TiO2–FeO–Ti2O3体系熔体局域结构和输运性质的机器学习分子动力学模拟

Machine learning molecular dynamics simulations of local structure and transport properties of TiO2–FeO–Ti2O3 melt

  • 摘要: 钛铁矿还原熔炼过程存在反应速率不高、渣铁分离不好、钛渣质量不优的问题. 钛渣熔体输运性质的调控是实现高品质钛渣高效制备的关键. 本论文通过经典分子动力学(Classical molecular dynamic, CMD) 模拟的方法,构建初始构型,用于第一性原理分子动力学(Ab initio molecular dynamic, AIMD)计算. 根据AIMD计算结果,构建数据集. 基于该数据集和神经网络理论,训练出准确的机器学习势,并根据原子力和体系能量验证其准确性. 采用获取的机器学习势函数,开展了TiO2–FeO–Ti2O3体系的局域结构和输运性质的分子动力学模拟. 结果表明:TiO68−八面体和TiO69−八面体参与网络骨架的构建,TiO68−八面体的稳定性大于TiO69−八面体. 不同FeO含量下,体系中TiO68−和TiO69−都是TiOnm的主体. 当FeO质量分数从5%增加到19%时,体系中团簇氧和桥氧向非桥氧和自由氧转变,体系的结构复杂程度(Degree of structure complexity, DSC)值从1.37降低到0.62,Q4Q5Q6转变为Q0Q1Q2Q3,体系聚合度(Degree of polymerization, DOP)的值从4.34降低到1.84,体系的复杂度和聚合度降低,网络骨架的整体强度降低,体系的黏度值从0.043 Pa·s降低到0.037 Pa·s. 研究结果将为高品质钛渣的低碳、高效制备奠定理论和技术基础.

     

    Abstract: Sponge titanium and titanium dioxide are the main products of the titanium metallurgy industry. Titanium slag is the key raw material for the preparation of sponge titanium and titanium dioxide, and its preparation method is the high-temperature reduction smelting of ilmenite in an electric furnace. The high-temperature reduction smelting process of ilmenite has many characteristics different from the ordinary pyrometallurgical smelting process. During this process, the iron oxide in the ilmenite is selectively reduced to metallic iron, and the titanium oxide is enriched in the slag. The by-product of metal iron and the main product of titanium slag are obtained by the separation of molten iron and slag. However, there are some problems in the reduction smelting process of ilmenite, such as low reaction rate, poor separation of slag and iron, and inferior quality of titanium slag. The control of the transport properties of titanium slag melt is key to achieve the efficient preparation of high-quality titanium slag. In this work, the initial configuration for the first-principles molecular dynamics simulation was constructed using classical molecular dynamics. According to the calculation results of the first-principles molecular dynamics simulation, the dataset was constructed, and the accurate machine learning potential function was trained based on the neural network theory. The local structure and transport properties of the TiO2–FeO–Ti2O3 system was studied by machine learning molecular dynamics simulation. The results show that the average bond lengths of Ti4+—O2−, Ti3+—O2−, and Fe2+—O2− are 1.88, 1.88, and 1.83 Å, respectively. There are two main connection modes between TiOnm polyhedral units: corner-sharing and edge-sharing. The TiO68− and TiO69− octahedra are involved in the construction of network skeleton. The stability of TiO68− octahedron is higher than that of TiO69− octahedron. Under different FeO contents, TiO68− and TiO68− are the main octahedra in the system. When the mass fraction of FeO increases from 5% to 19%, the variation of the average CN (coordination number) value is Ti3+—O2− > Ti4+—O2−; the tricluster oxygen and bridge oxygen in the system are transformed into nonbridge oxygen and free oxygen; the DSC (degree of structure complexity) value of the system decreases from 1.37 to 0.62; Q4, Q5, and Q6 are transformed into Q0, Q1, Q2, and Q3; and the DOP (degree of polymerization) value decreases from 4.34 to 1.84. The sequence of diffusion abilities of different ions is displayed as follows: Fe2+ ≈ O2− > Ti3+ > Ti4+. When the mass fraction of FeO increases from 5% to 19%, the complexity and polymerization degree of the system and overall strength of the network skeleton decreases, and the viscosity value of the system decreases from 0.043 Pa·s to 0.037 Pa·s. In this work, the correlation model between the viscosity value and the structural parameter DSC value of TiO2–FeO–Ti2O3 system was constructed. The model can reveal the root cause of the change of the viscosity of the system from the physical essence and predict the viscosity of the system. The results will lay the theoretical and technical foundation for the low-carbon and efficient preparation of high-quality titanium slag.

     

/

返回文章
返回