BibTex RIS Cite
Year 2020, , 1 - 6, 26.03.2020
https://doi.org/10.17350/HJSE19030000165

Abstract

References

  • 1. Akaike, H, 1974, A new look at the statistical model identification, IEEE Transaction on Automatic Control 19, 716–723.
  • 2. Bezerra, M.A., Santelli, R.E., Oliveira, E.P., Villar, L.S., Escaleira, L.A.,2008, Response surface methodology (RSM) as a tool for optimization in analytical chemistry, Talanta 76, 965–977.
  • 3. Box, G. E., Hunter, J. S., 1961, The 2k‐p fractional factorial designs. Technometrics, 3, 311– 351.
  • 4. Ciullo P.A., 1996, Industrial Minerals and Their Uses, A Handbook & Formulary, Noyes Publication, Westwood New Jersey, 125-136.
  • 5. Conceição, Petter & Sampaio, 2018, Prediction of water-based paint properties based on their mineral fillers; Simplex-PLSR coupling application, 3-5.
  • 6. Dattalo, P, 2013, Analysis of Multiple Dependent Variables. Oxford University, Oxford.
  • 7. DPT, (2001), Particular Industrial Minerals, Sub-commission Soil-based Industrial Raw Materials I. Special Commission Report, Ankara.
  • 8. Grim, R., 1968, Clay mineralogy, McGraw-Hill Book Company, New York, 596.
  • 9. Gündüz, G., 2005, Paint information. TMMOB Chamber of Chemical Engineers, p.461.
  • 10. Karakaş, F., 2011, Functioning Mechanism of Industrial Minerals in Water-based Paints, Ph.D. Thesis Istanbul Technical University, Institute of Science, Mining Engineering Dept., p.185, Istanbul Turkey.
  • 11. Menezes, R. R., Malzac Neto H. G., Santana, L. N. L., Lira, H. L., Ferreira, H. S. and Neves, G. A., 2008, Optimization of wastes content in ceramic tiles using statistical design of mixture experiments, Journal of the European Ceramic Society, 28, 3027– 3039.
  • 12. Özdamar, K, 2004, Statistical Data Analysis with Package Programs, Kaan Publishes, Eskisehir Turkey.
  • 13. Paksoy S., 1999, Paint Handbook, TMMOB Chamber of Chemical Engineers, Istanbul.
  • 14. Srivastava, M., Khatri, C, 1979, An Introduction to Multivariate Statistics. North Holland, New York, USA.
  • 15. Wold, H.,1973, “Nonlinear Iterative Partial Least Squares (NIPALS) modelling: Some current developments.” In Multivariate Analysis III. Proceedings of the 3rd International Symposium on Multivariate Analysis. Dayton, Ohio, edited by P. R. Krishnaiah, 383-407. Academic Press.
  • 16. Wold, S., Sjöström, M., Eriksson, L., 2001, Chemometrics and Intelligent Laboratory Systems 58, 109–130.
  • 17. Yekeler, M., Ulusoy, U., Hiçyılmaz, C., 2004, Effect of particle shape and roughness of talc mineral ground by different mills on the wettability and floatability, Powder Technology, 140, 68-78.
  • 18. Yürekli, Ş., 1997, Resine and Paint Technology, Istanbul.

The Influence of Grinding Parameters of Talc on Water-Based Paint Properties: Application of Multivariate Regression Analysis

Year 2020, , 1 - 6, 26.03.2020
https://doi.org/10.17350/HJSE19030000165

Abstract

İn this study, the influence of grinding parameters of talc sample in the conventional ball mill on water-based paint properties was investigated, and the results obtained from the experiments were statistically modeled. The regression analysis were designed to reveal the correlation between grinding parameters of the talc and the opacity and brightness of the paint with the recipes containing prepared mineral. In multivariate regression analysis, the differential grinding parameters were used to determine the change on opacity and brightness of the paint with a linear model between the change of the grinding parameters as the variables. Therefore, developed analysis includes a numerical model which could foresee the changes on final paint properties due to parameter changes ball charge, material charge and time in the grinding process. At the end of the experimental studies, the results indicated that the changes on brightness and opacity of a water-based paint are very dependant to the characteristics of talc mineral used as a filler in the same recipe. In other words, it was possible to foresee the changes on opacity and brightness of the paint due to changing grinding parameters of talc used as mineral filler in paint by using multivariate multiple regression analysis

