【作者】 向嵩；

【导师】 王雨；

【作者基本信息】 重庆大学 ， 钢铁冶金， 2003， 硕士

【摘要】 连铸保护渣是连铸生产中重要的功能材料，开发保护渣，需要对熔渣的性能、组成进行合理的设计。基于性能要求的保护渣的组成设计，目前基本上是基于经验积累的配方修正，这需要较多的实验和开发者长期的相关工作的经验；以实验数据为基础建立的保护渣组分—性能之间数理模型关系，由于保护渣性能与其组成之间存在着复杂的非线性关系、数据样本来源有限、实验数据点变化范围的局限性等因素，实际用于保护渣性能预测的准确性受到限制。 神经网络技术能够逼近任意的非线性系统、能够从背景不清楚和不完全的数据中自动提取反映事物内在规律和特点的知识、并能够通过学习所获得的知识对问题进行求解。针对连铸保护渣不断发展和品种开发的需求，建立了保护渣熔化温度及高温粘度BP神经网络预测模型，取得了较好的效果。 在分析和调研现行保护渣基本组成特征的基础上，确定预测模型的对象为CaO-SiO₂-Na₂O-B₂O₃-Al₂O₃-CaF₂-Li₂O-MnO-MgO的9元渣系；采用兼有上下约束条件的最优混料回归设计方法设计渣系实验点，减少和避免了所研究渣系中数据采集的盲点，保证模型数据来源的广泛性和分布均匀性。通过对233个实验点的保护渣熔化温度和高温粘度测试，探讨了多组元渣系中保护渣组分对性能的影响规律，并以此实验数据为基础，建立了保护渣熔化温度及高温粘度的BP神经网络预测模型。通过对模型隐含层单元数、学习速率等参数的配置优化，提高了模型的预测精度和效率。该模型具有适用组元多、成分变化范围大、结构简单、拓展性强等特点，有较高的预测准确性。采用该模型预测保护渣粘度和熔化温度，平均相对误差分别为8.25％和0.36％，远低于以同组数据为基础建立的多元非线性回归模型相应平均误差63.2％和2.8％。这一结果表明，利用神经网络预测保护渣性能是可行的，可以满足保护渣研发的要求。更多还原

【Abstract】 Mould fluxes play very important roles in continuous casting of steel. Reasonable properties and compositions are necessary requirements for designing and developing mould fluxes. Up to now, all the designing processes mainly have depended upon abundant experimental experience of researchers. The established mathematical model between compositions and properties of mould fluxes were based on experimental results. The accuracy of the model was usually limited in predicting properties because of nonlinear relationship between properties and compositions and limited samples.Neural network(NN) can approach every nonlinear relationship, extract knowledge, which reflects inherent law and feature, from unobvious background and imperfect data, solve problems by the obtained knowledge. To overcome the disadvantage of the empirical models, BP NN was used to construct a series of innovative models to forecast melting temperature and viscosity of mould fluxes.To establish the BP NN model, CaO-SiO2-Na2O-B2O3-Al2O3-CaF2-Li2O-MnO -MgO slag system were employed in the study. The compositions of mould fluxes were designed according to the principle of blending regression design with bound, which could reduce and avoid blind-spot of slag system and guarantee the extensiveness and uniform distribution of data. The effects of compositions on properties of multi-component slag system were obtained according to experimental melting temperature and viscosity of 233 groups of mould fluxes. Base on the data, BP neural network prediction models were established, which were used to forecast melting temperature and viscosity. Prediction accuracy and efficiency were improved through optimizing unit number of hidden layer and learning rate. With simple construction and good extension ability, the models are suited for mould fluxes with multi-components and wide range of compositions. For the same data, average relative predication errors of the models were 8.25% and 0.36% for viscosity and melting temperature respectively, the errors were much less than the errors of 63.2% and 2.8% made by nonlinear regression formula. Therefore, the NN models can be used to predict the molten properties of mould fluxes and meet the requirement of mould fluxes development and design.更多还原