Waste processing: new near infrared technologies for material identification and selection

被引:15
作者
Cesetti, M. [1 ,2 ]
Nicolosi, P. [1 ]
机构
[1] Univ Padua, Dept Informat Engn, Via G Gradenigo 6-b, I-35131 Padua, Italy
[2] PAL Srl, Via Ind 6-B, I-31047 Ponte Di Piave, Italy
关键词
Data processing methods; Imaging spectroscopy; Pattern recognition; cluster finding; calibration and fitting methods; PATTERN-RECOGNITION; LEAST-SQUARES; CLASSIFICATION;
D O I
10.1088/1748-0221/11/09/C09002
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
The awareness of environmental issues on a global scale increases the opportunities for waste handling companies. Recovery is set to become all the more important in areas such as waste selection, minerals processing, electronic scrap, metal and plastic recycling, refuse and the food industry. Effective recycling relies on effective sorting. Sorting is a fundamental step of the waste disposal/recovery process. The big players in the sorting market are pushing for the development of new technologies to cope with literally any type of waste. The purpose of this tutorial is to gain an understanding of waste management, frameworks, strategies, and components that are current and emerging in the field. A particular focus is given to spectroscopic techniques that pertains the material selection process with a greater emphasis placed on the NIR technology for material identification. Three different studies that make use of NIR technology are shown, they are an example of some of the possible applications and the excellent results that can be achieved with this technique.
引用
收藏
页数:9
相关论文
共 20 条
[1]  
Adams M.J., 1995, CHEMOMETRICS ANAL SP
[2]   Partial least squares for discrimination [J].
Barker, M ;
Rayens, W .
JOURNAL OF CHEMOMETRICS, 2003, 17 (03) :166-173
[3]  
Burns D.M., 2001, HDB NEAR INFRARED AN
[4]  
Cesetti M., 2015, FOT AEIT IT C PHOT T, P4
[5]  
COOMANS D, 1978, ANAL CHIM ACTA-COMP, V2, P409
[6]   ALTERNATIVE K-NEAREST NEIGHBOR RULES IN SUPERVISED PATTERN-RECOGNITION .2. PROBABILISTIC CLASSIFICATION ON THE BASIS OF THE KNN METHOD MODIFIED FOR DIRECT DENSITY-ESTIMATION [J].
COOMANS, D ;
MASSART, DL .
ANALYTICA CHIMICA ACTA, 1982, 138 (JUN) :153-165
[7]   COMPARISON OF RULE-BUILDING EXPERT SYSTEMS WITH PATTERN-RECOGNITION FOR THE CLASSIFICATION OF ANALYTICAL DATA [J].
DERDE, MP ;
BUYDENS, L ;
GUNS, C ;
MASSART, DL ;
HOPKE, PK .
ANALYTICAL CHEMISTRY, 1987, 59 (14) :1868-1871
[8]  
Dhanoa M., 1994, J. Near Infrared Spectrosc., V2, P43, DOI DOI 10.1255/JNIRS.30
[9]  
Hoornweg D., 2012, URBAN DEV SERIES KNO, V15
[10]  
Leardi R., 2003, Nature-inspired methods in chemometrics: genetic algorithms and artificial neural networks