Sahoo K.,Visakhapatnam Steel Plant |
Babu G.V.,Visakhapatnam Steel Plant |
Rao P.N.,Steel Melt Shop |
Jee S.,Steel Melt Shop
AISTech - Iron and Steel Technology Conference Proceedings | Year: 2014
Steelmaking through BOF route is still a predominant process as compared to EAF and Induction Furnace route. More than 60% of World liquid steel production comes from BOF route only. Steelmaking is a autogenous process due to oxidation of silicon which releases tremendous amount of heat that can be utilized and balanced by scrap and iron ore addition. Due to higher price of scrap and scarcity of lump iron ore in the world an alternate resource, iron ore fines in the form of sinter, may be used as a substitute coolant in the steelmaking process. In this paper a study is made for comparing the use of sinter vs iron ore as a coolant in BOF by evaluating the mass and heat balance, total oxygen consumption and trajectory of removal of the impurities especially during de-carburization and oxygen blowing pattern of refining of heats. The quality of slag generated with the usage of sinter as coolant and its impact on refractory lining is also studied. The cost effectiveness of the two processes is compared. © 2014 by AIST.
Deo B.,Indian Institute of Technology Kanpur |
Overbosch A.,Tata Steel |
Snoeijer B.,Tata Steel |
Das D.,Tata Steel |
Srinivas K.,Visakhapatnam Steel Plant
Transactions of the Indian Institute of Metals | Year: 2013
Decarburization, slag formation, foaming, and slopping in basic oxygen furnace (BOF) can now be partly understood, and to some extent predicted with the help of several on-line measurements and on-line control models. The principal reaction in BOF is decarburization. The bulk of the decarburization takes place in the turbulent region of jet impact irrespective of the fact whether the slag is solid, liquid or foamy. Metal droplets are ejected from the jet impact zone but it is difficult to distinguish the decarburization occurring in the bulk metal from that occurring in the droplets. Slag in BOF is heterogeneous and always contains some entrained gas bubbles and solid material (either un-dissolved or precipitated). At no stage the slag is 100 % liquid. A significant part of the metal droplets fall back and travel through the semi liquid slag. Through this mechanism the droplets can cause slag foaming and slopping in the BOF. Phenomenon of slag foaming and slopping can be monitored and controlled by following, dynamically, the audio signals, gas flow rate and composition, by tracking 'Chaos' in chemical reactions, by manipulating the chaotic attractors, and by monitoring the lance water temperature and weight. Relative stability potential (RSP) diagrams are found to be a good indicator of the dynamics of process inside the BOF and help in advance prediction of the impending chaos. © 2013 Indian Institute of Metals.
Sekhar P.C.,Visakhapatnam Steel Plant |
Avadhani P.S.,Andhra University |
Suresh Varma P.,Adikavi Nannaya University |
Bhargava N.R.M.R.,Andhra University
International Journal of Engineering and Technology | Year: 2016
Steel making is a process of converting hot metal into liquid steel by removing impurities by oxidation process. In this process Oxygen is blown into hot metal to oxidize impurities like carbon, Manganese, Sulhpur, Phosphorous and it will form into a slag. After tapping the steel into the ladle Ferro Alloys are added to achieve the target grade of the steel. The liquid steel is sent to Argon Rinsing station for homogenizing the steel and killing the steel to remove dissolved oxygen. In the continuous casting process the homogenized steel is allowed to flow into tundish and from there into bottom less mould. The semi cooled piece of bloom is continuously drawn out of mould and cut into required length pieces. Under one tundish several moulds may be available as per design. Scheduling a Steel making process is a very complicated and it involves lot of equipment and dynamism in nature. Lot of research is ongoing to schedule the steel making process and many attempts have been made using different kinds of algorithms. In this paper the authors have attempted to schedule steel making process at LD and ARS Processes. Different grades are produced in LD Converter based on the Hot Metal supply. Hot metal supply comes from the blast furnace in the form of Jobs. These jobs allocated to the LD Converters using Earliest Dead line first (EDF) scheduling algorithm. The output of LD converters are further processed in Argon Rinsing Stations(ARS) and send to continuous casting machines (CCM) to cast into Blooms. The Heats/Jobs arrived ARS are scheduled based on EDF Scheduling. The EDF algorithm used at LD and ARS have shown better performance metrics when compared with other conventional models metrics. In this paper, the authors have designed and developed a scheduling model based on Earliest Dead Line First. The authors have evaluated performance metrics of the model like Turnaround time, Average waiting time and dead line deviation. These results indicated that the scheduling model has shown a significant improvement of EDF on the job scheduling when compared with conventional methods like First Cum First scheduling (FCFS), Shortest Job First (SJF).
