Transient Thermal Modeling and Particle Swarm Optimization to stablish industrial annealing parameters for Grain-Oriented Electrical Steels

Resumo

Grain-oriented electrical steels (GOES) are applied in the manufacture of static electrical equipment cores that require high efficiency and minimal energy loss, where the magnetic flux direction is known and consistent. A key stage in the industrial production of GOES is the decarburization and heat treatment stage in a continuous annealing furnace, which plays a crucial role in defining its microstructure, texture, and magnetic properties. These industrial-scale heat treatment conditions are previously developed and investigated in lab-scale studies, and the challenge is converting those lab-scale recipes to industrial furnace operational parameters to provide the material with the same heat treatment. The present work deals with the problem of establishing the industrial furnace temperature set-ups and steel strip velocity that allows the steel strip to undergo the same heat treatment previously defined in lab-scale studies. A Finite Difference model is implemented to calculate the heat transfer between the furnace walls and the steel strip in both lab-scaled and industrial annealing processes. Finally, the Particle Swarm Optimization (PSO) method is implemented to search for the optimum industrial furnace heating zones temperatures by solving these heat transfer models and minimizing the sum of the squared errors (SSE) between the industrial strip steel thermal profile and the optimum thermal profile previously defined in the laboratory development. The cases presented in this work indicate that the suggested approach can indeed be a successful alternative to find suitable industrial setups capable of scaling up recipes, accelerating the process of industrialising new GOES grades.