Morsut L.,Danieli CentroMet |
Rinaldi M.,Danieli CentroMet |
Urbano A.,Danieli CentroMet |
Lena M.,Danieli Automation
MPT Metallurgical Plant and Technology International | Year: 2014
The article deals with the 'rail mill,' the second site in Novokuznetsk, which is one of the largest manufacturers of a complete range of rails for the Russian and international markets. Railway, tramway and underground rails are manufactured here. The site comprises an EAF melt shop with two continuous casters, a rail and beam shop, and a long products mill. Production Evraz Consolidated West-Siberian Metallurgical Plant (Evraz ZSMK) is the easternmost steel producer in Russia and the largest in the Siberian region. In 2012, consolidated Evraz ZSMK produced 7.3 million t of crude steel and 6.3 million t of rolled products. In terms of production levels, it ranks among the five biggest steel companies in Russia and the top-five global rail manufacturers. In order to improve MSR efficiency, a new release of the liquid pool control (LpC) system was developed to calculate solidification and fine-tuned on the plant based on an analysis of the position of deliberately generated internal crack.
Rocchetti F.,Danieli Morgardshammar |
Taurino A.,Danieli Morgardshammar |
Mestroni A.,Danieli Automation
SEAISI Quarterly (South East Asia Iron and Steel Institute) | Year: 2015
Some of the most significant Danieli innovations in the field of long products are the: «The EWR® system, through automatic continuous head-to-tail flash welding of billets at reheating furnace exit side, enables uninterrupted production of the mill for endless rolling of straight bars, wire rod and bar in coils. The process is particularly beneficial to wire rod and bar-in-coil mills as it enables production of "customized-size coils" (any coil weight according to specific Customer request) and with extra-high final coil weight, even starting from low-weight billets. » The Spooler process is based on twist-free winding of hot-rolled rebars into highly compact/ultra-heavy coils featuring a unique cobble-free un-coiling capability. This enables high-speed feeding of the downstream cold-processing lines with hot-rolled spooled coils coming directly from the rolling mill without the need of any traditional off-line operation (such as de-coiling, stretching & re-winding). The system results highly beneficial both to bar-in-coil producers and to the downstream lines operators. The latest step forward is the combined application of these two systems that has made it possible for endless rolling of spooled bar in coils, enabling to cumulate the benefits of the two processes, particularly as far as transformation costs are concerned.
Del Pin L.,Ferriere Nord Pittini Group |
Redolfi N.,Danieli Morgardshammar |
Taurino A.,Danieli Morgardshammar |
Buzzi G.,Danieli Automation
MPT Metallurgical Plant and Technology International | Year: 2012
Thanks to the collaboration between Ferriere Nord and the Danieli Group, the two-strand wire rod mill in operation since the end of the 1970s, has undergone several expansion phases. Today this mill is able to produce over 1 million t/year of wire rod. In spite of the recession that has affected steelmakers all over the world, it continues to be one of the most efficient and productive plants in Europe.
Zhang X.,Wright State University |
Zhang Q.,Wright State University |
Zhao S.,Wright State University |
Ferrari R.,Danieli Automation |
And 3 more authors.
IFAC Proceedings Volumes (IFAC-PapersOnline) | Year: 2011
In this paper, a fault detection and isolation (FDI) method is developed for wind turbines based on a benchmark system model. The FDI method follows a general architecture developed in previous papers, where a fault detection estimator is used for fault detection, and a bank of fault isolation estimators are employed to determine the particular fault type/location. Each isolation estimator is designed based on a particular fault scenario under consideration. Some representative simulation results are given to show the effectiveness of the FDI method. © 2011 IFAC.
Sellan R.,Danieli Centra Met |
Rinaldi M.,Danieli Centra Met |
Coughlan R.,Danieli Automation
Steel Times International | Year: 2011
Danieli Centro Met and Danieli Automation have designed a new combined Ladle Furnace - Vacuum Degasser (LF-VD) plant for SSAB Oxelösund, equipped with twin tanks on movable cars to maximize productivity. The plant is meeting the tight product quality requirements and has a new-generation, thermodynamics based metallurgical model to ensure the required end point precision. A heat starts in the LF-VD when the ladle is placed into a tank car, and that it only stops when this ladle is taken out of it. The L3 provides the time target for when a given heat needs to leave the LF-VD for the caster, and also the temperature at which it should do so. In the production system, the messages exchanged between the various L2 processes themselves, and also between the L2 and the L1 and L3 interfaces, are recorded.