Syamsundar A.,Visakhapatnam Steel Plant |
Naikan V.N.A.,Visakhapatnam Steel Plant
IEEE Transactions on Reliability | Year: 2011
The failure processes of a maintained system can be studied from the failure data using imperfect repair models. Additional information on the failure process can also be utilized in the form of covariates using proportional intensity models for getting more realistic results. Both these models can be combined to form imperfect repair proportional intensity models making use of the times to failure and the covariate data together. Such models have been proposed earlier with imperfect repair models using maximal repair baseline intensity. One model GPIM has been proposed using minimal repair baseline intensity. This paper proposes ARA and ARI imperfect repair proportional intensity models using minimal repair baseline intensity. These models are then applied to the field data from an industrial-setting to demonstrate that appropriate parameter estimates for such phenomena can be obtained, and such models are shown to more closely describe the failure processes of a maintained system. © 2011 IEEE.
Rao V.V.,Visakhapatnam Steel Plant |
Ratnam C.,Andhra University
Tribology in Industry | Year: 2015
This paper describes the comparison between Vibration Analysis (VA) and Acoustic Emission (AE) method to predict the defect severity in rolling element bearings with respect to the gradual increase of defect size. In bearing fault diagnosis vibration based methods are very popular, but the signals acquired by its transducers from the bearings are distorted by other faults and mechanical noise from the equipment. Vibration based methods are effective when the defect in the bearings has already become severe. AE is a non destructive testing (NDT) technique used in structural health monitoring and its application for bearing defect diagnosis is gaining momentum as an alternate diagnostic tool because of its inherent high signal-to-noise-ratio (SNR). A bearing test rig was designed and set up to study the various defects in rolling element bearings in real environment. In earlier cases, though the researchers studied on different types of seeded defects with random sizes, they could not ascertain the correlation between their defect sizes and the vibration amplitude. The experimental investigation reported in this paper is centered on seeded defect of same type with gradual increase of its size on outer race of radially loaded cylindrical roller bearings and running the defective bearing at different speeds and loads. Data acquired through AE & vibration probes simultaneously for better diagnosis. Comparisons between AE and VA over a range of speed and load conditions at gradual increase of defect size are presented and from them it is concluded that AE method is superior to identify the severity of defect. © 2015 Published by Faculty of Engineering.
Ramesh G.V.,Visakhapatnam Steel Plant |
Rao S.N.,Visakhapatnam Steel Plant |
Shashi M.,Andhra University
Communications in Computer and Information Science | Year: 2011
Data-warehousing is the computer application system that transforms a traditional intuitive decision making body into informed decision making organization. It provides key factors/facts across different chosen dimensions for ready consumption to the managers. The flexibility, provided by the data-warehouses for OLAP (On-line Analytical Processing) applications, offers a distinct edge to its practitioners over those who follow traditional way of browsing through conventional static reports for decision-making. However, the development and deployment of a proper data-warehousing solution in any industry is fraught with many challenges and more so in manufacturing industry in view of its complex processes and huge investments. Another important aspect is of identifying the right product to ensure that data-warehousing application can be built successfully for its effective use for on-line Analytical processing. This paper introduces the concept of data-warehousing, importance of it in a manufacturing industry, exploration of different types of solutions, and finally, the challenges faced in implementing the solution. © 2011 Springer-Verlag Berlin Heidelberg.