神经网络在稀土熔盐电解槽槽压中的应用研究  

Application of Neural Network for Rare Earth Electrolytic Cell Design

作　　者：张小联[1] 邓左民[2] 胡珊玲[1] 王科军[1] 郭雪锋[1] 

机构地区：[1]赣南师范学院化学与生命科学学院,江西赣州341000 [2]赣州有色冶金研究所,江西赣州341000

出　　处：《中国稀土学报》2009年第2期306-308,共3页Journal of the Chinese Society of Rare Earths

基　　金：国家863计划引导项目(2003AA001024)资助

摘　　要：采用BP神经网络算法研究了氟化物体系稀土电解槽设计时的重要参数如极距、电流密度、槽电压等对电能效率的影响,通过对多个槽体的现场取样数据模拟测试分析,研究了一种基于BP神经网络算法的稀土电解槽槽压模型,研究表明,该模型能够较为准确地预测极距、电流密度与槽电压关系,可用于新型稀土电解槽的优化设计。By BP neural network technology, designed parameters related to rare earth （RE） electrolytic cell for fluoride system, such as electrode gap, current density and cell voltage and so on, which would influence energy efficiency, were studied. The field-sampling data taken from several cells were simulated and analyzed to build a model for RE electrolytic cell voltage. Relationships between these parameters was predicted fairly accurately by the model which could be applicable to design a novel RE electrolysis cell.

关 键 词：神经网络 稀土熔盐电解槽 槽电压 

分 类 号：TQ175.71[化学工程—硅酸盐工业]