Tecnologia em Metalurgia, Materiais e Mineração
https://tecnologiammm.com.br/doi/10.4322/2176-1523.20191792
Tecnologia em Metalurgia, Materiais e Mineração
Artigo Original

KAOLIN REFLECTANCE SPECTROSCOPY: USING PLS-R TO PREDICT CONTAMINANT CONTENT

ESPECTROSCOPIA DO CAULIM: USO DE PLS-R PARA PREDIZER O CONTEÚDO DE CONTAMINANTES

Paulo Ricardo Nunes da Conceição, Carlos Otávio Petter, Carlos Hoffmann Sampaio, Aaron Young

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Abstract

The present study deals with the prediction of contaminant concentrations in kaolin using Partial Least Squares Regression (PLS-R). The aim is to show that PLS-R method can be used to predict contaminant concentration in kaolin. High level kaolin means a kaolin with high-brightness. Since brightness is directly related to the reflectance spectrum and kaolin contaminants affect the reflectance spectrum it is important to the beneficiation of kaolin relates optical features and contaminants. Depending on the product to be produced, the optical parameters will influence how the kaolin will be processed. High-brightness kaolin and two red and yellow inorganic pigments were used to simulate colours contaminants frequently found in Brazilian kaolins, such as, hematite, goethite, rutile and anatase. By adding different pigment concentrations to the pure kaolin, it was possible to create a small dataset containing the visible reflectance spectrum of each sample with the respective optical quality parameters of the kaolin. Results allow us to conclude that PLS-R can predict through the reflectance spectrum the contaminant concentration of the kaolin with R-squared equal 0.9954 for red content and R-squared equal 0.9973 for yellow one.

Keywords

Kaolin clays; Reflectance spectra; Brightness; Partial Least Squares.

Resumo

O presente estudo trata da predição da concentração de contaminantes presentes no caulim através do uso de Partial Least Squares Regression (PLS-R). O objetivo é mostrar que PLS-R pode ser usada para prever o conteúdo de contaminantes no caulim. Para o caulim ser alto nível é preciso ter alta alvura. Como alvura está diretamente vinculada ao espectro de reflectânica e este é afetado pelos contaminantes é importante para o processamento de caulim relacionar contaminantes com parâmetros ópticos. Os parâmetros ópticos do caulim influenciam como ele será produzido. Caulim de alta alvura e dois pigmentos inorgânicos amarelo e vermelho foram usados para simular contaminantes encontrados no caulim brasileiro, como, hematita, goetita, rutilo e anatásio. Adicionando diferentes concentrações de pigmento ao caulim puro, foi possível criar uma base de dados contendo o espectro de reflectância de cada amostra com seus respectivos parâmetros ópticos de qualidade. Conclui-se que PLS-R, através do espectro de reflectância, pode prever as concentrações dos contaminantes do caulim contaminado com R2 igual a 0,9954 para contaminante vermelho e R2 igual a 0,9973 para amarelo.

Palavras-chave

Caulim; Espectro de reflectância; Alvura; Partial Least Squares.

Referências

1 Luz AB, Lins FAF. Rochas e minerais industriais: usos e especificações. Rio de Janeiro: CETEM/MCT; 2008.

2 Murray HH. Traditional and new applications for kaolin, smectite, and palygorskite: a general overview. Applied Clay Science. 2000;17(5-6):207-221. http://dx.doi.org/10.1016/S0169-1317(00)00016-8.

3 Murray HH. Applied clay mineralogy: occurrences, processing and application of kaolins, bentonites, palygorskitesepiolite, and common clays. Amsterdam: Elsevier; 2007. p. 85-109.

4 Montes CR, Melfi AJ, Carvalho A, Vieira-Coelho AC, Formoso MLL. Genesis, mineralogy and geochemistry of kaolin deposits of the Jari River, Amapá State, Brazil. Clays and Clay Minerals. 2002;50(4):494-503. http://dx.doi.org/10.1346/000986002320514217.

5 Pruett RJ. Kaolin deposits and their uses: Northern Brazil and Georgia, USA. Applied Clay Science. 2016;131:3-13. http://dx.doi.org/10.1016/j.clay.2016.01.048.

6 Santos E, Scorzelli RB, Bertolino LC, Alves OC, Munayco P. Characterization of kaolin from the Capim River region - Brazil. Applied Clay Science. 2012;55:164-167. http://dx.doi.org/10.1016/j.clay.2011.11.009.

7 Santos AEA Jr, Rossetti DF, Murray HH. Origins of the Rio Capim kaolinites (northern Brazil) revealed by δ18O and δD analyses. Applied Clay Science. 2007;37(3-4):281-294. http://dx.doi.org/10.1016/j.clay.2007.01.005.

