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Most published Xplore authors for Steel Industry

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Xplore Articles related to Steel Industry

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Online strip cleanliness sensor for steel industries (draft)

2013 IEEE Industry Applications Society Annual Meeting, 2013

Steel cleanliness is very important prior to any steel coating process. A novel online cleanliness sensor for steel flat products has been designed. Sensor is based in the optical reflectivity of the dirty steel, which has been analyzed to determine how the incident light beam is dispersed. Lamination/processing steel marks effect on the light dispersion was quantified to be able ...


Modelling and identification of electrode position controller in electric arc furnace

International Conference on Control 1991. Control '91, 1991

Electrode position controller in electric arc furnace systems plays an important role in the steel-making process by which a reference arc impedance or arc current is maintained to guarantee steady power input to the steel in the furnace. Modelling and identification of the electrode position controller is a prerequisite for realization of its direct digital control. In this paper, detailed ...


Localization of slab identification numbers using deep learning

2016 16th International Conference on Control, Automation and Systems (ICCAS), 2016

In the steel industries, recognizing product information is an important task for the management of the manufacturing processes. For real factory scenes, localization of product identification numbers is conducted prior to recognition to obtain a satisfactory performance. The objective of this paper is localization of slab identification numbers in real factory scenes. Traditionally, most researches in the field of image ...


Fuzzy approach to shape control in cold rolling of steel strip

Electronics Letters, 1994

Cross-sectional shape control in the cold rolling of thin steel strips has been simulated using a fuzzy controller and emulator developed using fuzzy theory and neural networks, respectively based on production data. Irregular cross-sectional shapes of cold-rolled thin steal strips were classified into six types. For each irregular strip shape, fuzzy control rules were developed and applied continuously until the ...


Stabilisation and solidification of steel industry sludge

2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2017

This report presents the research done on the treatment of steel industry sludge by the solidification /stabilisation (S/S) process using desilicated fly ash (DFA) and lime as the binding agents. The sludge was characterized using X-ray fluorescence, X-ray diffraction, and geotechnical techniques. The main oxides in the sludge were iron oxide and zinc oxide. Unconfined compressed strength of 0.49 MPa ...


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Educational Resources on Steel Industry

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IEEE-USA E-Books

  • Online strip cleanliness sensor for steel industries (draft)

    Steel cleanliness is very important prior to any steel coating process. A novel online cleanliness sensor for steel flat products has been designed. Sensor is based in the optical reflectivity of the dirty steel, which has been analyzed to determine how the incident light beam is dispersed. Lamination/processing steel marks effect on the light dispersion was quantified to be able to design the optical circuit. Fresnel lenses were employed to concentrate the dispersed beam again on a photodiode.

  • Modelling and identification of electrode position controller in electric arc furnace

    Electrode position controller in electric arc furnace systems plays an important role in the steel-making process by which a reference arc impedance or arc current is maintained to guarantee steady power input to the steel in the furnace. Modelling and identification of the electrode position controller is a prerequisite for realization of its direct digital control. In this paper, detailed physical analysis of the arc characteristic in electric arc furnace is given, and a model of electric arc furnace electrode controller for one phase is proposed. An identification strategy is then presented to deal with the model's identification problem.<>

  • Localization of slab identification numbers using deep learning

    In the steel industries, recognizing product information is an important task for the management of the manufacturing processes. For real factory scenes, localization of product identification numbers is conducted prior to recognition to obtain a satisfactory performance. The objective of this paper is localization of slab identification numbers in real factory scenes. Traditionally, most researches in the field of image processing and pattern recognition were focused on feature representation or shallow learning. However, conventional rule-based algorithms heavily depend on carefully engineered feature values and require heuristic parameter tuning. To overcome these limitations, a deep learning based algorithm is proposed for the localization with the minimum of manual interventions. This paper contains construction of training data, labeling process, and an architecture of a deep convolutional neural network. The performance error is remarkably reduced to 2.19% by the proposed algorithm compared to 4.59% in the previous work. By using a data-based method, this algorithm is easily expandable to apply for other applications.

