Hostname: page-component-76fb5796d-22dnz Total loading time: 0 Render date: 2024-04-28T22:33:53.939Z Has data issue: false hasContentIssue false
  • English
  • Français

Application of Multivariate Analysis in the Assessment of Ceramic Raw Materials

Published online by Cambridge University Press:  01 January 2024

José V. Lisboa ,
Fernando Rocha  and
Daniel P. S. de Oliveira
José V. Lisboa*
Affiliation:
Laboratório Nacional de Energia e Geologia (LNEG), Mineral Resources and Geophysics Research Unit, Estrada da Portela, Bairro do Zambujal, 2610-999 Amadora, Portugal
Fernando Rocha
Affiliation:
Geobiotec, Geosciences Department, University of Aveiro, 3810-193, Aveiro, Portugal
Daniel P. S. de Oliveira
Affiliation:
Laboratório Nacional de Energia e Geologia (LNEG), Mineral Resources and Geophysics Research Unit, Estrada da Portela, Bairro do Zambujal, 2610-999 Amadora, Portugal
*
*E-mail address of corresponding author: vitor.lisboa@lneg.pt
Get access Rights & Permissions [Opens in a new window]

Abstract

The aim of the present study was to discriminate between distinct types of clay units by applying multivariate statistical techniques, which have seldom been applied to the exploitation of ceramic clays. At the outcrop scale, texturally similar argillaceous or clayey layers of different ceramic types cannot be effectively distinguished, which can result in the misuse and loss of raw materials. Representative samples of clayey raw materials from central Portugal Cenozoic deposits with potential use in the manufacture of structural clay products were first assessed for granulometric, mineralogical, chemical, and technological properties. Based on those properties and the use of multivariate statistical techniques, i.e., factor analysis (FA) and cluster analysis (CA), a novel statistical approach that combined all these variable properties was produced. This approach made it possible to distinguish the ceramic suitability and perceive which parameters most influence that suitability. The use of R-mode FA made it feasible to differentiate and group samples based on the most influential variables: the contents of Al2O3, Fe, illite, quartz, feldspars, and K2O. The use of R-mode CA substantiated the FA results in the identification of influential variables, such as Al2O3, Fe, and illite. The use of Q-mode CA established two main clusters: clayey-silt samples and sandy and/or feldspathic samples, the clayey-silt samples encompassed three sub-clusters. These three sub-clusters match ceramic types with different suitabilities and relate sample stratigraphic setting to the encompassing stratigraphic units. Diagrams that relate the grain size, the content of different oxides, the content of different minerals, and the plasticity to the ceramic suitability illustrate the CA groupings. An adequate blend of sand and clay for red stoneware (bricks and tiles) manufacture was indicated as a major requirement for most raw materials of the clayey-silt cluster. Raw materials represented by the sandy and/or feldspathic cluster can either be used to blend with materials that lack sand or to blend with excessively plastic samples.

Keywords

Ceramic BehaviorClayCluster AnalysisFactor AnalysisMondego PlanationStructural Ceramics
Type
Article
Information
Clays and Clay Minerals , Volume 64 , Issue 6 , December 2016 , pp. 767 - 787
Copyright
Copyright © Clay Minerals Society 2016

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This paper is published as part of a special section on the subject of ‘Developments and applications of quantitative analysis to clay-bearing materials, incorporating The Reynolds Cup School’, arising out of presentations made during the 2015 Clay Minerals Society-Euroclay Conference held in Edinburgh, UK.

