首页|基于GA-BP神经网络的铁水硅含量预测方法

基于GA-BP神经网络的铁水硅含量预测方法

Prediction method of hot metal silicon content based on improved GA-BP neural network

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长期以来在高炉炼铁过程中铁水硅含量一直作为代表高炉热状态的重要指数.然而由于高炉具有动态特性,内部化学反应十分复杂,是一个典型的黑箱模型,因此对高炉铁水硅含量进行实时预测十分困难.针对这一问题,利用遗传算法(Genetic Algorithm,GA)对传统 BP(Back Propagation)算法进行改进,构建 GA-BP 神经网络预测铁水硅含量.首先将高炉炼铁过程中的 13 个参数(如风量、风压等)进行特征提取,并利用遗传算法全局搜索BP神经网络最优的初始权值和阈值,接着利用前向传播算法(Forward Propagation,FP)在三层神经网络中传递筛选出来的特征并计算出预测值,其中三层神经网络每层神经元个数分别为 7、50、1.最终将铁水硅含量预测值与真实值进行误差分析,利用梯度下降(Gradient Descent,GD)的原理不断更新神经元的权重,直到预测值与真实值之间的误差达到所给定的阈值.相比于传统 BP 神经网络,GA-BP 神经网络改善了 BP 神经网络权值、阈值难定,学习速度慢且易陷入局部最优等缺点.将某钢厂生产过程中实时采集到的数据经过预处理之后,输入到神经网络中进行训练并且利用测试集来验证该模型的精度.最终该模型在测试集上取得了 92%的正确率且均方误差(Mean Square Error,MSE)稳定在 0.001,证明了该模型的有效性.选取了 50 组数据集之外的新数据来进行预测,结果验证了该模型具备指导生产实践的能力.
The silicon content in molten iron during the ironmaking process in a blast furnace has been an important indicator of the furnace's thermal state for a long time.However,predicting the silicon content in real-time is extremely difficult due to the dynamic nature of the blast furnace and the complex internal chemical reactions that occur within it.To address this issue,a GA-BP neural network for predicting the silicon content in molten iron by improving the traditional BP algorithm using genetic algorithms(GA)was proposed.Firstly,feature extraction is performed on 13 parameters(such as air volume,air pressure,etc.)during the blast furnace ironmaking process.A genetic algorithm is used to globally search for the optimal initial weights and thresholds of the BP neural network.Then,the forward propagation algorithm(FP)is used to transmit the selected features in the three-layer neural network and calculate the predicted values.The number of neurons in each layer of the three-layer neural network is 7,50,and 1,respectively.Finally,an error analysis was conducted between the predicted silicon content in molten iron and that of the actual value,using the principle of Gradient Descent(GD)to continuously update the weights of neural network until the error between the predicted value and the actual value reached the given threshold.Compared to traditional BP neural networks,GA-BP neural networks improve the shortcomings of BP neural networks,such as difficult to determine weights and thresholds,slow learning speed,and easy to fall into local optima.After preprocessing the real-time data collected during the production process of a certain steel plant,it is input into a neural network for training and the accuracy of the model is verified using a test set.In the end,the model achieved an accuracy of 92%on the test set and a stable Mean Square Error(MSE)of 0.001,proving the effectiveness of the model.New data outside of 50 datasets for prediction were selected,and the results verified that the model has the ability to guide production practice.

blast furnaceironmakingsilicon contentBP neural networkgenetic algorithmfeature engineering

何奕波、郭辉、张冰谦、朱强、汤海明、李怡宏

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太原科技大学材料科学与工程学院,山西 太原 030024

澳大利亚伍伦贡大学创新材料研究所,澳大利亚新南威尔士州 伍伦贡市 NSW2522

山西太钢不锈钢股份有限公司,山西 太原 030003

高炉 炼铁 硅含量 BP神经网络 遗传算法 特征工程

中央引导地方科技发展资金项目山西省基础研究计划面上资助项目山西省基础研究计划面上资助项目山西省高等学校大学生创新创业训练计划

YDZJSX2022C0282021030212321820220302 1211187202210109006

2024

10.13228/j.boyuan.issn1001-0963.20230158

钢铁研究学报
中国钢研科技集团有限公司

钢铁研究学报

CSTPCD北大核心
影响因子：0.997
ISSN：1001-0963
年,卷(期)：2024.36(3)
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