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  • TiO2–FeO–Ti2O3體系熔體局域結構和輸運性質的機器學習分子動力學模擬

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

    • 摘要: 鈦鐵礦還原熔煉過程存在反應速率不高、渣鐵分離不好、鈦渣質量不優的問題. 鈦渣熔體輸運性質的調控是實現高品質鈦渣高效制備的關鍵. 本論文通過經典分子動力學(Classical molecular dynamic, CMD) 模擬的方法,構建初始構型,用于第一性原理分子動力學(Ab initio molecular dynamic, AIMD)計算. 根據AIMD計算結果,構建數據集. 基于該數據集和神經網絡理論,訓練出準確的機器學習勢,并根據原子力和體系能量驗證其準確性. 采用獲取的機器學習勢函數,開展了TiO2–FeO–Ti2O3體系的局域結構和輸運性質的分子動力學模擬. 結果表明:TiO68?八面體和TiO69?八面體參與網絡骨架的構建,TiO68?八面體的穩定性大于TiO69?八面體. 不同FeO含量下,體系中TiO68?和TiO69?都是TiOnm?的主體. 當FeO質量分數從5%增加到19%時,體系中團簇氧和橋氧向非橋氧和自由氧轉變,體系的結構復雜程度(Degree of structure complexity, DSC)值從1.37降低到0.62,Q4Q5Q6轉變為Q0Q1Q2Q3,體系聚合度(Degree of polymerization, DOP)的值從4.34降低到1.84,體系的復雜度和聚合度降低,網絡骨架的整體強度降低,體系的黏度值從0.043 Pa·s降低到0.037 Pa·s. 研究結果將為高品質鈦渣的低碳、高效制備奠定理論和技術基礎.

       

      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 ?, 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.

       

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