References

Agha, M. Ferrell, R.E. and Hart, G.F., 2012 Mineralogy of Egyptian bentonitic clays I: discriminant function analysis Clays and Clay Minerals 60 387–404.CrossRefGoogle Scholar
ASTM C 326-82, 1997 Standard Test Method for Drying and Firing Shrinkages of Ceramic Whiteware Clays Pennsylvania, USA American Society for Testing and Materials, ASTM International, West Conshohocken.Google Scholar
ASTM C 371-09, 2014 Standard Test Method for Wire-cloth Sieve Analysis of Nonplastic Ceramic Powders Pennsylvania, USA American Society for Testing and Materials, ASTM International, West Conshohocken.Google Scholar
ASTM C 373-88, 1999 Water Absorption, Bulk Density, Apparent Porosity, and Apparent Specific Gravity of Fired Whiteware Products Pennsylvania, USA American Society for Testing and Materials, ASTM International, West Conshohocken.Google Scholar
ASTM C 674-89, 1999 Standard Test Methods for Flexural Properties of Ceramic Whiteware Material Pennsylvania, USA American Society for Testing and Materials, ASTM International, West Conshohocken.Google Scholar
ASTM C 689-93, 1997 Standard Test Method for Modulus of Rupture of Unfired Clays Pennsylvania, USA American Society for Testing and Materials, ASTM International, West Conshohocken.Google Scholar
ASTM D 4318-10, 2010 Standard Test Methods for Liquid Limit, Plastic Limit, and Plasticity Index of Soils Pennsylvania, USA American Society for Testing and Materials, ASTM International, West Conshohocken.Google Scholar
ASTM D 4972-13, 2013 Standard Test Method for pH of Soils Pennsylvania, USA American Society for Testing and Materials, ASTM International, West Conshohocken.Google Scholar
Bain, J.A. and Highley, D.E., 1979 Regional appraisal of clay resources — a challenge to the clay mineralogist. In Proceedings of the 6th International Clay Conference, July 1978 Developments in Sedimentology 27 437–446.CrossRefGoogle Scholar
Blazek, A., 1972 Thermal Analysis London Van Nostrand Reinhold Company.Google Scholar
Braekmans, D. Degryse, P. Poblome, J. Neyta, B. Vyncke, K. and Waelkens, M., 2011 Understanding ceramic variability: an archaeometrical interpretation of the Classical and Hellenistic ceramics at Dúzen Tepe and Sagalassos (Southwest Turkey) Journal of Archaeological Science 38 2101–2115.CrossRefGoogle Scholar
Brindley, G.W. and Brown, G., 1980 Crystal Structures of Clay Minerals and Their X-ray Identification London Mineralogical Society.CrossRefGoogle Scholar
Bruguera, J., 1985.Manual Práctico de CerámicaGoogle Scholar
Bundy, W.M. Johns, W.D. and Murray, H.H., 1966 Interrelationships of physical and chemical properties of kaolinites Clays and Clay Minerals 14 331–346.CrossRefGoogle Scholar
Caputo, H.P. (1998) Mecânica dos Solos e Suas Aplicações. Vol. 1 and 2, 6th ed. Livros Técnicos e Científicos, Editora S.A., Rio de Janeiro.Google Scholar
Castaing, P., 1973 Remarques sur l’utilisation de l’analyse factorielle en sedimentologie Bulletin de l’Institut de Géologie du Bassin d’Aquitaine 13 53–85.Google Scholar
Cattell, R.B., 1966 The scree-test for the number of factors Multivariate Behaviour Research 1 245–276.CrossRefGoogle ScholarPubMed
Child, D., 1970 The Essentials of Factor Analysis London Holt, Rinehart and Winston.Google Scholar
CIE, 1978 Recommendations on Uniform Color Space, Color-difference Equations and Psychometric Color Terms Paris Commission Internationale de l’Eclairage.Google Scholar
