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

  • Murat Muduroglu Afyon Kocatepe University, Mining Engineering Department, Afyon, Turkey
  • Muhammed Fatih Can Afyon Kocatepe University, Mining Engineering Department, Afyon, Turkey http://orcid.org/0000-0001-6527-1336
  • Baris Ergul Eskisehir Osmangazi University, Statistics Deparment, Eskisehir, Turkey http://orcid.org/0000-0002-1811-5143

Abstract

In this study, the influence of grinding parameters of talc sample in the conventional ballmill 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.

Keywords:

Talc, Grinding, Grinding parameters, Paint, Opacity (OP), Brightness (BR), Multivariate regression analysis (MRA), Modeling.

DOI: 10.17350/HJSE19030000165

Full Text: page_white_acrobat.png

Downloads

Download data is not yet available.

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.
Published
2020-03-26
How to Cite
Muduroglu, M., Can, M., & Ergul, B. (2020). The Influence of Grinding Parameters of Talc on Water-Based Paint Properties: Application of Multivariate Regression Analysis. Hittite Journal of Science & Engineering, 7(1), 01-06. Retrieved from https://www.hjse.hitit.edu.tr/hjse/index.php/HJSE/article/view/392
Section
ENGINEERING