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Integrated deep learning-driven multi-stage steam forecasting and scheduling optimization for converter energy systems - Scientific Reports
Abstract Converter steam is a valuable secondary energy in steelmaking, and improving its recovery and utilization is essential for reducing energy cost. However, converter steam data are inherently time-segmented: before steelmaking starts, only limited planning information is available, while during production, high-frequency process signals drive rapid steam fluctuations.
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