Cravero, F. Marfil, S.A. and Maiza, P.J., 2010 Statistical analysis of geochemical data: a tool for discriminating between kaolin deposits of hypogene and supergene origin, Patagonia, Argentina Clay Minerals 45 183–196.CrossRefGoogle Scholar
Cunha, P.R.P., 1992 Estratigrafia e Sedimentologia dos Depósitos do Cretácico Superior e Terciário de Portugal Central, a leste de Coimbra Coimbra, Portugal PhD thesis, Univ. Coimbra 263.Google Scholar
Cunha, P.R.P., 1999 Unidades litostratigráficas do Terciário na região de Miranda do Corvo-Viseu (Bacia do Mondego, Portugal) Comunicações do Instituto Geológico e Mineiro 86 143–196.Google Scholar
Cunha, P.R.P., 2000 Litostratigrafia do Terciário da região de Miranda do Corvo — Viseu (Bacia do Mondego, Portugal) I Congresso sobre o Cenozóico de Portugal Monte da Caparica Faculdade de Ciências e Tecnologia, (UNL) 107–122.Google Scholar
Cunha, P.R.P. and Reis, R.B.P., 1991.Proposta de definiçãa formal de unidades litostratigraáficas no registo arcósico, paleogénico e miocénico, do bordo NE da Bacia Lusitaniana — região a NE de Coimbra 3° Congresso Nacional de Geologia (Resumos)Google Scholar
Davis, J.C., 1986 Statistics and Data Analysis in Geology New York Wiley.Google Scholar
Decleer, J. Ottenburgs, R. Vandenberghe, N. Viaene, W., Van Olphen, H. and Veniale, F., 1981 Geological and physico-chemical characterization of Belgian non-refractory clay deposits and its implications for industrial use Proceedings of the International Clay Conference Amsterdam Elsevier 699–709.Google Scholar
Dondi, M., 1999 Clay materials for ceramic tiles from the Sassuolo District (Northern Apennines, Italy) Geology, composition and technological properties. Applied Clay Science 15 337–366.Google Scholar
Everitt, B., 1977 Cluster Analysis London Heinemann Educational Books, Ltd..Google Scholar
Fabbri, B. and Dondi, M., 1995 Caratteristiche e Difetti del Laterizio Faenza Gruppo Editoriale Faenza Editrice.Google Scholar
Fabbri, B. and Fiori, C., 1985 Clays and complementary raw materials for stoneware tiles Mineralogica et Petrographica Acta 29A 535–545.Google Scholar
Fiori, C. Fabbri, B. Donati, F. and Venturi, I., 1989 Mineralogical composition of the clay bodies used in the Italian tile industry Applied Clay Science 4 461–473.CrossRefGoogle Scholar
Galan, E. Aparicio, P. Gonzalez, I. and Miras, A., 1998 Contribution of multivariate analysis to the correlation of some properties of kaolin with its mineralogical and chemical composition Clay Minerals 33 65–75.CrossRefGoogle Scholar
Galhano, C. Rocha, F. and Gomes, C., 1999 Geostatistical analysis of the influence of textural, mineralogical and geochemical parameters on the geotechnical behaviour of the “Argilas de Aveiro” formation (Portugal) Clay Minerals 34 109–116.CrossRefGoogle Scholar
Gorsuch, R.L., 1974 Factor Analysis Philadelphia W.B. Saunders Company.Google Scholar
Gorsuch, R.L., 1983 Factor Analysis Hillside, New Jersey Lawrence Erlbaum Associates.Google Scholar
Jouenne, C.A., 1975 Traité de Céramiques et Matériaux Mineraux Paris Editions Septima.Google Scholar
Kaiser, H.F., 1958 The varimax criteria for analytical rotation in factor analysis Psychometrika 23 187–200.CrossRefGoogle Scholar
Kowalkowski, T. Zbytniewski, R. Szpejna, J. and Buszewski, B., 2006 Application chemometrics in river water classification Water Research 40 744–752.CrossRefGoogle ScholarPubMed
Kramar, S. Lux, J. Mladenović, A. Pristacz, H. Mirtic, B. Sagadin, M. and Rogan-Ćmuc, N., 2012 Mineralogical and geochemical characteristics of Roman pottery from an archaeological site near Mos.nje (Slovenia) Applied Clay Science 57 39–48.CrossRefGoogle Scholar
