Research on a wireless sensor network method in a real-time system

Abstract

In steelmaking-continuous casting production processes, the optimizing scheduling of ladles is an effective way to reduce energy consumption and improve productivity. Because the ladle scheduling must satisfy multiple conflicting goals and conflicting constraints, it is difficult to adopt the existing optimal scheduling methods. The intelligent ladle scheduling method was proposed for the ladle scheduling problem of molten steel in Baosteel in China. First, the strategy of ladle scheduling was proposed. Then, the first-order rule learning was used to select the optimal objective, and the least general generalization rule reasoning was used to extract the ladle matching rules. In addition, an optimal scheduling model for ladle was established, and an intelligent scheduling method for ladle was proposed, including the ladle matching, ladle path optimization and crane scheduling. Rule reasoning was employed to select decarbonized ladle or dephosphorization ladle. The multi-priority heuristic method was designed to determine the path from ladle to converter, refining furnace and continuous caster. The heuristic method of avoiding interference strategy was designed to decide the crane for ladle transportation in the air and the start and end time of operation. Finally, a software system for ladle scheduling was developed, and industrial validation was carried out with real data. The results show that the overall production efficiency and ladle turnover rate are improved.