System Optimization of an Electric Steel Making Plant with Sequenced Production and Dynamic Stock Level
Lingebrant, P.
Dahl, J.
Larsson, M.
Sandberg, E.
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How to Cite

Lingebrant P., Dahl J., Larsson M., Sandberg E., 2012, System Optimization of an Electric Steel Making Plant with Sequenced Production and Dynamic Stock Level, Chemical Engineering Transactions, 29, 523-528.
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One third of the total steel production in the world today is produced by electrical steel making and is supposed to increase. It is a very energy intense process but for the production costs the scrap mix is nowadays clearly the most dominating cost factor. Because the ingoing raw material mix affects the energy consumption and the chemistry of the final product it is an important factor to control.
A system optimization model for a generalized electric steelmaking plant has been developed. The vision has been to include a planned production sequence and a dynamic scrap stock level along with a full material- and energy balance connected to the processes. This gives the opportunity to run optimizations with restrictions similar to real production conditions.
The generalized steelmaking plant produces hot rolled coils and five main processes are included in the model; a material pre-treatment process, an electric arc furnace, a ladle furnace, a continuous casting process and a hot rolling mill process. To estimate the chemical composition of the ingoing scrap grades, a regression model has been made based on process data from a Höganäs Sweden AB plant. Mixed Integer Linear Programming (MILP) has been used as the method for modeling the production system. Simulations and optimizations have been focused on changes in the chemical composition of certain scrap grades, restrictions of the availability of scrap grades and restrictions regarding the forecasted production sequence. The objectives used for the optimizations are production costs and total energy consumption. The model deliveries results in form of optimal raw-material mixes for the different steel grades defined in the model, optimal energy mix and optimal target temperatures for the sub-processes. Further it shows the effect on process parameters such as energy consumption, slag amount, off gas generation, injected carbon and oxygen etc.
The model makes it possible to simulate scenarios that are expected for the future regarding new steel grades, availability of raw-materials and changed amount of tramp elements in the raw material used today. It is a good tool to find an optimal solution not only for a single heat but for a sequence of heats with varying chemical specifications.
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