Kumru, M.N. and Bakaç, M., 2003 R-mode factor analysis applied to the distribution of elements in soils from the Ayd.n basin, Turkey Journal of Geochemical Exploration 77 81–91.CrossRefGoogle Scholar
Lisboa, J.V., 2009 Matérias-primas da Plataforma do Mondego para Cerâmica Portugal PhD thesis, Univ. Aveiro, Aveiro 247.Google Scholar
Lisboa, J.V., Dinis, P. Gomes, A. and Monteiro-Rodrigues, S., 2014 Argilas comuns em Portugal Continental: ocorrência e caraterísticas Proveniência de Materiais Geológicos: Abordagens Sobre o Quaternário de Portugal Portugal APEQ, Coimbra 135–164.Google Scholar
Lisboa, J.V. Carvalho, J. Cunha, P.R.P. and Oliveira, A., 2013 Typological classification of clayey raw materials for ceramics manufacture, in the Tábua region (central Portugal) Bulletin of Engineering Geology and the Environment 72 225–232.CrossRefGoogle Scholar
Lisboa, J.V. Oliveira, D.P.S. Rocha, F. Oliveira, A. and Carvalho, J., 2015 Patterns of rare earth and other trace elements in Paleogene and Miocene clayey sediments from the Mondego platform (Central Portugal) Chemie der Erde 75 389–401.CrossRefGoogle Scholar
Mackenzie, R.C., 1957 The Differential Thermal Investigation of Clays London Mineralogical Society.Google Scholar
Mackenzie, R.C., 1962 Differential Thermal Analysis Data Index (with mineral, inorganic and organic sections) London Cleaver-Hume Press.Google Scholar
Maritan, L. Holakooei, P. and Mazzoli, C., 2015 Cluster analysis of XRPD data in ancient ceramics: What for? Applied Clay Science 114 540–549.CrossRefGoogle Scholar
Marques, R. Dias, M.I. Prudêncio, M.I. and Rocha, F., 2011 Upper Cretaceous clayey levels from western Portugal (Aveiro and Taveiro regions): clay mineral and traceelements distribution Clays and Clay Minerals 59 315–327.CrossRefGoogle Scholar
Martins, R.V.S., 2007 Investigaçãa Científica e Tecnológica de Matérias-primas Minerais de Santiago do Cacém (Alentejo) e das suas Potencialidades para a Indústria Cerâmica Aveiro, Portugal PhD thesis, Univ. Aveiro 457.Google Scholar
Mazzoleni, P. and Summa, V., 1996 Compositional characteristics of Plio-Pleistocene clays from Tricarico (Potenza, Southern Italy) and their utilization by the Italian tile industry Applied Clay Science 11 251–268.CrossRefGoogle Scholar
Montana, G. Ontiveros, M.A.C. Polito, A.M. and Azzaro, E., 2011 Characterisation of clayey raw materials for ceramic manufacture in ancient Sicily Applied Clay Science 53 476–488.CrossRefGoogle Scholar
Oliveira, J.M.S. Moura, A.C. and Grade, J., 1980 Argilas especiais da região de Barracão-Pombal: aplicação da análise matemática multivariada ao seu estudo e caracterização Comunicações dos Serviços Geolñgicos de Portugal 66 195–208.Google Scholar
Pais, J. Cunha, P.P. Pereira, D.I. Legoinha, P. Kullberg, J.C. Dias, R. Brum, S.A. and Moura, D., 2012 The Paleogene and Neogene of Western Iberia (Portugal) Heidelberg Springer, Berlin.CrossRefGoogle Scholar
Pais, J. Cunha, P.P. Legoinha, P. Dias, R.P. Pereira, D.I. Ramos, A., Dias, R. Araújo, A. Terrinha, P. and Kullberg, J.C., 2013 Cenozóico das Bacias do Douro (sector ocidental), Mondego, Baixo Tejo e Alvalade Geologia de Portugal: Vol. II — Geologia Mesocenozóica de Portugal Lisbon Escolar Editora 461–532.Google Scholar
Papatheodorou, G. Demopouloua, G. and Lambrakis, N., 2006 A long-term study of temporal hydrochemical data in a shallow lake using multivariate statistical techniques Ecological Modelling 193 759–776.CrossRefGoogle Scholar
