Optimal control system for hot strip finishing mill

Okada, Masaki; Iwasaki, Yukiko; Murayama, K.; Urano, A.; Kawano, Akira; Shiomi, Hiroshi

doi:10.1109/cdc.1996.572664

Cited by 18 publications

(5 citation statements)

References 3 publications

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“…References [7] and [12] reported the use of PCA on steel mill coil failure analysis. These studies applied PCA to the signal PSDs to differentiate between normal and abnormal coils.…”

Section: Pca and Spementioning

confidence: 99%

“…Hearns and Grimble [6] applied multivariable H ' control synthesis on a hot strip finishing mill to account for the various coupling effects and to obtain improved milling performance. Okada et al [7] used a perturbation-type system model to apply decoupling control on the finishing mill. Similar to reference [6], the decoupling effect helps improve the performance.…”

Section: Introductionmentioning

confidence: 99%

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Downcoiler surface fault prediction for a hot strip steel mill

Chuang

Chen

Yen

et al. 2008

Proceedings of the Institution of Mechanical Engineers, Part I:

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This paper studies the means of detecting steel strip surface defects produced by a hot strip mill downcoiler. The analysis is based on the force feedback signals taken from the side-guide and the pinch-roll of the steel mill. The study compares the results from two approaches. One approach is to apply principal component analysis (PCA) on the autoregressive (AR) model spectrum, and the other is to apply PCA on the signal periodogram. The spectrum estimation processes help eliminate the noise effects, and the PCA then distinguishes the signals into different clusters. Some of these clusters will then represent the problematic strips. The analysis results on the data from China Steel Corporation (CSC) show that PCA in conjunction with the AR model is able to separate a major portion of the defective processes, and that PCA with the periodogram is not as effective. However, the two processes sometimes compensate for each other, and the combination of the methods may provide improved results.

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“…References [7] and [12] reported the use of PCA on steel mill coil failure analysis. These studies applied PCA to the signal PSDs to differentiate between normal and abnormal coils.…”

Section: Pca and Spementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Downcoiler surface fault prediction for a hot strip steel mill

Chuang

Chen

Yen

et al. 2008

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…To develop a simulation platform which can simulate mill dynamic has never been an easy task. [8][9][10] In 2003, Freyer built the linear models of strip thickness and tension systems of a Steckel hot rolling mill process and developed a diagonal H N controller based on the built model. Five years later, 11 Sansel studied the dynamic process of the looper-strip system and introduced the variance control technology into the looper-strip system.…”

Section: Introductionmentioning

confidence: 99%

Dynamic modeling and rolling data analysis of the tandem hot rolling process

Yin

Zhang

2016

SIMULATION

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Hot rolling is an essential industrial process in the production of sheet steel, a widely used product in manufacturing and construction. The hot strip mill is a complex unit including many processes and control loops and its modeling is a challenging task owing to the variety of phenomena that occur within the mill. Model validation is also challenging owing to a scarcity of measurements. In this study, a computer simulative emulation that can analyze the dynamic behaviors of the tandem hot rolling process with original non-linear rolling models has been developed and validated against a linear model and measured data. Moreover, the influence coefficients of rolling process parameters were calculated by analyzing the rolling data.

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“…The automatic gauge control (AGC) system, maintaining steady and designated delivery thickness of the strip, plays an important role in determining quality of the steel strip in hot strip mills [3,4,5]. The hot strip mill is a large-scale and highly-complicated manufacturing system, which involves high temperature, high speed, and high rolling force that make the process control even more difficult.…”

Section: Introductionmentioning

confidence: 99%

Simulation and Analysis of Feedforward Automatic Gauge Control for a Hot Strip Finishing Mill

Cheng

Hsu

Hsu³

et al. 2011

AMR

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In order to fully understand dynamic behavior of the thickness variation and conduct off-line process tuning, a dynamic simulator for the feedforward Automatic Gauge Control (AGC) for a hot strip finishing mill in China Steel Corporation is developed. One of the major factors influencing on deviation of delivery thickness of the steel strip is the temperature variation caused by cyclic contact between the steel strip and beams, which transfer the strip in the hot rolling process. This temperature difference brings about hardness variation along the strip, which is known as skid marks. Simulation results and system analysis verify that the feedforward AGC system is capable of overcoming the high frequency thickness variation disturbed by the cyclic temperature difference, which usually cannot be effectively compensated by regular gaugemeter AGC feedback loop. A strategy to locate the optimal feedforward gain is also proposed.

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Optimal control system for hot strip finishing mill

Cited by 18 publications

References 3 publications

Downcoiler surface fault prediction for a hot strip steel mill

Downcoiler surface fault prediction for a hot strip steel mill

Dynamic modeling and rolling data analysis of the tandem hot rolling process

Simulation and Analysis of Feedforward Automatic Gauge Control for a Hot Strip Finishing Mill

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