基于时序注意力网络的高炉煤气预测方法

Blast furnace gas prediction method based on Temporal attention network

现场充分利用高炉煤气(BFG)可有效降低一次能源的消耗,但高炉现场工况不断变化,煤气供需关系时刻处于不平衡状态,导致煤气放散现象仍然存在。为了提高煤气利用率,提出一种基于时序注意力(T-Attention)网络的BFG预测方法。该方法首先结合高炉冶炼机理和最大互信息系数(MIC)选取影响BFG含量的关键因素;然后针对采集数据中存在随机扰动,利用小波分析去除数据中的噪声;且在建模过程中,利用门控循环单元(GRU)捕捉多变量数据中周期性波动规律,同时融入注意力机制实时计算每个样本中各变量与预测值之间耦合关系并进行权重分配,提高模型动态自适应能力和解读性;最后利用某钢铁厂高炉现场数据进行验证。结果表明,T-Attention网络模型预测效果优于传统方法,能够准确预测BFG中的指标,为后期BFG调度以及节能减排提供及时准确的决策参考。

The full use of blast furnace gas(BFG)can effectively reduce the consumption of primary energy,but the working condition of the blast furnace site are constantly changing,and the relationship between gas supply and demand is always in an unbalanced state,resulting in the phenomenon of gas escaping.In order to improve the utilization rate of gas,a BFG prediction method based on Temporal Attention(T-Attention)network is proposed.Firstly,the key factors affecting BFG content are selected by combining the blast furnace smelting mechanism and the maximum mutual information coefficient(MIC).Then,in view of the random perturbation in the collected data,a wavelet analysis is used to remove the noise in the data.In the modeling process,the gated recurrent unit(GRU)is used to capture the periodic fluctuation law in multivariate data,and the attention mechanism is integrated to calculate the coupling relationship between each variable and the predicted value in each sample in real time and distribute the weights,so as to improve the dynamic adaptability and interpretability of the model.Finally,the field data of blast furnace of a steel plant is used for verification.The results show that the prediction effect of the T-Attention network model is better than that of the traditional method,and it can accurately predict the indicators in BFG,and provide timely and accurate decision-making reference for BFG scheduling and energy conservation and pollution reduction in the later stage.