BP神经网络在铁液预处理脱硫率预测中的应用

Application of BP Neural Network in Prediction of Desulfurization Efficiency of Iron Liquid

水模动力学试验是设计KR脱硫搅拌器结构及优化工艺参数的主要方法之一,此方法存在脱硫率难以提取、试验周期长和成本高的缺点。针对上述问题,提出了一种BP神经网络脱硫率的预测方法。搅拌器在运行过程中,不同的结构参数和操作参数直接影响到脱硫率。基于此,选取叶片厚度、叶片高度、插入深度和搅拌转速作为BP神经网络的输入量,以脱硫率为输出量,构建了3层神经网络预测模型。利用Matlab软件对BP神经网络模型反复训练,训练精度为0.001 02,达到了预设精度。应用该网络模型对脱硫率进行预测,结果表明,建立的模型具有可行性、通用性强等优点,缩短了实验周期,提高了搅拌器的设计效率。

The influence of different structure and operating parameters on desulfurization efficiency was different. A method of predicting the desulfurization efficiency was presented using BP neural network. A three layers BP neural network prediction model was built, selecting blade thickness, blade height, inserting depth and stirrer speed as input, the desulfurization efficiency as output. The BP neural network was trained repeatedly to achieve the preset accuracy by using MATLAB tool, and the training accuracy is 0.001 02, The prediction results show that the established model has the advantages of good fault tolerance and high universality, which can shorten the experimental period and improve the design efficiency.