Jamil J. Al Asfar
Assistant Professor, The University of Jordan, Amman, Jordan
Ashraf Salim
Assistant Professor, Philadelphia University, Jarash, Jordan
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http://dx.doi.org/10.3384/ecp110571708Published in: World Renewable Energy Congress - Sweden; 8-13 May; 2011; Linköping; Sweden
Linköping Electronic Conference Proceedings 57:30, p. 1708-1715
Published: 2011-11-03
ISBN: 978-91-7393-070-3
ISSN: 1650-3686 (print), 1650-3740 (online)
This work represents a true case study and analysis of the technical and energy managerial aspects of recommended designs of the production lines of a steel factory in Jordan. A modern structure of a control system based on SCADA (Supervisory Control And Data Acquisition) technology is proposed. Furthermore; the mechanical and electrical maintenance sections in the factory were reviewed due to their major effects on the production cost and energy consumption of the factory. This study was performed in two main phases: The first phase contains the collected data and process assessment that were undertaken by traditional direct observation and activity categorization; while the second phase gave details on the proposed control methodology in terms of design and architecture.
Moreover; a proposal on maintenance planning and procedure program was also included in this study in order to reduce the time and accordingly the cost of maintenance. The steel factory studied produces various steel products such as: Concrete Reinforcement Steel Bars (Rebars); Flat & Square Bars Section which includes standard flat bars; standard square bars; and plane round bars; in addition to Wire mesh in different sizes and steel billets. In steel production industries; two automation levels can; in general; be identified. The first level involves the electromechanical actuation of the devices in the production plant; this level of automation is currently available in every plant. The second level involves the supervision of the production process; this level of automation is less frequent and is generally only partial. In fact; steel production involves a variety of complex physical phenomena; described by sophisticated mathematical models which are rarely usable to derive real-time advice for process supervision and control. Most operators’ support systems for steel production are represented by simple technologies such as microprocessor-based systems.
Based on the outcomes of this study; the factory purchased a new melting furnace of (60) tons capacity instead of the (30) tons capacity melting furnace used in the factory before the study. The factory is considering also the purchase of a scrap press in its new budget in order to improve scrap quality before melting it; in order to reduce the rate of consuming the furnace electrodes. Also; the maintenance section will be restructured by merging electrical and mechanical maintenance sections into one section headed by the deputy of the factory manager.
[1] Reeder; Thomas J. 1995. Take a Flexible Approach - Combine Project Management and Business Principles into Program Management. Industrial Engineering 27(3): pp. 29-35.
[2] E. Quation; Jose´ Manuel Mesa Ferna´ ndez_; Valeriano A ´ lvarez Cabal; Vicente Rodri´guez Montequin; Joaqui´n Villanueva Balsera; Online estimation of electric arc furnace tap temperature by using fuzzy neural networks; Engineering Applications of Artificial Intelligence; 2007.
[3] Fruehan; R.J.; Fortini; O.; Paxton; H.W.; and Brindle; R.; 2000. Theoretical Minimum Energies to Produce Steel for Selected Conditions. Energetics; Inc.; Columbia; MD; US Department of Energy Office of Industrial Technologies Washington; DC.
[4] Leu; Bor-Yuh.; 1996. Simulation Analysis of Scheduling Heuristics in a Flow-Line Manufacturing Cell with Two Types of Order Shipment Environments. Simulation 66(2): pp. 106-116.
doi: 10.1177/003754979606600207.
[5] McIlvaine; Bill. 1996. Planning and Scheduling Gets the Job Done. Managing Automation 11(8): pp. 24-30.
[6] Morton; Thomas E. and David W. Pentico. 1993. Heuristic Scheduling Systems with Applications to Production Systems and Project Management. New York: John Wiley & Sons.
[7] Mundel. Marvin E.; and David L. Danner. 1994. Motion and Time Study Improving Productivity;7th edition. Englewood Cliffs; New Jersey: Prentice-Hall.
[8] Pegden; C. Dennis; Robert E. Shannon and Randall P. Sadowski. 1995. Introduction to Simulation Using SIMAN; 2nd edition. New York; New York: McGraw-Hill.
[9] Pinedo; Michael. 1995. Scheduling Theory; Algorithms; and Systems. Englewood Cliffs; .New Jersey:Prentice-Hall; Incorporated.
[10] Profozich; David M. and David T. Sturrock. 1995. Introduction to SIMAN/CINEMA. In Proceedings of the 1995 Winter Simulation Conference; eds. Christos Alexopoulos; Keebom Kang. William R. Lilegdon and David Goldsman: pp. 515-518.
doi: 10.1109/WSC.1995.478784.
[11] Raman; Narayan. 1995. Input Control in Job Shops. IIE Transactions 27(2): pp. 201-209.
doi: 10.1080/07408179508936732.
[12] Seila; Andrew F. 1995. Introduction to Simulation. In Proceedings of the 1995 Winter Simulation Conference; eds. Christos Alexopoulos. Keebom. Kang; William R. Lilegdon; and David Goldsman; pp. 7-15.
doi: 10.1109/WSC.1995.478698.
[13] Seila; Andrew F. 1995. Introduction to Simulation. In Proceedings of the 1995 Winter Simulation Conference; eds. Christos Alexopoulos. Keebom. Kang; William R. Lilegdon; and David Goldsman; 7-15.
doi: 10.1109/WSC.1995.478698.
