Modeling of Steelmaking Processes
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Sanjula Kammammettu, Zukui Li. Floudas, Hechang Li.
ANN Modeling of a Steelmaking Process
Energy , DOI: Anupama Prashar. Journal of Cleaner Production , , Haijuan Cui, Xiaochuan Luo.
An improved Lagrangian relaxation approach to scheduling steelmaking-continuous casting process. Kun He, Li Wang. A review of energy use and energy-efficient technologies for the iron and steel industry. Renewable and Sustainable Energy Reviews , 70 , A multitasking continuous time formulation for short-term scheduling of operations in multipurpose plants.
Rescheduling optimization of steelmaking-continuous casting process based on the Lagrangian heuristic algorithm. Journal of Industrial and Management Optimization , 13 3 , A hybrid multi-objective evolutionary algorithm based on NSGA-II for practical scheduling with release times in steel plants.
Journal of the Operational Research Society , 67 9 , A subpopulation-based differential evolution algorithm for scheduling with batching decisions in steelmaking-continuous casting production. Mauricio C. Gomes, Allan M. Models for scheduling charges in continuous casting: application to a Brazilian steel plant.
Optimization Letters , 10 4 , Grossmann, Sebastian Engell. Optimization of steel production scheduling with complex time-sensitive electricity cost. Yuriy Lytvynyuk Corresponding Author E-mail address: ylitvinyuk gmail. Read the full text.
Analysis of Melt Flow in Metallurgical Vessels of Steelmaking Processes
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source url Abstract A new model for the simulation of the converter steelmaking process was developed after the detailed study of existing thermodynamic and kinetic models and approaches. Citing Literature.
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Volume 85 , Issue 4 April Pages Related Information. Close Figure Viewer. This paper proposes a dynamic analytics method based on the least squares support vector machine with a hybrid kernel to address real-time prediction problems in the converter steelmaking process.
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The hybrid kernel function is used to enhance the performance of the existing kernels. To improve the model's accuracy, the internal parameters are optimized by a differential evolution algorithm.