Efficient weakly supervised LIBS feature selection method in quantitative analysis of iron ore slurry
文献类型:期刊论文
作者 | Chen T(陈彤)1,2,3,4; Sun LX(孙兰香)1,2,3; Yu HB(于海斌)1,2,3; Qi LF(齐立峰)1,2,3; Shang D(尚栋)1,2,3,4; Xie YM(谢远明)1,2,3,5 |
刊名 | Applied Optics |
出版日期 | 2022 |
卷号 | 61期号:7页码:D22-D29 |
ISSN号 | 1559-128X |
产权排序 | 1 |
英文摘要 | On-stream analysis of the element content in ore slurry plays an important role in the control of the mineral flotation process. Therefore, our laboratory developed a LIBS-based slurry analyzer named LIBSlurry, which can monitor the iron content in slurries in real time. However, achieving high-precision quantitative analysis results of the slurries is challenging. In this paper, a weakly supervised feature selection method named spectral distance variable selection was proposed for the raw spectral data. This method utilizes the prior information that multiple spectra of the same slurry sample have the same reference concentration to assess the important weight of spectral features, and features selected by this prior can avoid over-fitting compared with a traditional wrapper method. The spectral data were collected on-stream of iron ore concentrate slurry samples during the mineral flotation process. The results show that the prediction accuracy is greatly improved compared with the full-spectrum input and other feature selection methods; the root mean square error of the prediction of iron content can be decreased to 0.75%, which helps to realize the successful application of the analyzer. |
WOS关键词 | INDUCED BREAKDOWN SPECTROSCOPY ; RAY-FLUORESCENCE ANALYSIS ; ON-STREAM ; MINERAL IDENTIFICATION ; TAILING SLURRIES ; WATER |
资助项目 | National Natural Science Foundation of China[62173321] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC037] ; Science and Technology Service Network Initiative Program, CAS[KFJ-STS-QYZD2021-19-002] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences |
WOS研究方向 | Optics |
语种 | 英语 |
WOS记录号 | WOS:000762602500005 |
资助机构 | National Natural Science Foundation of China (62173321) ; Key Research Program of Frontier Sciences, CAS (QYZDJ-SSW-JSC037) ; Science and Technology Service Network Initiative Program, CAS (KFJ-STS-QYZD-2021-19-002) ; Youth Innovation Promotion Association of the Chinese Academy of Sciences |
源URL | [http://ir.sia.cn/handle/173321/30304] |
专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
通讯作者 | Sun LX(孙兰香) |
作者单位 | 1.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 4.University of Chinese Academy of Sciences, Beijing 100049, China 5.Shenyang University of Chemical Technology, Shenyang 110142, China |
推荐引用方式 GB/T 7714 | Chen T,Sun LX,Yu HB,et al. Efficient weakly supervised LIBS feature selection method in quantitative analysis of iron ore slurry[J]. Applied Optics,2022,61(7):D22-D29. |
APA | Chen T,Sun LX,Yu HB,Qi LF,Shang D,&Xie YM.(2022).Efficient weakly supervised LIBS feature selection method in quantitative analysis of iron ore slurry.Applied Optics,61(7),D22-D29. |
MLA | Chen T,et al."Efficient weakly supervised LIBS feature selection method in quantitative analysis of iron ore slurry".Applied Optics 61.7(2022):D22-D29. |
入库方式: OAI收割
来源:沈阳自动化研究所
其他版本
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。