硅酸盐通报2017，Vol.36Issue(3)：1046-1050,5.
基于高斯过程回归的软测量模型预测水泡陈化中钢渣f-CaO含量
Forecast f-CaO Content of Steel Slag in Sodden Aging by Soft Sensor Modeling Based on Gaussian Process Regression
陈华 1李辉 2顾恒星 1杨刚 1陈伟 2徐德龙1
作者信息
- 1. 西安建筑科技大学材料与矿资学院,西安 710055
- 2. 中冶宝钢技术服务有限公司,上海 201999
- 折叠
摘要
Abstract
Sodden aging method was used to deal with BSSF steel slag, and the content of free calcium (f-CaO) in BSSF steel slag was traced and checked.Soft sensor modeling based on gaussian process regression was established by gaussian process regression to forecast the f-CaO content in steel slag by sodden aging.The results show that f-CaO content in BSSF steel slag is effective reduced by sodden aging method to meet the requirements of the safe use, namely f-CaO content in A-BSSF steel slag is about 3.98%-4.03% after 120 d and f-CaO content in B-BSSF steel slag is about 9.84%-10.03% after 105 d.Soft sensor modeling based on gaussian process regression real value agree well with the forecast value, the relative error is-1.493%-0.748%, which increases the prediction accuracy efficiently of the f-CaO content in steel slag by sodden aging.关键词
水泡陈化/滚筒钢渣/高斯过程回归/游离氧化钙/软测量Key words
sodden aging/BSSF steel slag/gaussian process regression/free calcium/soft sensor分类
能源科技引用本文复制引用
陈华,李辉,顾恒星,杨刚,陈伟,徐德龙..基于高斯过程回归的软测量模型预测水泡陈化中钢渣f-CaO含量[J].硅酸盐通报,2017,36(3):1046-1050,5.
