摘要
过热度是铝电解生产过程中的一项重要参数,将过热度保持在适当的范围内可以提高电流效率,减小电解槽损耗,但是过热度测量难度较大且测量过程复杂.因此,基于粒计算理论,提出一种基于时间粒的过热度预测模型.通过在时间序列上构建时间粒,结合时间粒构建新的特征集与样本集,在此基础上,利用分类器对新的样本集进行训练,得到模型.采用山东魏桥铝电有限公司的铝电解生产数据进行实验,结果表明,该方法在预测过热度上较已有模型的预测能力有较大提升。
Superheat is an important parameter in the process of aluminum electrolysis. Keeping the superheat within an appropriate range can improve the current efficiency and reduce cell loss. However, the measurement of superheat is difficult and the measurement process is complex. According to granular computing theory, this paper proposes a prediction model of superheat based on time granule. By constructing time granules on time series,new feature sets and sample sets are constructed combinating with time granules. On this basis,new sample sets are trained by the classifier to obtain the model. In this paper, we use the data of aluminum electrolysis production from Shandong Weiqiao Aluminum and Electricity Ltd to test the experiment. The result shows that the supreheat prediction of this method is better than the existing models.
作者
郭英杰
胡峰
于洪
张红亮
Guo Yingjie;Hu Feng;Yu Hong;Zhang Hongliang(Chongqing Key Laboratory of Computational Intelligence,Chongqing University ofPosts and Telecommunications,Chongqing,400065,China;School of Metallurgy and Environment,Central South University,Changsha,410083,China)
出处
《南京大学学报（自然科学版）》
CAS
CSCD
北大核心
2019年第4期624-632,共9页
Journal of Nanjing University（Natural Science）
基金
国家自然科学基金(61533020,61876027,61751312)
关键词
过热度
粒计算
时间序列
铝电解
superheat
granular computing
time series
aluminum electrolysis
