改进BP网络在铁水预脱硫终点硫含量预报中的应用

针对本溪钢铁集团有限公司的铁水罐喷吹CaO+Mg复合粉剂脱硫过程,采用BP神经网络建立铁水预处理终点硫含量预报模型.在模型建立过程中,为了克服标准BP算法迭代次数多、收敛速度慢的缺点,采用新的自适应调整学习率方法和最大误差学习法对标准BP算法进行了改进.用1 900炉数据进行模型训练,经100炉数据现场验证表明,有12%的炉次预报值与实际值完全一致,有89%的炉次误差≤0.003%,平均误差为0.002 0%.

Application of Improved BP Neural Network to Final Sulfur Content Prediction of Hot Metal Pre-desulfurization

A prediction model of final sulfur content is developed for hot metal pre-desulfurization by use of an improved BP neural network,especially for the desulfurization process by CaO Mg powder co-injection in Benxi Steel Co.Ltd.To overcome the disadvantages of overmuch iterative repetition and slow convergence rate of normal BP algorithm,an approach to readjust adaptively the self-learning rate with self-learning for maximum error is used to improve the normal BP algorithm during modeling.The data from 1 900 heats are used to train the model with other 100 heats randomly picked out as test samples.Test results showed that 12% of the predicted values got from the 100 samples are the same to the actually measured values,89% have the error within 0.003% and the average error is 0.002 0%.