【摘要】 数字化工厂是智能制造技术中的重要环节,对被控对象精准建模是对工艺设计、自动化及智能化系统实现精准控制的重要支撑。针对热风炉燃烧状况复杂,很难建立合适的机理模型的实际情况,提出了基于长短期记忆深度网络(LSTM)对热风炉废气温度进行建模,并用L2正则化的方式对网络进行了优化。经河钢邯钢8#高炉实际数据仿真验证,预测模型的动态性能良好。更多还原

【Abstract】 Digital factory is an important part of intelligent manufacturing technology. Accurate modeling of controlled object is an important support for accurate control of process design,automation and intelligent system. In view of the complicated combustion conditions of hot blast stoves,it is difficult to establish a suitable mechanism model. Then a long-term short-term memory depth network( LSTM) was used to model the hot blast stove exhaust gas temperature,and the network was optimized by L2 regularization. The simulation results of 8# BF show that the dynamic performance of the prediction model is good.更多还原