摘要
将人工神经网络技术应用到转炉炼钢过程控制,与增量模型结合,开发出转炉炼钢人工智能静态控制模型。通过在武钢80 t转炉上的生产试验证明,转炉人工智能静态控制模型比传统的静态控制模型提高了模型对炼钢过程各因素之间复杂非线性关系的处理能力及对系统随机因素变化的反应能力和适应能力,因而提高了静态模型的控制精度和终点命中率。
In this paper, artificial neural networks technique has been used to LD steelmaking control process, and artificial intelligence static control model of converter steelmaking has been developed combining with the increment model, compared with conventional increment model, artificial intelligence static control model has raised ability of treating the complicated unlined relationship of various factors in steelmaking process, adapting and responding to the change of system random factors. The control precision and the end-point carbon temperature hitting ratio of the static control model has been improved.
出处
《钢铁》
CAS
CSCD
北大核心
1997年第1期22-26,共5页
Iron and Steel
关键词
转炉
炼钢
人工智能
神经网络
静态模型
convertersteelmaking
artificialintelligence
neuralnetworks
staticmodel
