基于LSTM-Attention与MOPSO高炉节能减排控制算法研究

Research on control algorithm for energy saving and emission reduction of blast furnace based on LSTM-Attention and MOPSO

随着节能环保观念和绿色持续发展理念的深入人心,如何做好高炉节能减排已为目前钢铁行业面对的主要问题之一。为实现高炉节能减排,将人工智能技术与高炉生产数据相结合,提出了燃料比最低和煤比最高的多目标优化方案。在燃料比和煤比预测方面运用随机森林(RF)、长短时记忆网络结构(LSTM)、结合注意力机制的长短时记忆(LSTM-Attention)3个算法对比分析,选择出对燃料比和煤比预测最准确的LSTM-Attention模型作为预测模型。并在LSTM-Attention预测模型基础上,结合多目标粒子群算法(MOPSO)和非支配排序遗传算法(NSGA-II)分别寻找Pareto最优解进行对比,选择效果较好的MOPSO进行结果分析。结果表明,在高炉生产工况一定的情况下,控制决策变量压差、氧量、喷煤量和风量的参数值,约能降低能耗4.06×10^(11)kJ/年,减少CO^(2)的排放量25.91 t/年,为高炉实现节能减排提供技术支持。

With the concept of energy conservation and environmental protection and the concept of sustainable green development deeply rooted in the hearts of people,how to do a good job in energy conservation and emission reduction of blast furnaces has become one of the main problems facing the steel industry at present.In order to achieve energy conservation and emission reduction of blast furnace,a multi-objective optimization scheme with the lowest fuel ratio and the highest coal ratio is proposed by combining artificial intelligence technology with blast furnace production data.In terms of fuel ratio and coal ratio prediction,random forest(RF),long short memory network structure(LSTM),and long short memory combined with attention mechanism(LSTM Attention)are used for comparative analysis,and LSTM Attention model,which is the most accurate for fuel ratio and coal ratio prediction,is selected as the prediction model.On the basis of LSTM Attention prediction model,combined with multi-objective particle swarm optimization(MOPSO)and non dominated sorting genetic algorithm(NSGA-II),the pareto optimal solution is found and compared,and MOPSO with better effect is selected for result analysis.The results show that under a certain production condition of the blast furnace,the energy consumption can be reduced by 4.06%by controlling the parameter values of the decision variables such as pressure difference,oxygen content,coal injection volume and air volume×10^(11)kJ/year,reducing CO^(2)emissions by 25.91t/year,providing technical support for energy conservation and emission reduction of blast furnace.