8 Silva FANG, Luz AB, Sampaio JA, Bertolino LC, Scorzelli RB, Duttine M, et al. Technological characterization of kaolin: Study of the case of the Borborema-Seridó region (Brazil). Applied Clay Science. 2009;44(3-4):189-193. http://dx.doi.org/10.1016/j.clay.2009.01.015.

9 Sousa DJL, Varajão AFDC, Yvon J, Scheller T, Moura CAV. Ages and possible provenance of the sediments of the Capim River kaolin, northern Brazil. Journal of South American Earth Sciences. 2007;24(1):25-33. http://dx.doi.org/10.1016/j.jsames.2007.02.007.

10 Bertolino LC, Rossi AM, Scorzelli RB, Torem ML. Influence of iron on kaolin whiteness: an electron paramagnetic resonance study. Applied Clay Science. 2010;49(3):170-175. http://dx.doi.org/10.1016/j.clay.2010.04.022.

11 Castellano M, Turturro A, Riani P, Montanari T, Finocchio E, Ramis G, et al. Bulk and surface properties of commercial kaolins. Applied Clay Science. 2010;48(3):446-454. http://dx.doi.org/10.1016/j.clay.2010.02.002.

12 Chandrasekhar S, Ramaswamy S. Influence of mineral impurities on the properties of kaolin and its thermally treated products. Applied Clay Science. 2002;21(3-4):133-142. http://dx.doi.org/10.1016/S0169-1317(01)00083-7.

13 Garcia AG, Buxton M. Visible and infrared reflectance spectroscopy for characterization of iron impurities in calcined kaolin clays. In: Proceedings of the Optical Characterization of Materials Conference (OCM 2015); 2015. Karlsruhe: KIT Scientific Publishing; 2015. p. 215-226.

14 Hunter RS, Harold RW. The measurement of appearance. 2nd ed. New York: John Wiley & Sons; 1987.

15 Morgano MA, Faria CG, Ferrão MF, Bragagnolo N, Ferreira MMC. Determinação de proteína em café cru por espectroscopia NIR e regressão PLS. Food Science and Technology. 2005;25(1):25-31. http://dx.doi.org/10.1590/S0101-20612005000100005.

16 American National Standards Institute. TAPPI T 535-OM-03: brightness of clay and other mineral pigments (D/0 DIFFUSE). Washington: ANSI; 2003.

17 American National Standards Institute. TAPPI T 452 OM-02: brightness of pulp, paper, and paperboard (directional reflectance at 457 nm). Washington: ANSI; 2002.

18 Nayatani Y, Sobagaki H. Relationship between brightness/luminance ratio and additivity-law failure. Color Research and Application. 2002;27(3):185-190. http://dx.doi.org/10.1002/col.10045.

19 Hunter RS, Harold RW. The measurement of appearance. 2nd ed. New York:John Wiley & Sons; 1987.

20 Melville MD, Atkinson G. Soil colour: Its measurement and its designation in models of uniform colour space. European Journal of Soil Science. 1985;36(4):495-512. http://dx.doi.org/10.1111/j.1365-2389.1985.tb00353.x.

21 Alpaydin E. Introduction to machine learning. 2nd ed. Massachusetts Institute of Technology; 2010. 579 p.

22 Esbensen KH. Multivariate data analysis: in practice. 4th ed. CAMO; 2000. 600 p.

23 Conceição PRN. Utilização de análise multivariada de dados na otimização de misturas de minerais industriais para a formulação de tintas [tese]. Porto Alegre: PPGEM/UFRGS; 2006.

24 Conceição PRN, Petter CO, Sampaio CH. Prediction of water-based paint properties based on their mineral fillers: Simplex-PLSR coupling application. HOLOS. 2018;34(2):2-15.

25 Wold S, Sjostrom M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems. 2001;58(2):109-130. http://dx.doi.org/10.1016/S0169-7439(01)00155-1.

26 Esbensen KH, Guyot D, Westad F, Houmoller LP. Multivariate data analysis in practice: an introduction to multivariate data analysis and experimental design. 5th ed. Oslo: CAMO; 2002. 598 p.

27 Esbensen K, Geladi P. Strategy of Multivariate Image Analysis (MIA). Chemometrics and Intelligent Laboratory Systems. 1987;7:67-86.

28 Sales PFS. Estudo dos tratamentos químico e térmico na caulinita e a influência na remoção de contaminantes em efluentes de mineração [dissertação]. Lavras: Universidade Federal de Lavras; 2011.

29 Gonçalves IG. Determinação da concentração de contaminantes no caolim através da Teoria de Kubelka-Munk [dissertação]. Porto Alegre: Universidade Federal do Rio Grande do Sul; 2009.

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