Rejection of unknown periodic load disturbances in continuous steel casting process using learning repetitive control approach

1996 ◽  
Vol 4 (3) ◽  
pp. 259-265 ◽  
Author(s):  
T.J. Manayathara ◽  
Tsu-Chin Tsao ◽  
J. Bentsman
Keyword(s):  
Steel Casting ◽  
Repetitive Control ◽  
Casting Process ◽  
Continuous Steel Casting ◽  
Continuous Steel ◽  
Periodic Load ◽  
Control Approach

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

Metals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 237
Author(s):  
Michal Brezina ◽  
Tomas Mauder ◽  
Lubomir Klimes ◽  
Josef Stetina
Keyword(s):  
Convergence Rate ◽  
Water Flow ◽  
Steel Casting ◽  
Casting Process ◽  
Continuous Steel Casting ◽  
Continuous Steel ◽  
Cooling Zone ◽  
Secondary Cooling ◽  
Flow Rates ◽  
Secondary Cooling Zone

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.

Determination of the Cracking Susceptibility of Steel S355J2G3 during the Continuous Casting Process

2015 ◽  
Vol 220-221 ◽  
pp. 731-736
Author(s):  
Konrad Błażej Laber ◽  
Henryk Dyja
Keyword(s):  
Continuous Casting ◽  
Steel Casting ◽  
Physical Modelling ◽  
Casting Process ◽  
Steel Grade ◽  
Continuous Steel Casting ◽  
Continuous Steel ◽  
Continuous Casting Process ◽  
Process Simulator

The paper presents the results of physical modelling aimed at determining the cracking susceptibility of the selected steel grade under conditions characteristic of the continuous casting process. The material used for investigation was steel grade S355J2G3 [1]. For a study on the physical modelling of the continuous steel casting process, the GLEEBLE 3800 [2, 3], a metallurgical process simulator, was employed. The obtained results allowed establishing conditions for a continuous steel casting process that could cause cracks to form in the material being cast. Research on one of technological conditions for steelworks was carried out taking into account the problem of cracking during rolling in the initial group of the bar rolling mill.

scholarly journals Applying a Numerical Model of the Continuous Steel Casting Process to Control the Length of the Liquid Core in the Strand

2015 ◽  
Vol 60 (1) ◽  
pp. 251-256 ◽  
Author(s):  
K. Miłkowska-Piszczek ◽  
J. Falkus
Keyword(s):  
Numerical Model ◽  
Continuous Casting ◽  
Steel Casting ◽  
Continuous Casting Machine ◽  
Liquid Core ◽  
Casting Process ◽  
Continuous Steel Casting ◽  
Continuous Steel ◽  
Cooling Zone ◽  
Secondary Cooling

Abstract This paper presents development and the application of a numerical model of the continuous steel casting process to optimise the strand solidification area. The design of the numerical model of the steel continuous casting process was presented and which was developed based on the actual dimensions of the slab continuous casting machine in ArcelorMittal Poland Unit in Kraków. The S235 steel grade and the cast strand format of 220×1280 mm were selected for the tests. Three strand casting speeds were analysed: 0.6, 0.8 and 1 m min-1. An algorithm was presented, allowing the calculation of the heat transfer coefficient values for the secondary cooling zone. In order to verify the results of numerical simulations, additional temperature measurements of the strand surface within the secondary cooling chamber were made. The ProCAST software was used to construct the numerical model of continuous casting of steel.

scholarly journals Application of Time-Series Analysis for Predicting Defects in Continuous Steel Casting Process

2016 ◽  
Vol 16 (4) ◽  
pp. 125-130
Author(s):  
A. Rodziewicz ◽  
M. Perzyk
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Steel Casting ◽  
Casting Process ◽  
Continuous Steel Casting ◽  
Continuous Steel ◽  
Industrial Data ◽  
The Real ◽  
Series Analysis ◽  
The Future

Abstract The purpose of this paper was testing suitability of the time-series analysis for quality control of the continuous steel casting process in production conditions. The analysis was carried out on industrial data collected in one of Polish steel plants. The production data concerned defective fractions of billets obtained in the process. The procedure of the industrial data preparation is presented. The computations for the time-series analysis were carried out in two ways, both using the authors’ own software. The first one, applied to the real numbers type of the data has a wide range of capabilities, including not only prediction of the future values but also detection of important periodicity in data. In the second approach the data were assumed in a binary (categorical) form, i.e. the every heat(melt) was labeled as ‘Good’ or ‘Defective’. The naïve Bayesian classifier was used for predicting the successive values. The most interesting results of the analysis include good prediction accuracies obtained by both methodologies, the crucial influence of the last preceding point on the predicted result for the real data time-series analysis as well as obtaining an information about the type of misclassification for binary data. The possibility of prediction of the future values can be used by engineering or operational staff with an expert knowledge to decrease fraction of defective products by taking appropriate action when the forthcoming period is identified as critical.

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