摘要:
分析了铁水脱硫时铁水温度、铁水量、初始硫含量、脱硫后硫含量对镁粉耗量的影响, 表明:随铁水温度增加镁粉耗量随之增加;随脱硫后硫含量的降低, 镁粉耗量明显增加且增幅逐步扩大, 为降低成本, 脱硫深度应控制合理。为确定合适的粉剂用量, 建立了基于BP神经网络和回归的铁水脱硫粉剂预报模型, 其中BP神经网络模型是粉剂模型的主输出, 回归模型用于限定输出范围。铁水脱硫粉剂预报模型已实现了在线控制, 无需人工干预, 达到了较好的应用效果。当偏差区间为[-0.001 5%, 0.001 5%]时, 脱硫后硫含量的符合率为90.85%, 可有效实现脱硫后硫含量的控制。
Abstract:
The effect of hot metal temperature,hot metal amount,initial sulfur content,end-point sulfur content,etc,on the consumption of Mg powder was analyzed.It is found out that the consumption of magnesium powder increased with elevation of hot metal temperature,and obviously increased with decrease of end-point sulfur content by amplitude enlarge gradually.In order to reduce cost,the depth of desulfurization should be controlled properly.The desulfurizer consumption prediction model based on BP neural network and regression was established to ascertain appropriate powder consumption. The BP neural network was the main output and the regression model was used to constrain output range. The prediction model was realized on-line control without human intervention,and good application effect was achieved.If deviation between end-point sulfur content and aim sulfur content was within [-0.001 5%,0.001 5%],the coincidence rate of end-point sulfur content reached 90.85%,so the model could effectively achieve the control of the sulfur content after desulfurization.
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