Reimann, C. and Filzmoser, P., 2000 Normal and lognormal data distribution in geochemistry: death of a myth Consequences for the statistical treatment of geochemical and environmental data. Environmental Geology 39 1001–1014.Google Scholar
Rollinson, H., 1993 Using Geochemical Data: Evaluation, Presentation and Interpretation Wiley, New York Longman Scientific and Technical..Google Scholar
Santos, P.S. (1975) Tecnologia de Argilas. Vol. 1-Fundamentos and Vol. 2-Aplicações (Blucher, E., editor), University of São Paulo, São Paulo, Brazil.Google Scholar
Schultz, L.G., 1964 Quantitative interpretation of mineralogical composition from X-ray and chemical data for the Pierre Shale U.S. Geological Survey Professional Paper 391-C 1–31.Google Scholar
Sequeira, A.D. Cunha, P.P. and Sousa, M.B., 1997 A reactivação de falhas no intenso contexto compressivo desde meados do Tortoniano, na região de Espinhal-Coja-Caramulo (Portugal Central) Comunicaçõs do Instituto Geológico e Mineiro 83 95–126.Google Scholar
Singer, F. and Singer, S., 1963 Industrial Ceramics, vol. 1 and 2 London Chapman & Hall, Ltd..CrossRefGoogle Scholar
Singh, K.P. Malik, A. Mohan, D. and Sinha, S., 2004 Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)-a case study Water Research 38 3980–3992.CrossRefGoogle Scholar
Shepard, F.P., 1954 Nomenclature based on sand-silt-clay ratios Journal of Sedimentary Petrology 24 151–158.Google Scholar
Soares, A.F. Marques, J.F. and Sequeira, A.D., 2007 Notícia Explicativa da Folha 19-D — Lousã Lisbon Geology Department, INETI.Google Scholar
Statsoft, 2001 STATISTICA System Reference 2300 East 14th Street, Tulsa, OK 74104 USA Statsoft, Inc..Google Scholar
Stevens, J., 1986 Applied Multivariate Statistics for the Social Sciences New Jersey, USA Lawrence Erlbaum Associates, Hillsdale.Google Scholar
Strazzera, B. Dondi, M. and Marsigli, M., 1997 Composition and ceramic properties of Tertiary clays from southern Sardinia (Italy) Applied Clay Science 12 247–266.CrossRefGoogle Scholar
Thorez, J., 1976 Practical Identification of Clay Minerals Belgium Editions G. Lelotte.Google Scholar
Ward, J.H., 1963 Hierarchical grouping to optimize an objective function Journal of the American Statistical Association 58 236–244.CrossRefGoogle Scholar
Winkler, H.G.F., 1954 Bedeutung der korngróbenverteilung und des mineralbestandes von tonen fiir die herstellung grobkeramischer erzeugnisse Berichte der Deutschen Keramischen Gesellschaft 31 337–343.Google Scholar
Zhu, J. Shan, J. Qiu, P. Qin, Y. Wang, C. He, D. Sun, B. Tong, P. and Wu, S., 2004 The multivariate statistical analysis and XRD analysis of pottery at Xigongqiao site Journal of Archaeological Science 31 1685–1691.CrossRefGoogle Scholar
Zhou, F. Liu, Y. and Guo, H., 2007 Application of multivariate statistical methods to water quality assessment of the watercourses in northwestern New Territories, Hong Kong Environmental Monitoring Assessment 132 1–13.CrossRefGoogle ScholarPubMed
Zhou, J. Ma, D. Pan, J. Nie, W. and Wu, K., 2008 Application of multivariate statistical approach to identify heavy metal sources in sediment and waters: a case study in Yangzhong, China Environmental Geology 54 373–380.CrossRefGoogle Scholar
Zorski, T. Ossowski, A. Środoń, J. and Kawiak, T., 2011 Evaluation of mineral composition and petrophysical parameters by the integration of core analysis data and wireline well log data: the Carpathian Foredeep case study Clay Minerals 46 25–45.CrossRefGoogle Scholar