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Journal ArticleDOI

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

01 Feb 2021-Vol. 11, Iss: 2, pp 237
TL;DR: The main concept of this paper is to analyze and compare the most known metaheuristic optimization approaches applied to the continuous steel casting process and find the optimal solution was reached in every optimization run by only one algorithm.
Abstract: 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 25D 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
Citations
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Proceedings ArticleDOI
28 May 2021
TL;DR: In this article, an improved artificial bee colony algorithm was proposed to improve the quality and efficiency of the slab by adding the learning factor to the optimal individual learning factor and random individual of the current population and integrating the output overshoot mechanism into the objective function.
Abstract: Dynamic control of secondary cooling in continuous casting is the key technology to improve the quality and efficiency of the slab. In order to improve the quality and efficiency of the slab, a new control strategy of secondary cooling water distribution based on an improved artificial bee colony algorithm is proposed. Based on the traditional artificial bee colony algorithm, adding the learning factor to the optimal individual learning factor and random individual of the current population and integrating the output overshoot mechanism into the objective function is effective to avoid the great overshoot phenomenon caused by the abruptly change of casting speed and the large fluctuation of casting surface temperature. The transfer function was constructed based on the coupling relationship between the distribution of secondary cooling water and slab surface temperature. The Simulink simulation model was established, which was compared with the PSO-DSWPI Method and conventional PID controller. The results showed that the improved artificial bee colony algorithm has a more minor error, higher control accuracy and more smooth step response, which provided a new method for realizing more accurate control of secondary cooling dynamic water distribution and had practical significance for improving slab quality.

1 citations

Journal ArticleDOI
TL;DR: In this paper , the authors show the potential of the use of the original solidification model BrDSM in the case of steel production and show that significant cost savings in the production can be achieved by the minimization of the number of rejected slabs and billets, which need to be scraped, as well as by minimizing the cooling water consumption.
Abstract: Digitalization of the real manufacturing process according to Industry 4.0 becomes necessary for industrial producers. Digitalization increases their competitiveness on the market through optimization of processes in the production chain. The rise of prices of raw materials, water shortage, and the effort of the society to reduce the CO2 and greenhouse gas emissions must be compensated by lower production costs. Metallurgical processes and steel production are this case. In the last year, the price of iron ore and scrap metal has risen by more than a third, which is directly proportional to the steel price. The consequence is the increase in the construction sector, car production, white goods, etc. There is a strong assumption that in the next years, steel prices will remain relatively high. Significant cost savings in the production can be achieved by the minimization of the number of rejected slabs and billets, which need to be scraped as well as by the minimization of the cooling water consumption. This paper shows the potential of the use of the original solidification model BrDSM. This long-term validated solidification model represents a digital copy of the real continuous casting process. In the future modern steelmaking process, these models will be irreplaceable.
Journal ArticleDOI
TL;DR: In this article , a real-time prediction (ReP) model was developed to predict the 3D temperature field distribution in continuous casting on millisecond timescale, with mean absolute error (MAE) of 4.19 K and mean absolute percent error (MAPE) 0.49% on test data.
Abstract: Abstract Digitalisation of metallurgical manufacturing, especially technological continuous casting using numerical models of heat and mass transfer and subsequent solidification has been developed to achieve high manufacturing efficiency with minimum defects and hence low scrappage. It is still challenging to perform adaptive closed-loop process adjustment using high-fidelity computation in real-time. To address this challenge, surrogate models are a good option to replace the high-fidelity model, with acceptable accuracy and less computational time and cost. Based on deep learning technology, here we developed a real-time prediction (ReP) model to predict the three-dimensional (3D) temperature field distribution in continuous casting on millisecond timescale, with mean absolute error (MAE) of 4.19 K and mean absolute percent error (MAPE) of 0.49% on test data. Moreover, by combining the ReP model with machine learning technology—Bayesian optimisation, we realised the rapid decision-making intelligent adaptation of the operating parameters for continuous casting with high predictive capability. This innovative and reliable method has a great potential in the intelligent control of the metallurgical manufacturing process.
Journal ArticleDOI
TL;DR: In this article , an evaluation of ten metaheuristic optimization algorithms applied on the inverse optimization of the Interfacial Heat Transfer Coefficient (IHTC) coupled on the solidification phenomenon was proposed.
Abstract: In this paper is proposed an evaluation of ten metaheuristic optimization algorithms applied on the inverse optimization of the Interfacial Heat Transfer Coefficient (IHTC) coupled on the solidification phenomenon. It was considered an upward directional solidification system for Al-7wt.% Si alloy and, for IHTC model, a exponential time function. All thermophysical properties of the alloy were considered constant. Scheil Rule was used as segregation model ahead phase-transformation interface. Optimization results from Markov Chain Monte Carlo method (MCMC) were considered as reference. Based on average, quantiles 95% and 5%, kurtosis, average iterations and absolute errors of the metaheuristic methods, in relation to MCMC results, the Flower Pollination Algorithm (FPA) and Moth-Flame Optimization (MFO) presented the most appropriate results, outperforming the other methods in this particular phenomenon, based on these metrics. The regions with the most probable values for parameters in IHTC time function were also determined.
References
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Journal ArticleDOI
13 May 1983-Science
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.

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TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
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"Comparison of Optimization-Regulati..." refers methods in this paper

  • ...Particle Swarm Optimization (PSO) The PSO was developed by Kennedy and Eberhart in 1995 [44] and became one of the most widely used swarm-intelligence based algorithms due to its simplicity and flexibility....

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Journal ArticleDOI
Rainer Storn1, Kenneth Price
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Abstract: A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.

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Book
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TL;DR: This paper introduced the physical effects underlying heat and mass transfer phenomena and developed methodologies for solving a variety of real-world problems, such as energy minimization, mass transfer, and energy maximization.
Abstract: This undergraduate-level engineering text introduces the physical effects underlying heat and mass transfer phenomena and develops methodologies for solving a variety of real-world problems.

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"Comparison of Optimization-Regulati..." refers methods in this paper

  • ...Slice Solidification Model The mathematical formulation of heat transfer and solidification to the temperature distribution and solid shell profile prediction is based on the governing equation of transient heat conduction, called the Fourier-Kirchhoff equation [32]....

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Journal ArticleDOI
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.

7,090 citations