钢铁企业高炉煤气供需预测模型及应用

以钢铁企业高炉煤气系统为研究对象,采用灰色关联度分析了高炉煤气产生量、消耗量的影响因素与煤气量的关系.基于人工神经网络预测方法,建立了高炉煤气BP神经网络预测模型,对钢铁企业各生产工序中高炉煤气的产生与消耗量进行预测,探讨了企业在正常生产、事故检修等工况下各工序的煤气产生量和消耗量预测的合理性.研究表明:所建立的预测模型精度高、误差小,能有效解决实际生产中高炉煤气的供需预测问题,从而减少高炉煤气放散,为企业制定合理煤气使用计划提供了理论依据.

Supply-Demand Forecasting Model of Blast Furnace Gas in Iron & Steel Works and Its Application

With the blast furnace gas (BFG) system of an iron and steel works taken as an object, the relationship between the gas throughput and influencing factors on BFG generation/consumption was analyzed by grey correlation. A prediction model of BFG was developed on the basis of BP neural network for forecasting the supply and demand of BFG in the whole iron/steel-making process. The reasonability of the forecasting of BFG generation and consumption was discussed on various working conditions including normal operation and troubleshooting. The results showed that the forecasting model developed is of high precision with small errors and available to predict actually the BFG supply and demand so as to decrease the unnecessary BFG emission. The model is therefore able to lay a theoretical foundation to schedule the BFG utilization reasonably.