Terminal Optimization Control for Converter Steelmaking Based on the Auxiliary Resources Operating Characteristics

Abstract:

The end point control for converter steelmaking is important operation in converter smelting later stage, and the end point control level directly affects the production efficiency and product quality. From the point of view of auxiliary resources operating characteristics, we analyzed the steelmaking end point control process, established the terminal forecast model based on DRNN type neural network, and established terminal optimization control model using improved particle swarm optimization algorithm to solve, and finally drew converter terminal optimization control strategy. Through the simulation, the optimal results show that: When the end forecast error (ω[△C]＜±0.03%), hit rate is 93.1%, and with (ω[△T]＜±12°C), hit rate is 94%, supplementary blowing oxygen time is 2.3 min, and materials resources saving 15%. For promoting the work of energy saving and emission reduction of Converter steelmaking process, it has important practical significance.

• Periodical: Advanced Materials Research (Volumes 634-638)
• Pages: 3265-3270
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.634-638.3265
• Online since: January 2013
• Authors: Zhi Peng He, Hua Zhang, Yan Hong Wang
• Keywords: Converter Steelmaking, DRNN Type Neural Network, Material Resource, Simulation, Terminal Optimization Control

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