Gas Injection in Steelmaking Vessels: Coupling a Fluid Dynamic Analysis with a Genetic Algorithms-Based Pareto-Optimality

Kumar, Amit; Sahoo, Debasis; Chakraborty, Suman; Chakraborti, Nirupam

doi:10.1081/amp-200053434

Cited by 23 publications

(14 citation statements)

References 13 publications

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“…Genetic Algorithms and Turbulence are two interesting topics which have already been coupled in previous works, notably for solving applied optimization problems related to the solidification of thermosolutal flows, as those encountered in metallurgy and manufacturing processes [19][20][21][22][23].…”

Section: Resultsmentioning

confidence: 99%

Shape Optimization Using Genetic Algorithms and Laser Beam Propagation for the Determination of the Diffusion Coefficient in a Hot Turbulent Jet of Air

Nyobe¹,

Pemha²

2008

PIER B

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Abstract-The paper describes the propagation of a thin laser beam which passes through a hot turbulent jet, perpendicularly to the flow direction, using geometrical optics approximation. From the modelling of the random propagation direction of the laser beam along its whole path, the diffusion coefficient of the turbulent jet is determined by means of a shape optimization technique in which a genetic algorithm is used. The results obtained from the GA are then improved by the Golden Section method.

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Section: Resultsmentioning

confidence: 99%

Shape Optimization Using Genetic Algorithms and Laser Beam Propagation for the Determination of the Diffusion Coefficient in a Hot Turbulent Jet of Air

Nyobe¹,

Pemha²

2008

PIER B

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“…In [26], the authors have identified the factors governing the mechanical properties of a trip-aided steel using GA and neural networks (NN). Some other examples of applications of GA can be found in [27,28]. A survey on the application of GA in the field of material science is given in [29].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Electrical Performance of Polymeric Films

Tyagi

Pant

Negi

et al. 2014

Materials and Manufacturing Processes

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In the present study, an attempt is made to optimize the electrical performance of the thin polymeric films through optimization techniques. The study is conducted in two phases: (1) laboratory experiments and (2) through numerical optimization. For laboratory analysis, thin and transparent films are prepared using polyethersulfone (PES) as host material and meta-nitroaniline (MNA) as guest materials. A set of nine film samples are prepared by the solution casting method in the laboratory using different concentrations of MNA. The electrical properties capacitance, conductance, and dissipation factor of films are measured by Aligent Impedance Analyzer. These characteristics are then optimized mathematically. For this purpose, initially single-objectives are considered for optimizing the electrical properties individually, and later a multiobjective model is considered for analyzing the properties simultaneously. The algorithms employed are metaheuristics: genetic algorithms, particle swarm optimization, differential evolution, and its variant modified differential evolution along with fmincon (a MATLAB toolbox) for single-objective optimization and multiobjective differential evolution algorithm and nondominated sorting genetic algorithm-II for multiobjective optimization.

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“…However, for an oscillation designer, how to select the optimum control parameters for the inverse oscillation control model remains unknown. A number of researchers have used genetic algorithms for optimization in some aspects of the continuous casting process, including casting speed optimization [5][6][7], strand temperature optimization [8,9], secondary cooling optimization [10][11][12], and fluid flow optimization [13]. In this work, we focus our attention primarily on mold oscillation optimization.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Mold Inverse Oscillation Control Parameters in Continuous Casting Process

Liu

2015

Materials and Manufacturing Processes

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This paper introduces an approach towards selection of optimum control parameters of mold oscillation in the continuous casting process. A new model was applied to optimize the control parameters of the inverse oscillation control model. Optimization was aimed at reducing the depth of oscillation marks and was performed within constraints imposed by process parameters that influence the quality and productivity of continuous casting. Optimization generated an optimum control parameter set that provided better performance than that offered by Baoshan Iron & Steel Co., Ltd., and was verified by testing results.

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Gas Injection in Steelmaking Vessels: Coupling a Fluid Dynamic Analysis with a Genetic Algorithms-Based Pareto-Optimality

Cited by 23 publications

References 13 publications

Shape Optimization Using Genetic Algorithms and Laser Beam Propagation for the Determination of the Diffusion Coefficient in a Hot Turbulent Jet of Air

Shape Optimization Using Genetic Algorithms and Laser Beam Propagation for the Determination of the Diffusion Coefficient in a Hot Turbulent Jet of Air

Optimization of the Electrical Performance of Polymeric Films

Optimization of Mold Inverse Oscillation Control Parameters in Continuous Casting Process

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