基于知识蒸馏的钢铁高炉煤气系统建模方法OA北大核心CSTPCD

Blast furnace gas system in steel enterprises has the characteristics of high volatility,time-variability and great uncertainty,accurately modeling of its future generation and consumption flow plays a crucial role in efficiently decision-making,energy-saving and emissions reduction.In this study,a knowledge distillation-based modelling method for blast furnace gas system is proposed.Based on a long and short-term memory network,a sequence-to-sequence model is built in the teacher network to extract the intermediate features of the samples.And then,a knowledge distillation strategy is constructed which incorporates the intermediate features of the teacher model.Besides,in order to evaluate the capability of feature extraction,a new loss function is established by both considering that of the knowledge distillation process and the regression error of the actual energy data.Validation experiments are carried out by employing real-world data from the blast furnace gas system of a typical steel enterprise,and the results indicate the effectiveness of the proposed method when facing with the modeling problem,so as to provide powerful support for the optimal scheduling of the energy system.