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Burden Optimisation of Lump Charge Materials for Foundry Furnaces

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EN
Abstrakty
EN
Calculation of economically optimum charge burden for foundry furnaces depends on a solution that can be found in the field of mathematical linear programming. A traditional linear optimisation method means looking for continuous variables that can define portion of every charge component. Sometimes, it can be observed that a calculated portion of charge material with a defined lump mass is rounded up, which is wrong because the charge burden prepared in such a way may either show a mistaken chemical composition, or it can entail a significant cost. With charge materials characterised by defined mass lumps, it is recommended to use a modified method of integer linear optimisation. This article contains definitions of a goal function and a system of constraints considering the charge materials as stated above. In addition, a sample calculation has been included hereto to show such optimisation for a selected process of metal melting in a casting furnace.
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Twórcy
  • AGH University of Science and Technology, Faculty of Foundry Engineering, 23 Reymonta Str., 20-059 Krakow, Poland
Bibliografia
  • [1] E. Ziółkowski, Algorithms of furnace charge burden optimization in foundries, Archives of Metallurgy and Materials 52, 3, 487-495 (2007).
  • [2] E. Ziółkowski, Methods review for the determination of burden for foundry furnaces, Archives of Foundry Engineering, ISBN: 978-83-929266-0-3, 449-460 (2009).
  • [3] E. Ziółkowski, Algorithm for burden calculation for foundry furnaces using charge materials with an uncertain composition, Archives of Metallurgy and Materials 58, 3, 887-889 (2013).
  • [4] S. I. Gass, Linear Programming: Methods and Applications, Courier Corporation (2003).
Uwagi
EN
The study was co-financed by AGH as a targeted project No. 11.11.170.318-6
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
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Bibliografia
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bwmeta1.element.baztech-fabd2d72-829f-4719-beb4-50779d6ce26b
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