【摘要】 砷是锡矿中最常见的伴生元素，了解砷、锡在冶炼过程中的锡熔体中的赋存状态及挥发行为对于提高锡的冶炼效率及环境污染的控制具有重要意义，但目前缺乏相关研究。由于锡的还原熔炼在高温条件下进行，导致试验研究手段有限。因此，本文基于第一性原理分子动力学（FPMD）模拟计算获得的数据集训练得到的深度神经网络模型势函数，对不同组成的Sn-Fe-As体系在1 673 K下开展了纳秒及上万原子尺度下的动力学模拟研究。获得了1 673 K下不同组成的Sn-Fe-As体系的熔体结构特点以及各组元的挥发特性。通过对动力学模拟结果的分析，获得了Sn-Fe-As体系组元间的相互作用规律，锡熔体中Fe、As含量及铁砷比对锡、砷挥发行为的影响规律。研究结果可为锡冶炼过程中工艺优化及砷的污染控制提供理论依据。更多还原

【Abstract】 Arsenic and its compounds are listed as the first category carcinogens by the World Health Organization. Arsenic is the most common associated element in tin ore,and is often accompanied by the volatilization of tin,arsenic and other elements in the tin smelting process. Existing studies have shown that in the process of tin reduction smelting,the interaction between the main elements tin,iron and arsenic in the melt,especially the strong affinity between iron and arsenic,will generate arsenic iron metal compounds,which have a significant impact on the volatilization behavior of tin and arsenic,but there is still a lack of detailed research on this problem. In recent years,with the decline of tin ore grade,the content of impurities(including arsenic) is increasing. The volatilization of tin and arsenic in tin smelting process has brought economic and environmental pressure to the tin smelting industry. Therefore,it is very important to carry out systematic research on the occurrence and volatilization behavior of each component in the tin melt,so as to reduce the volatilization of tin and arsenic in the smelting process,control the pollution of arsenic from the source,and improve economic benefits. However,due to the high reduction melting temperature of tin,it is difficult to study the structure of tin melt at high temperature by experiment. Therefore,based on the actual tin smelting production,the calculation results of the first principles molecular dynamics simulation of the unit,binary and ternary systems of Sn,Fe and As at 1 673 K show that in the tin iron arsenic system,tin iron and tin arsenic are bonded by weak ionic covalent bonds,and the bonding is unstable,but there is a strong ionic covalent bond between iron and arsenic,which destroys the stability of tin tin tin bond to a certain extent. However,due to the existence of iron,arsenic mainly transfers electrons to iron,so iron can inhibit the weakening of tin tin bond by arsenic to a certain extent. However,due to the covalent bonding of arsenic and iron,the inhibition of iron on arsenic must be affected by stoichiometry.Then,the deep learning algorithm was used to train the data set obtained from the first principles molecular dynamics(FPMD) simulation calculation above to obtain the potential function of the deep neural network model. The dynamic simulation of Sn-Fe-As system with different composition was carried out at 1 673 K at nanosecond and tens of thousands of atomic scales. Through the analysis of the kinetic simulation results,the melt structure characteristics and the volatilization characteristics of different components of Sn-Fe-As system at 1 673 K were obtained. Thermodynamic analysis and kinetic simulation results show that the presence of arsenic in Sn-As system will strongly enhance the volatilization of tin,and the iron in Sn-Fe system will also lead to the volatilization of tin to a certain extent;The solubility of arsenic in tin melt is greater than that of iron in tin. When the ratio of iron to arsenic in tin melt is greater than 2∶1,in addition to iron arsenic tin phase,iron tin phase can also be precipitated in tin melt;when the ratio of iron to arsenic in the tin melt is less than 1∶2,in addition to the iron arsenic tin phase,arsenic tin phase can also be precipitated in the tin melt,and the arsenic tin phase is wrapped in the iron arsenic tin phase. The average concentrations of tin and arsenic in the gas phase of the Sn-Fe-As system with different compositions at 1 673 k,the interaction between the components of the Sn-Fe-As system,and the influence of the content of Fe and As in the tin melt and the ratio of iron to arsenic on the volatilization behavior of tin and arsenic were also obtained. In Sn-Fe-As system,due to the strong interaction between iron and arsenic,iron can inhibit the volatilization of arsenic,but the inhibition is affected by the ratio of iron to arsenic,which is greater than 1∶1 and less than 2∶1;when the ratio of iron to arsenic is less than 1∶1,the inhibitory effect of iron decreases with the increase of arsenic content;the results can provide a theoretical basis for process optimization and arsenic pollution control in tin smelting process. The research also shows that the molecular dynamics simulation driven by deep learning algorithm can provide a new research method for the mechanism research of high temperature heterogeneous reaction system,and promote the theoretical research of smelting process improvement.更多还原