Aurora C.,Danieli Automation |
Ferrara A.,University of Pavia |
Foscolo M.,University of Pavia
2007 European Control Conference, ECC 2007 | Year: 2015
The design of a globally stable second order sliding mode controller for sensorless induction motors is discussed in this paper. The adaptive component of the controller enables to compensate for the variation in time of the rotor resistance value, while the use of a suitably designed sliding mode speed/flux observer avoids the necessity of including speed and flux sensors within the control loop, making this proposal suitable for sensorless applications. The use of a second order sliding mode control approach, realized by regarding the second time derivatives of the stator currents as discontinuous auxiliary control inputs, is effective in limiting the mechanical wear, making this proposal more acceptable in practical applications than those based on conventional sliding mode control. © 2007 EUCA.
Mukhopadhyay A.,Danieli Automation |
Galasso L.M.,Danieli Automation
Metallurgia Italiana | Year: 2010
Be it a plate or a bar, uniformity of mechanical properties is an important indicator of superior quality. To manufacture such products the cooling systems are required to be properly controlled and tuned. In the conventional practice, the mechanical properties are tested after the product is manufactured. This leaves no room to take any corrective action. An accurate estimation of property during actual processing stage itself is required to control the cooling system. Danieli has developed PQM and QTB PLUS systems for monitoring and control of plates and bars respectively in real time. The estimation of mechanical properties such as Yield Strength, Tensile Strength, Hardness, and Elongation is made with the help of a series of interconnected, physically based mathematical models, complemented by empirical and data driven techniques to include processing uncertainties. The accuracy of the PQM system is ± 54 MPa for both YS and UTS, and ± 32 points for HV. And that for the QTB PLUS system is ± 20 MPa for YS, and ± 25 MPa for UTS. Such systems are useful for Testing, Quality Assurance, and Process Control. QTB Plus has been implemented in Riva Plants at Verona (Italy) and Seville (Spain). PQM is scheduled to be implemented in Iran this year.
Mukhopadhyay A.,Danieli Automation |
Polo A.,Danieli Automation
AISTech - Iron and Steel Technology Conference Proceedings | Year: 2010
DANIELI has recently implemented its Coil Quality Estimator (DANIELI-CQE™) system to the Hot Strip Mill of United Metallurgical Company (OMK) at Vyksa, Russia. This system is developed for the purpose of real time assessment and control of mechanical properties for hot rolled coils. Mechanical properties such as strength, toughness, ductility and hardness are predicted over the entire length of a strip while it is processed. The property estimation is based on the final microstructure as predicted from a group of interconnected physically based metallurgical models. The CQE system is available for different steel grades such as low, medium and high carbon steels, and also High Strength Low Alloy (HSLA) grades with different microalloying elements. The CQE implementation and commissioning has been successful at OMK. The system performance, as judged by accuracy and reliability of prediction, has been compared with the physical material testing data from the plant. The results are found to be excellent. CQE is found useful for generation of test certificate of a coil, quality assurance, process control, product development, and customer claim assessment. It is used for resource optimization for production, and other operational improvements such as reduction of downgrades. The present paper shares the implementation experience and the system performance at the OMK hot strip mill.
Cappellari D.,Danieli Automation |
De Siervo U.,Danieli Automation |
Guttman G.,Danieli Automation
AISTech - Iron and Steel Technology Conference Proceedings | Year: 2012
In a modern steel plant, a vast amount of data is constantly being collected by various acquisition systems, e.g. PLC, SCADA but only a small fraction of this data is ever properly analyzed to obtain any useful information concerning the metallurgical processes. At present in fact, most plant data is just displayed instantaneously at some user interface and then lost forever. Additionally the Level 2 databases typically store their process data for just a few months. In other words, there is a considerable waste of opportunities to gather information as data is not considered from a historical perspective. In any case, the tools available with which to analyze the data available are not as user friendly and flexible as they need to be. The MOREIntelligence system has been conceived and developed in order to address this major concern: the transformation of the huge amount of production data available into information for decision-making, i.e. knowledge. The system collects data from a range of heterogeneous sources, automatically reconciles, synchronizes and normalizes them, storing the results in a dedicated database. For the melting process, in particular, MOREIntelligence collects and stores the enormous quantity of data generated during the production and, most importantly, readily facilitates finding correlations and dependencies among the great number of different variables involved here. MOREIntelligence provides an overview of the KPIs and BPIs via standardised web reports, while providing powerful features for further unrestricted ad-hoc exploration of the data.