摘要
在分析配矿方案、工艺参数、产、质量指标三者相互关系的基础上,提出了两种优化烧结工艺参数的数学逻辑模型,即网络输出参数优化模型和网络输入参数优化模型.通过神经网络建模,比较建模结果,网络输入参数优化模型的效果更好.在神经网络模型的基础上结合遗传算法求解网络输入参数优化模型,计算出最佳的工艺参数.模型通过应用,不仅降低了烧结能耗,而且提高了烧结矿的产、质量,验证了模型的有效性和实用性.
Based on analyzing the relationships between iron ore blending, sintering process parameters, and output and quality, two models were developed for optimization of process parameters. One was an output optimization network model, the other was an input optimization network model. After modeling with neural network, and comparing the results, the model of input optimization network was better. So the input optimization model was solved based on neural network and genetic algorithms, and the optimal process parameters were calculated. After application, it shows that the model can not only reduce the energy consumption of sintering process, but can also improve the output and quality of sinter. This proved the effectiveness and practicability of the model.
出处
《材料与冶金学报》
CAS
2007年第4期248-251,共4页
Journal of Materials and Metallurgy
基金
教育部新世纪优秀人才支持计划(NCET-05-0690)
中南大学研究生教育创新工程基金资助(042310011)
关键词
烧结过程
工艺参数
优化模型
sintefing process
process parameter
optimization model