References

  • 1. Akaike, H, 1974, A new look at the statistical model identification, IEEE Transaction on Automatic Control 19, 716–723.
  • 2. Bezerra, M.A., Santelli, R.E., Oliveira, E.P., Villar, L.S., Escaleira, L.A.,2008, Response surface methodology (RSM) as a tool for optimization in analytical chemistry, Talanta 76, 965–977.
  • 3. Box, G. E., Hunter, J. S., 1961, The 2k‐p fractional factorial designs. Technometrics, 3, 311– 351.
  • 4. Ciullo P.A., 1996, Industrial Minerals and Their Uses, A Handbook & Formulary, Noyes Publication, Westwood New Jersey, 125-136.
  • 5. Conceição, Petter & Sampaio, 2018, Prediction of water-based paint properties based on their mineral fillers; Simplex-PLSR coupling application, 3-5.
  • 6. Dattalo, P, 2013, Analysis of Multiple Dependent Variables. Oxford University, Oxford.
  • 7. DPT, (2001), Particular Industrial Minerals, Sub-commission Soil-based Industrial Raw Materials I. Special Commission Report, Ankara.
  • 8. Grim, R., 1968, Clay mineralogy, McGraw-Hill Book Company, New York, 596.
  • 9. Gündüz, G., 2005, Paint information. TMMOB Chamber of Chemical Engineers, p.461.
  • 10. Karakaş, F., 2011, Functioning Mechanism of Industrial Minerals in Water-based Paints, Ph.D. Thesis Istanbul Technical University, Institute of Science, Mining Engineering Dept., p.185, Istanbul Turkey.
  • 11. Menezes, R. R., Malzac Neto H. G., Santana, L. N. L., Lira, H. L., Ferreira, H. S. and Neves, G. A., 2008, Optimization of wastes content in ceramic tiles using statistical design of mixture experiments, Journal of the European Ceramic Society, 28, 3027– 3039.
  • 12. Özdamar, K, 2004, Statistical Data Analysis with Package Programs, Kaan Publishes, Eskisehir Turkey.
  • 13. Paksoy S., 1999, Paint Handbook, TMMOB Chamber of Chemical Engineers, Istanbul.
  • 14. Srivastava, M., Khatri, C, 1979, An Introduction to Multivariate Statistics. North Holland, New York, USA.
  • 15. Wold, H.,1973, “Nonlinear Iterative Partial Least Squares (NIPALS) modelling: Some current developments.” In Multivariate Analysis III. Proceedings of the 3rd International Symposium on Multivariate Analysis. Dayton, Ohio, edited by P. R. Krishnaiah, 383-407. Academic Press.
  • 16. Wold, S., Sjöström, M., Eriksson, L., 2001, Chemometrics and Intelligent Laboratory Systems 58, 109–130.
  • 17. Yekeler, M., Ulusoy, U., Hiçyılmaz, C., 2004, Effect of particle shape and roughness of talc mineral ground by different mills on the wettability and floatability, Powder Technology, 140, 68-78.
  • 18. Yürekli, Ş., 1997, Resine and Paint Technology, Istanbul.
There are 18 citations in total.

Details

Primary Language English
Journal Section Research Article
Authors

Murat Muduroglu This is me

Muhammed Fatih Can This is me

Baris Ergul

Publication Date March 26, 2020
Published in Issue Year 2020

Cite

Vancouver Muduroglu M, Can MF, Ergul B. The Influence of Grinding Parameters of Talc on Water-Based Paint Properties: Application of Multivariate Regression Analysis. Hittite J Sci Eng. 2020;7(1):1-6.

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