Optimization of continuous casting mould oscillation parameters in steel manufacturing process using genetic algorithms

Bhattacharya, Arya K.; Debjani, S.; Roychowdhury, A.; Das, Jadav Chandra

doi:10.1109/cec.2007.4424992

Cited by 9 publications

(6 citation statements)

References 6 publications

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“…At the basic algorithm level, Differential Evolution (DE) was formulated as an alternate approach to GA by Storn and Price [14]. The present authors have applied both GA and DE in a few complex industrial processes [15][16][17]; the latter work also provides a comparison in computational efficiency for that industrial process between GA and DE demonstrating that DE comes out favourably. Due to these developments the authors decided to use their version of DE as the baseline algorithm for the current multi-objective problem.…”

Section: A Differential Evolutionmentioning

confidence: 92%

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Parallel differential evolution approach for cloud workflow placements under simultaneous optimization of multiple objectives

Balouek-Thomert

Bhattacharya

Caron

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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The recent rapid expansion of Cloud computing facilities triggers an attendant challenge to facility providers and users for methods for optimal placement of workflows on distributed resources, under the oftencontradictory impulses of minimizing makespan, energy consumption, and other metrics. Evolutionary Optimization techniques that from theoretical principles are guaranteed to provide globally optimum solutions, are among the most powerful tools to achieve such optimal placements. Multi-Objective Evolutionary algorithms by design work upon contradictory objectives, gradually evolving across generations towards a converged Pareto front representing optimal decision variables-in this case the mapping of tasks to resources on clusters. However the computation time taken by such algorithms for convergence makes them prohibitive for real time placements because of the adverse impact on makespan. This work describes parallelization, on the same cluster, of a Multi-Objective Differential Evolution method (NSDE-2) for optimization of workflow placement, and the attendant speedups that bring the implicit accuracy of the method into the realm of practical utility. Experimental validation is performed on a real-life testbed using diverse Cloud traces. The solutions under different scheduling policies demonstrate significant reduction in energy consumption with some improvement in makespan.

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Section: A Differential Evolutionmentioning

confidence: 92%

“…The present work generates the mutant vector according to the alternate scheme (proposed in [14] and also used by current authors in [15][16][17] where it is found to work better than other DE variants)…”

Section: A Baseline Differential Evolutionmentioning

confidence: 99%

Parallel differential evolution approach for cloud workflow placements under simultaneous optimization of multiple objectives

Balouek-Thomert

Bhattacharya

Caron

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

Self Cite

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“…Rao et al [27] published a comprehensive review dealing with parameter-based optimization of selected casting processes. The authors pointed out that various optimization techniques such as simulated annealing [28], genetic algorithm [29] and particle swarm optimization [30] applied to the CC process does not always find the optimal solution. To control the casting process, these supervision systems include regulation algorithms such as the PID regulation which exhibit a slower response against a very fast regulation using fuzzy logic [31].…”

Section: Optimization and Regulation Algorithms In CCmentioning

confidence: 99%

Comparison of Optimization-Regulation Algorithms for Secondary Cooling in Continuous Steel Casting

Březina

Mauder

Klimeš

et al. 2021

Metals

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The paper presents the comparison of optimization-regulation algorithms applied to the secondary cooling zone in continuous steel casting where the semi-product withdraws most of its thermal energy. In steel production, requirements towards obtaining defect-free semi-products are increasing day-by-day and the products, which would satisfy requirements of the consumers a few decades ago, are now far below the minimum required quality. To fulfill the quality demands towards minimum occurrence of defects in secondary cooling as possible, some regulation in the casting process is needed. The main concept of this paper is to analyze and compare the most known metaheuristic optimization approaches applied to the continuous steel casting process. Heat transfer and solidification phenomena are solved by using a fast 2.5D slice numerical model. The objective function is set to minimize the surface temperature differences in secondary cooling zones between calculated and targeted surface temperatures by suitable water flow rates through cooling nozzles. Obtained optimization results are discussed and the most suitable algorithm for this type of optimization problem is identified. Temperature deviations and cooling water flow rates in the secondary cooling zone, together with convergence rate and operation times needed to reach the stop criterium for each optimization approach, are analyzed and compared to target casting conditions based on a required temperature distribution of the strand. The paper also contains a brief description of applied heuristic algorithms. Some of the algorithms exhibited faster convergence rate than others, but the optimal solution was reached in every optimization run by only one algorithm.

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“…[1][2][3][4][5][6][7] The oscillation parameters can be divided into three particular parameters: frequency, stroke, and modification ratio. 5) In the steel industry factories, sinusoidal mold oscillation has been used.…”

Section: Introductionmentioning

confidence: 99%

Mold Oscillation Feedforward Control Algorithm for Sinusoidal Oscillation of Various Asymmetries

et al. 2017

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A feedforward control algorithm for the mold oscillators of a continuous casting process is proposed for both sinusoidal and non-sinusoidal oscillations. As steel industries grow, processes allowing for faster continuous casting are required in order to keep up with production demands. To achieve a quicker, continuous casting process, while maintaining slab quality, requires mold oscillations that make use of improved powder consumption rate and reduced friction force. Recently, non-sinusoidal oscillations have been researched owing to their advantages in regards to lubrication and friction. There have been several mechanical approaches but no algorithmic approaches. In this paper, a feedforward control algorithm consisting of embedded input shape and feedforward velocity control algorithms is proposed. Although conventional feedback controls have been commercialized for technical applications, the input shape control algorithm can prevent the non-negligible phase delays or amplitude reduction problems caused by a slow response of systems. In addition, the proposed feedforward velocity control algorithm is derived from the relationship between the valve input and mold velocity, and it allows the mold to correctly follow the non-sinusoidal oscillation. To determine the feasibility of the proposed algorithm, a Matlab Simulink model is derived from the mathematical model. Additionally, 1/100 scale specimen of a typical industry mold oscillator is used. The tests on the specimen showed that the proposed feedforward control algorithm could achieve precise control for both sinusoidal and non-sinusoidal oscillations.

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Optimization of continuous casting mould oscillation parameters in steel manufacturing process using genetic algorithms

Cited by 9 publications

References 6 publications

Parallel differential evolution approach for cloud workflow placements under simultaneous optimization of multiple objectives

Parallel differential evolution approach for cloud workflow placements under simultaneous optimization of multiple objectives

Comparison of Optimization-Regulation Algorithms for Secondary Cooling in Continuous Steel Casting

Mold Oscillation Feedforward Control Algorithm for Sinusoidal Oscillation of Various Asymmetries

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