摘要
准确预报转炉炼钢动态过程的补吹氧气用量和冷却剂添加量,对于提高终点命中率具有重要意义·采用机理模型及基于数据的自适应神经模糊推理系统混合建模方法建立了转炉炼钢动态过程预设定模型·用减法聚类,最小二乘法及梯度下降法辨识了T S模型并用该模型对机理模型进行补偿建模·对一座180t转炉的实测数据进行了仿真,仿真结果表明该方法是切实可行并有效的·
A new framework was presented for the accurate modeling and prediction of the reblown oxygen and the added coolant in dynamic basicoxygenfurnace(BOF) steelmaking processes. The proposed method takes advantages of the modeling approach based on mechanism and uses adaptive neuralnetworkfuzzyinference system(ANFIS) to compensate for the BOF modeling uncertainties based on mechanism. In the ANFIS compensating model, the firstorder TakagiSugeno type fuzzy rules were employed and a hybrid algorithm combining the least square method(LSM) and the gradient descent method was adopted to obtain the model structure. The practical data of an 180t converter were simulated. The simulated results are close to the practical values. The method is practicable and effective.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2003年第8期715-718,共4页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(60074019)
关键词
转炉
炼钢
混合建模
预设定模型
自适应神经模糊系统
T-S模型
减法聚类
basic oxygen furnace(BOF)
steelmaking
hybrid modeling
presetting model
adaptive neural network fuzzy inference system(ANFIS)
T-S model
subtractive clustering
