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What to expect from heavy clay?

Authorized Users Only
2013
Authors
Arsenović, Milica
Radojević, Zagorka
Stanković, Slavka
Lalić, Željko
Pezo, Lato
Article (Published version)
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Abstract
The need of testing the quality of brickclay arises in all brick factories, with the opening of new deposits. The analyses are both time and economically consuming, so the aim of this study was to shorten the procedure using the already known data. This study was focused on determining the usability of heavy clays, when only the raw material major elements chemical composition is determined. The effects of chemical composition, firing temperature, and several shape formats of laboratory samples on the final properties were investigated. Chemical composition of major elements was determined on the basis of classical silicate analysis. Firing was conducted in an oxidizing atmosphere, while maintaining all other experimental conditions constant, except the final temperature. Principal component analysis (PCA) was used to determinate groups of samples according to similarity of chemical composition. Prediction of compressive strength (CS) and water absorption (WA) was done by developing fi...ve artificial neural networks (ANN). The average regression coefficients r(2) were used to explore the confidence level of the models. Developed models were able to predict CS and WA in a wide range of chemical composition and temperature treatment data, and the highest average r(2) of 0.923 for CS was obtained, while r(2) for WA was 0.958. The wide range of processing variables was considered in the model formulation, and its easy implementation in a spreadsheet using a set of equations makes it very useful and practical for CS and WA prediction. As it is known from literature, all the parameters entered this analysis are dependent on each other, but their mutual relationship was not quantified yet. Most importantly-the developed neural networks can be used on a global scale.

Keywords:
Prediction / Neural networks / Heavy clay
Source:
Ceramics International, 2013, 39, 2, 1667-1675
Publisher:
  • Elsevier Sci Ltd, Oxford
Funding / projects:
  • Development and application of multifunctional materials using domestic raw materials in upgraded processing lines (RS-45008)
  • Osmotic dehydration of food - energy and ecological aspects of sustainable production (RS-31055)

DOI: 10.1016/j.ceramint.2012.08.009

ISSN: 0272-8842

WoS: 000313379400100

Scopus: 2-s2.0-84870291621
[ Google Scholar ]
28
21
URI
http://rims.institutims.rs/handle/123456789/224
Collections
  • Radovi istraživača / Researchers' publications
Institution/Community
Institut za ispitivanje materijala
TY  - JOUR
AU  - Arsenović, Milica
AU  - Radojević, Zagorka
AU  - Stanković, Slavka
AU  - Lalić, Željko
AU  - Pezo, Lato
PY  - 2013
UR  - http://rims.institutims.rs/handle/123456789/224
AB  - The need of testing the quality of brickclay arises in all brick factories, with the opening of new deposits. The analyses are both time and economically consuming, so the aim of this study was to shorten the procedure using the already known data. This study was focused on determining the usability of heavy clays, when only the raw material major elements chemical composition is determined. The effects of chemical composition, firing temperature, and several shape formats of laboratory samples on the final properties were investigated. Chemical composition of major elements was determined on the basis of classical silicate analysis. Firing was conducted in an oxidizing atmosphere, while maintaining all other experimental conditions constant, except the final temperature. Principal component analysis (PCA) was used to determinate groups of samples according to similarity of chemical composition. Prediction of compressive strength (CS) and water absorption (WA) was done by developing five artificial neural networks (ANN). The average regression coefficients r(2) were used to explore the confidence level of the models. Developed models were able to predict CS and WA in a wide range of chemical composition and temperature treatment data, and the highest average r(2) of 0.923 for CS was obtained, while r(2) for WA was 0.958. The wide range of processing variables was considered in the model formulation, and its easy implementation in a spreadsheet using a set of equations makes it very useful and practical for CS and WA prediction. As it is known from literature, all the parameters entered this analysis are dependent on each other, but their mutual relationship was not quantified yet. Most importantly-the developed neural networks can be used on a global scale.
PB  - Elsevier Sci Ltd, Oxford
T2  - Ceramics International
T1  - What to expect from heavy clay?
EP  - 1675
IS  - 2
SP  - 1667
VL  - 39
DO  - 10.1016/j.ceramint.2012.08.009
ER  - 
@article{
author = "Arsenović, Milica and Radojević, Zagorka and Stanković, Slavka and Lalić, Željko and Pezo, Lato",
year = "2013",
abstract = "The need of testing the quality of brickclay arises in all brick factories, with the opening of new deposits. The analyses are both time and economically consuming, so the aim of this study was to shorten the procedure using the already known data. This study was focused on determining the usability of heavy clays, when only the raw material major elements chemical composition is determined. The effects of chemical composition, firing temperature, and several shape formats of laboratory samples on the final properties were investigated. Chemical composition of major elements was determined on the basis of classical silicate analysis. Firing was conducted in an oxidizing atmosphere, while maintaining all other experimental conditions constant, except the final temperature. Principal component analysis (PCA) was used to determinate groups of samples according to similarity of chemical composition. Prediction of compressive strength (CS) and water absorption (WA) was done by developing five artificial neural networks (ANN). The average regression coefficients r(2) were used to explore the confidence level of the models. Developed models were able to predict CS and WA in a wide range of chemical composition and temperature treatment data, and the highest average r(2) of 0.923 for CS was obtained, while r(2) for WA was 0.958. The wide range of processing variables was considered in the model formulation, and its easy implementation in a spreadsheet using a set of equations makes it very useful and practical for CS and WA prediction. As it is known from literature, all the parameters entered this analysis are dependent on each other, but their mutual relationship was not quantified yet. Most importantly-the developed neural networks can be used on a global scale.",
publisher = "Elsevier Sci Ltd, Oxford",
journal = "Ceramics International",
title = "What to expect from heavy clay?",
pages = "1675-1667",
number = "2",
volume = "39",
doi = "10.1016/j.ceramint.2012.08.009"
}
Arsenović, M., Radojević, Z., Stanković, S., Lalić, Ž.,& Pezo, L.. (2013). What to expect from heavy clay?. in Ceramics International
Elsevier Sci Ltd, Oxford., 39(2), 1667-1675.
https://doi.org/10.1016/j.ceramint.2012.08.009
Arsenović M, Radojević Z, Stanković S, Lalić Ž, Pezo L. What to expect from heavy clay?. in Ceramics International. 2013;39(2):1667-1675.
doi:10.1016/j.ceramint.2012.08.009 .
Arsenović, Milica, Radojević, Zagorka, Stanković, Slavka, Lalić, Željko, Pezo, Lato, "What to expect from heavy clay?" in Ceramics International, 39, no. 2 (2013):1667-1675,
https://doi.org/10.1016/j.ceramint.2012.08.009 . .

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