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Ladle intelligent re-scheduling method in steelmaking–refining–continuous casting production process based on BP neural network working condition estimation
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Ladle intelligent re-scheduling method in steelmaking–refining–continuous casting production process based on BP neural network working condition estimation
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Ladle intelligent re-scheduling method in steelmaking–refining–continuous casting production process based on BP neural network working condition estimation
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Journal Article
Ladle intelligent re-scheduling method in steelmaking–refining–continuous casting production process based on BP neural network working condition estimation
2022
Overview
Frequent delays will be experienced in the start-up of molten steel on the converter equipment during the steelmaking–continuous casting (SCC) production process due to the untimely supply of molten iron or scrap, which may cause conflicts between adjacent heat on the same equipment or in the same casting. The casting machine is cut off, resulting in the failure of the static scheduling plan. SCC production ladle re-scheduling is based on the premise that the production process path remains unchanged, the operation of adjacent heat on the converter and refining furnace does not conflict, and the casting of adjacent heat within the same casting is continuous. The ladle re-scheduling of steelmaking and continuous casting production aims at continuously casting many charges with the same cast and avoiding conflicts of adjacent charges on the same machine. This mechanism proposes a method of ladle re-scheduling in the production process of steelmaking–refining–continuous casting, which is divided into two parts: plan re-scheduling and ladle optimisation scheduling. Firstly, a re-scheduling optimisation model of the steelmaking and continuous casting production is built. This model aims at minimising the waiting time of all charges. The re-scheduling strategy of steelmaking and continuous casting production is proposed by interval processing time of charges and scheduling expert experience. This strategy is composed of two parts: re-scheduling charge decision and charge processing machine decision. Then, the first-order rule learning is used to select the optimisation target to establish the ladle optimal scheduling model. The ladle matching rules are extracted on the basis of the rule reasoning of the minimum general generalisation. The ladle optimisation scheduling method that consists of the optimal selection of the ladle and the preparation of the optimal path of the ladle is proposed. Ladle selection is based on the production process and adopts rule-based reasoning to select decarburised ladle after choosing dephosphorised ladle. Ladle path preparation, which is a multi-priority heuristic method, is designed to decide the path of the ladle from the converter to the refining furnace to the continuous casting machine. Finally, this mechanism was actually verified based on the large-scale data of a steel company in Shanghai, China. Results showed that the production efficiency of steelmaking-refining-continuous casting was improved.
Publisher
Springer London,Springer Nature B.V
