for simultaneous scheduling of two distinct steel production lines.
