{"title":"Rapid classification of coal by laser-induced breakdown spectroscopy (LIBS) with K-nearest neighbor (KNN) chemometrics","authors":"Zhi Cao, Junjie Cheng, Xiaodan Han, Lianshun Li, Jian Wang, Qingwen Fan, Qingyu Lin","doi":"10.1080/10739149.2022.2087185","DOIUrl":null,"url":null,"abstract":"Abstract It is important to classify coal in the industry to improve its utilization. Herein, coal classification was performed using laser-induced breakdown spectroscopy (LIBS) combined with K-nearest neighbor (KNN) chemometrics. The principal component analysis was used to determine the optimum component of the original data. Eight elements (Al, Fe, Ca, Na, Mg, Si, Ti, and K) were selected as the indices for coal classification, while 11 elements were further divided into four categories as indicators for coal classification using the KNN model. The standard coal samples were divided based upon the ash and volatile values and the elemental content. The results were satisfactory, achieving an optimum accuracy of 97.73%. In contrast to traditional methods, LIBS significantly reduced the analysis time, simplified the process, and maintained high accuracy.","PeriodicalId":13547,"journal":{"name":"Instrumentation Science & Technology","volume":"51 1","pages":"59 - 67"},"PeriodicalIF":1.3000,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Instrumentation Science & Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/10739149.2022.2087185","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 4
Abstract
Abstract It is important to classify coal in the industry to improve its utilization. Herein, coal classification was performed using laser-induced breakdown spectroscopy (LIBS) combined with K-nearest neighbor (KNN) chemometrics. The principal component analysis was used to determine the optimum component of the original data. Eight elements (Al, Fe, Ca, Na, Mg, Si, Ti, and K) were selected as the indices for coal classification, while 11 elements were further divided into four categories as indicators for coal classification using the KNN model. The standard coal samples were divided based upon the ash and volatile values and the elemental content. The results were satisfactory, achieving an optimum accuracy of 97.73%. In contrast to traditional methods, LIBS significantly reduced the analysis time, simplified the process, and maintained high accuracy.
期刊介绍：
Instrumentation Science & Technology is an internationally acclaimed forum for fast publication of critical, peer reviewed manuscripts dealing with innovative instrument design and applications in chemistry, physics biotechnology and environmental science. Particular attention is given to state-of-the-art developments and their rapid communication to the scientific community.
Emphasis is on modern instrumental concepts, though not exclusively, including detectors, sensors, data acquisition and processing, instrument control, chromatography, electrochemistry, spectroscopy of all types, electrophoresis, radiometry, relaxation methods, thermal analysis, physical property measurements, surface physics, membrane technology, microcomputer design, chip-based processes, and more.
Readership includes everyone who uses instrumental techniques to conduct their research and development. They are chemists (organic, inorganic, physical, analytical, nuclear, quality control) biochemists, biotechnologists, engineers, and physicists in all of the instrumental disciplines mentioned above, in both the laboratory and chemical production environments. The journal is an important resource of instrument design and applications data.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
