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- W3186998974 abstract "Optimization techniques are applied widely in iron and steel making process to solve complicated process-related problems. These methods and the models created through them are regularly used in this field to find out the optimum operating conditions in terms of cost, quality, quantity and effectiveness of the process. The various blast furnace parameters like burden distribution, oxygen enrichment, productivity improvement, composition of the top gas, quality of hot metal production, etc., are very difficult to effectively optimize. In recent times data-driven models of diverse nature have been quite successfully applied for this purpose, where evolutionary approaches have made a significant impact in simultaneous optimization of multiple number of objectives in problems related to the iron and steel making industry. In this chapter implementation of various evolutionary strategies are discussed, which are recently applied in this domain to tackle some real-world problems." @default.
- W3186998974 created "2021-08-02" @default.
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- W3186998974 date "2021-07-25" @default.
- W3186998974 modified "2023-09-23" @default.
- W3186998974 title "Data-Driven Optimization of Blast Furnace Iron Making Process Using Evolutionary Deep Learning" @default.
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- W3186998974 doi "https://doi.org/10.1007/978-3-030-75847-9_3" @default.
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