  • Fuzzy approach to shape control in cold rolling of steel strip

    Cross-sectional shape control in the cold rolling of thin steel strips has been simulated using a fuzzy controller and emulator developed using fuzzy theory and neural networks, respectively based on production data. Irregular cross-sectional shapes of cold-rolled thin steal strips were classified into six types. For each irregular strip shape, fuzzy control rules were developed and applied continuously until the strip shape converged to the desired flat shape. The simulation results demonstrated that the developed fuzzy controller worked properly.<>

  • Stabilisation and solidification of steel industry sludge

    This report presents the research done on the treatment of steel industry sludge by the solidification /stabilisation (S/S) process using desilicated fly ash (DFA) and lime as the binding agents. The sludge was characterized using X-ray fluorescence, X-ray diffraction, and geotechnical techniques. The main oxides in the sludge were iron oxide and zinc oxide. Unconfined compressed strength of 0.49 MPa was achieved with 40% DFA: lime (70:30) addition, at pH of 12.88. The leachability of Fe was reduced by 94% and Zn by 98.6%. The rate of lime consumption during stabilisation was best described with 2nd order kinetics plot.

  • Expert systems in British Steel

    The most advanced projects in British Steel are in the process control domain with large systems in advanced stages of development. As an example of the approach taken the intelligent Kiln Controller project is described.<>

  • Declarative objects for simulation

    The general aim of the work is to allow a non-expert programmer to develop simulations, in particular for training purposes. For example, simulations of machinery to support fault-finding exercises can be constructed readily with the system described. It is expected that the simulation builder will be primarily interesting in the production of computer-based training material, but would be supported by a domain specialist, familiar with the machine being simulated. Other requirements include the provision of an interactive user interface both for development and debugging, a well a a clean interface to special user interfaces for training purposes. It is, also expected that a partially complete simulation can be tested without problems. With these requirements in mind, an object-oriented programming model was developed, together with supporting tools. This system was evaluated by constructing several simulations; one of these is described briefly. An unusual feature, however, is that the objects have declarative properties, based on a notion of attributes whose values are functions of other attributes. The same language and system was used both for building simulations and for user interface construction, although in this paper only the simulation aspects considered. As part of the evaluation of the modelling system, a simulation of a hydraulically-operated steel press was created.<>

  • Considerations in Protecting the Consumer-Utility Interconnection

    It is essential that good protective reay applications be followed during the initil design of a utiity-consumer Interconnection. This approach will minimize operating problems that may not arise until after the interconnecton b in service. Tfie coesumer-utity guide is intended to establish a comuhon undertanding among those involved in the intertie design. Important Information requird by both the utlity and consumer are outlined. Supply and load condderations are described. Protection theory is discussed. and spedic relay application examples are covered in detail.

  • Research on carbon emission evaluation of China's iron and steel industry

    At present, China is the largest carbon emission country in the world. To adapt to the green new normal, solving the economic and environmental problem become a task of top priority. The paper calculates the carbon emission of China's iron and steel industry over the time period 1992-2012 by the IPCC empirical approach. We found that the carbon emissions increased rapidly during this time period, which had become one of the main source of carbon emission in China. In addition, this paper introduces the concept of urbanization and studies the long-term equilibrium relationship between carbon emissions and variables by optimizing KAYA identical equation. The results indicate that overcapacity affect heavily on the carbon emission of Chinese iron and steel industry. Dissolving the contradiction with overcapacity and ameliorating energy intensity can effectively control carbon emissions.

  • Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0

    To achieve smart manufacturing in Industry 4.0 for steel industry, a smart steel manufacturing framework is considered in this paper, where an automatic identification tracking method for steel products is developed. Existing approaches usually rely on marking or embedding a series of identification codes on the steel surfaces. However, steel-making is usually processed under a very high temperature environment, making it difficult to well embed the identification codes with acceptable quality for further online processing. Therefore, this paper presents a vision-based automatic identification tracking method without needing to embed any identification codes onto the steel product surfaces. The key is to use the essential identity of a steel product without extrinsic information embedded, achieved by extracting visual features from the steel image. Our preliminary results have verified the efficiency of the proposed method.



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