Neural networks for steel manufacturing

Schlang, Martin; Poppe, Thomas; Gramchow, O.

doi:10.1109/64.511770

Cited by 21 publications

(6 citation statements)

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“…Numerous statistical problems can be easily solved through the application of artifical neural network (ANN). The function of ANN at modeling and function learning is powerful as well [6]. Although the improvement for traditional modeling approach in ANN is obtained, literature survey shows that a large number of modeling methods in ANN used in high power, non-linear and impact load are associated with transient power quality parameters and load control problems, and applications on power quality problems for these loads in ANN are rarely found.…”

Section: A Structure Of Mapping Function For Bp Network In Load Modementioning

confidence: 97%

Modeling Analysis for High Power Load with Voltage Source Harmonics

Tian

Zhang

et al. 2012

2012 Asia-Pacific Power and Energy Engineering Conference

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This paper presents a method that the voltage/current characteristic of the high power, nonlinear and time varying load is modeled by using artificial neural network, this method is implemented by using back propagation network and look up tables which accurately establish high power load model and receive the consumption of the active, reactive and apparent power and voltage distortions in different harmonic orders. Then the type of the reactive power compensation devices and the capacity of the reactive power compensation equipments can be effectively determined.

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Section: A Structure Of Mapping Function For Bp Network In Load Modementioning

confidence: 97%

Modeling Analysis for High Power Load with Voltage Source Harmonics

Tian

Zhang

et al. 2012

2012 Asia-Pacific Power and Energy Engineering Conference

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“…Most commercial system compensation techniques (P or PI based) only compensate for error under current conditions, therefore, the first of coil a batch frequently present out of specification head-end. 8,9,12 In recent years, research on estimation of process variable in an HSM by adaptive neural networks [13][14][15][16][17] and type 1 8,18,19 fuzzy logic systems has received particular attention worldwide.…”

Section: Hot Strip Millmentioning

confidence: 99%

Finishing mill thread speed set-up and control by interval type 1 non-singleton type 2 fuzzy logic systems

Mendez

Colás

Leduc

et al. 2012

Ironmaking & Steelmaking

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The set-up of the finishing mill (FM) thread speed to achieve the desired strip temperature as measured by the finish mill delivery temperature sensor is made by an intelligent controller based on interval type 2 fuzzy logic system. The controller calculates the FM thread speed required to achieve the strip FM exit target temperature, and the interstand strip surface temperature profiles. The interval type 2 fuzzy set-up controller uses as inputs the predicted transfer bar FM entry temperature, the transfer bar thickness, the target strip FM exit temperature, the target strip FM exit gauge, last stand draft reduction, last stand work roll diameter, last stand rolling force, %C of strip, and target width. Taking into account that the measurements are modelled as type 1 nonsingleton fuzzy numbers, the authors present the so called interval type 1 non-singleton type 2 fuzzy logic system controllers. As reported in the literature, interval type 2 fuzzy logic systems have greater approximation capacity than that of its type 1 counterpart and it has the advantage to develop more robust and reliable solutions than the latter. The experiments of these applications were carried out for three different types of coils, from a real hot strip mill. The results proved the feasibility of the developed system for FM thread speed set-up and control, and for interstand strip temperature profile prediction. Comparisons with the online mathematical based models show that the proposed interval type 2 fuzzy logic system equalises the performance in FM strip thread speed, and temperature set-up and control under the tested conditions characterised by high uncertainty levels.

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“…11 In recent years, research on estimation of process variable in a HSM by adaptive neural networks and T1 fuzzy logic systems has received particular attention worldwide. [12][13][14] Adaptive neural networks and T1 FLS offer the advantages of reliably representing non-linear relations, automatically updating the knowledge they contain and providing fast processing times. 10,15 Strip resistance, and therefore, force and gap set-up, highly depends on the strip temperature.…”

Section: Hot Strip Millmentioning

confidence: 99%

Modelling and control of coiling entry temperature using interval type-2 fuzzy logic systems

Mendez

Leduc-Lezama²,

Colás

et al. 2010

Ironmaking & Steelmaking

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The set-up of the cooling water applied to the strip as it traverses the runout table in order to achieve the coiler entry temperature was made by an intelligent model implemented using interval type-2 fuzzy logic systems. The model uses as inputs the targets for coiling entry temperature, strip thickness, finish mill exit temperature and finishing mill exit speed. The experiments of this application were carried out for three different types of coil in a real hot strip mill. The results proved the feasibility of the system developed for coiler entry temperature prediction. Comparison with the online type-1 fuzzy logic based model shows that the proposed interval type-2 fuzzy logic system improves performance in coiler entry temperature prediction under the tested condition.

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Neural networks for steel manufacturing

Cited by 21 publications

References 0 publications

Modeling Analysis for High Power Load with Voltage Source Harmonics

Modeling Analysis for High Power Load with Voltage Source Harmonics

Finishing mill thread speed set-up and control by interval type 1 non-singleton type 2 fuzzy logic systems

Modelling and control of coiling entry temperature using interval type-2 fuzzy logic systems

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