An online model was proposed to identify the reasons behind changes in the energy consumption of the reheating furnace of a steel processing plant. The heat conversion of the furnace was analyzed and integrated with the fuel consumption of the furnace to obtain a model of the energy consumption. Combined with the mechanism analysis, the basic parameters affecting energy consumption were determined, and four key influencing factors were obtained: furnace output, furnace charging temperature, furnace tapping temperature, and steel type. The specific calculation method of the contribution of each influencing factor was derived to define the conditions of the baseline energy consumption, while the online data were used to calculate the energy value and the actual performance value of the baseline energy consumption. The contribution of each influencing factor was determined through normalization. The cloud platform was used for database reconstruction and programming to realize the online intelligent evaluation of the energy consumption of the reheating furnace. Finally, a case study of the evaluation of the practical energy consumption of a steel rolling furnace in a steel plant was presented. The intelligent evaluation results were quantified and displayed online, and the performance of the system in reducing production line energy consumption was demonstrated.

提出了一种在线模型来确定钢铁加工厂加热炉能耗变化的原因。分析炉子的热转换并与炉子的燃料消耗量相结合，得到能源消耗量模型。结合机理分析，确定了影响能耗的基本参数，得出了4个关键影响因素：出炉量、装炉温度、出炉温度、钢种。推导各影响因素贡献的具体计算方法，定义基准能耗的条件，同时利用在线数据计算能耗值和基准能耗的实际绩效值。通过标准化确定每个影响因素的贡献。利用云平台进行数据库重构和编程，实现加热炉能耗在线智能评价。最后，以某钢铁厂轧钢炉实际能耗评价为例进行了介绍。将智能评价结果量化并在线展示，展示了系统在降低生产线能耗方面的表现。介绍了一个钢铁厂轧钢炉实际能耗评估的案例研究。将智能评价结果量化并在线展示，展示了系统在降低生产线能耗方面的表现。介绍了一个钢铁厂轧钢炉实际能耗评估的案例研究。将智能评价结果量化并在线展示，展示了系统在降低生产线能耗方面的表现。