Mobarakeh Steel Company is an Iranian steel company, located 65 km south west of Esfahan, near the city of Mobarakeh, Esfahan Province, Iran. It is the largest steel maker of MENA region, and one of the largest industrial complexes operating in Iran. It was commissioned after the Iranian Revolution in 1979 and initiated operations during 1993. It underwent major revamping during year 2000, and is scheduled for a second and third revamping in 2009–2010, bringing the total steel output to 7,200,000 metric tons per year. The company owns the successful football club, Sepahan. Wikipedia.
Amiri Rad A.,Isfahan University of Technology |
Forouzan M.R.,Isfahan University of Technology |
Sadeghi Dolatabadi A.,Mobarakeh Steel Company
Fatigue and Fracture of Engineering Materials and Structures | Year: 2014
In this paper, the fatigue crack growth in helical gear tooth root has been simulated using linear elastic fracture mechanics. The extended finite element method has been used to simulate 3D fatigue crack growth and obtain growth path. Paris equation has been used to calculate the fatigue life of the gear. The modelling time has reduced considerably compared to previous works carried out on 3D crack growth in gears. Some verifications have been carried out to ensure the reliability of the results. © 2014 Wiley Publishing Ltd.
Poursina M.,University of Isfahan |
Rahmatipour M.,University of Isfahan |
Mirmohamadi H.,Mobarakeh Steel Company
International Journal of Advanced Manufacturing Technology | Year: 2015
A new method for prediction of forward slip in the tandem cold rolling mill without the velocity meter sensors based on rolling geometry is proposed here. According to this proposed method, an algorithm is developed for online estimation of friction coefficient and strip’s behavior. Online exertion of friction coefficient and strip’s behavior in the rolling’s program results in better control. So, the unsaturated actuators are satisfied and the possibility of strip tearing is decreased. The strip’s material is st12. The material is considered elastic-plastic, homogenous, and it follows the Ludwick’s constitutive equation law. The yield stress of strip and Young modulus are determined by simple tension test on a specimen of strip before rolling. For validation of the developed scheme, two operating samples are considered and the results are compared with the available literature. © 2015, Springer-Verlag London.
Poursina M.,University of Isfahan |
Dehkordi N.T.,Isfahan University of Technology |
Fattahi A.,Islamic Azad University |
Mirmohammadi H.,Mobarakeh Steel Company
Simulation Modelling Practice and Theory | Year: 2012
It is well known that tandem cold rolling is one of the most widely used processes in the manufacture of various sheet products with high accuracy and production rate. This paper deals with an optimization problem for tandem cold rolling. A genetic algorithm is developed to optimize the reduction schedules from the power consumption and damage evolution points of view. Damage-coupled finite element simulations are employed to determine the damage objective function. The dominant parameters of the rolling process are calculated using an experimental-analytical model, obtained from an industrial tandem rolling mill. Generally, in rolling process damage and power have conflicting natures and none of them can be improved without degrading the other. In this paper, in the first step, power and damage are optimized independently and some reduction schedules are introduced to minimize power consumption or damage evolution during the process and the results are compared with the experimental observations. Afterwards power and damage are optimized simultaneously by defining a multi-objective function and employing the Pareto optimality; a set of optimized reduction schedules are provided to optimize the power and damage based on the preference ordering of the decision makers in tandem mill. This multi-objective optimization enables the mill operators to select the most appropriate optimized schedule according to the mill necessities. Finally the optimal schedules are numerically simulated to investigate the efficiency of the damage optimized schedule. © 2011 Elsevier B.V. All rights reserved.
Anvari F.,Mobarakeh Steel Company |
Edwards R.,University of Manchester
Journal of Quality in Maintenance Engineering | Year: 2011
Purpose - The main purpose of the research is to develop a comprehensive model for measuring overall equipment effectiveness in the capital-intensive industry such as steel, oil and chemical companies so as to meet their essential requirements. Design/methodology/approach - Market time is used as a representation of all the losses, which affect incurred equipment effectiveness. Based on a comprehensive scheme for loss analysis within market time, the concept of Integrated Equipment Effectiveness (IEE) is developed. Multiple case studies including three different cases within one large Asian steel making company were developed to assess the proposed model. Findings - The case study reveals the importance of the new scheme for loss analysis in the capital-intensive industry. IEE provides a whole perspective on effectiveness based on loading, capital and market features. Practical implications - IEE monitors manufacturing process to utilise equipment effectively as much as possible and also measures the equipment effectiveness for full process cycle in order to respond to the market. It provides a sound perspective on improvement to the capital-intensive industry. Originality/value - The paper provides information on a new model to more accurate estimation of equipment effectiveness in the capital-intensive industry. It helps to optimise resource allocation and make better strategic decisions. The model may be applied as a benchmark to achieve world-class standard. © 2011 Emerald Group Publishing Limited. All rights reserved.
Samet H.,Shiraz University |
Farhadi M.R.,Shiraz University |
Banaeian Mofrad M.R.,Mobarakeh Steel Company
2012 IEEE International Energy Conference and Exhibition, ENERGYCON 2012 | Year: 2012
The time varying nature of electric arc furnace (EAF) gives rise to voltage fluctuations which produce the effect known as flicker. The ability of static VAr compensator (SVC) is limited by delays in reactive power measurements and thyristor ignition. In order to improve the SVC performance, this paper presents a technique for prediction of EAF reactive power for a half cycle ahead. This technique is based on Artificial Neural Networks (ANNs). The procedure uses huge field data, collected from eight arc furnaces in Mobarakeh Steel Industry in Iran. About 90% of the recorded data are used for training the ANN and the rest are used in the test procedure. The performance of the compensator under the case of employing the predicted fundamental reactive power of EAF is compared with that for conventional method by using four indices which are defined based on concepts of flicker frequencies and power spectral density. © 2012 IEEE.