TY  - JOUR
AU  - Vasić, Milica
AU  - Pezo, Lato
AU  - Vasić, Miloš
AU  - Mijatović, Nevenka
AU  - Mitrić, Miodrag
AU  - Radojević, Zagorka
PY  - 2022
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/13117
AB  - This study presents the 51 mixtures of ceramic clays characterized by using XRF, XRD, granulometry, and dilatometry analyses. After firing in a 1000-1250 °C range, water absorption (WA) according to EN standards by boiling in water, under vacuum, and by 24 h soaking is determined. The results indicated that there was a high and statistically significant correlation between the standard methods, but the testing under vacuum gave the highest saturation of the samples fired at 1200 °C and 1250 °C. It is determined that these illitic-kaolinitic clays can be used to produce floor ceramic tiles belonging to the BIIa group (water absorption between 3% and 6%). The study also aimed to reveal which method of WA determination is suitable to read the sintering interval from the gresification diagrams, which is compared to the beginning of sintering as read from dilatometry curves.
T2  - Boletin de la Sociedad Espanola de Ceramica y Vidrio
T1  - What is the most relevant method for water absorption determination in ceramic tiles produced by illitic-kaolinitic clays? The mystery behind the gresification diagram
VL  - 61
IS  - 3
SP  - 241
EP  - 251
DO  - 10.1016/j.bsecv.2020.11.006
ER  - 

@article{
author = "Vasić, Milica and Pezo, Lato and Vasić, Miloš and Mijatović, Nevenka and Mitrić, Miodrag and Radojević, Zagorka",
year = "2022",
abstract = "This study presents the 51 mixtures of ceramic clays characterized by using XRF, XRD, granulometry, and dilatometry analyses. After firing in a 1000-1250 °C range, water absorption (WA) according to EN standards by boiling in water, under vacuum, and by 24 h soaking is determined. The results indicated that there was a high and statistically significant correlation between the standard methods, but the testing under vacuum gave the highest saturation of the samples fired at 1200 °C and 1250 °C. It is determined that these illitic-kaolinitic clays can be used to produce floor ceramic tiles belonging to the BIIa group (water absorption between 3% and 6%). The study also aimed to reveal which method of WA determination is suitable to read the sintering interval from the gresification diagrams, which is compared to the beginning of sintering as read from dilatometry curves.",
journal = "Boletin de la Sociedad Espanola de Ceramica y Vidrio",
title = "What is the most relevant method for water absorption determination in ceramic tiles produced by illitic-kaolinitic clays? The mystery behind the gresification diagram",
volume = "61",
number = "3",
pages = "241-251",
doi = "10.1016/j.bsecv.2020.11.006"
}

Vasić, M., Pezo, L., Vasić, M., Mijatović, N., Mitrić, M.,& Radojević, Z.. (2022). What is the most relevant method for water absorption determination in ceramic tiles produced by illitic-kaolinitic clays? The mystery behind the gresification diagram. in Boletin de la Sociedad Espanola de Ceramica y Vidrio, 61(3), 241-251.
https://doi.org/10.1016/j.bsecv.2020.11.006

Vasić M, Pezo L, Vasić M, Mijatović N, Mitrić M, Radojević Z. What is the most relevant method for water absorption determination in ceramic tiles produced by illitic-kaolinitic clays? The mystery behind the gresification diagram. in Boletin de la Sociedad Espanola de Ceramica y Vidrio. 2022;61(3):241-251.
doi:10.1016/j.bsecv.2020.11.006 .

Vasić, Milica, Pezo, Lato, Vasić, Miloš, Mijatović, Nevenka, Mitrić, Miodrag, Radojević, Zagorka, "What is the most relevant method for water absorption determination in ceramic tiles produced by illitic-kaolinitic clays? The mystery behind the gresification diagram" in Boletin de la Sociedad Espanola de Ceramica y Vidrio, 61, no. 3 (2022):241-251,
https://doi.org/10.1016/j.bsecv.2020.11.006 . .

TY  - JOUR
AU  - Terzić, Anja
AU  - Radulović, Dragan
AU  - Pezo, Milada L.
AU  - Stojanović, Jovica
AU  - Pezo, Lato
AU  - Radojević, Zagorka
AU  - Andrić, Ljubiša
PY  - 2020
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/9042
AB  - The optimal outputs of pyrophyllite mechano-chemical activation in an ultra-centrifugal mill performing under different technological conditions were determined by analytical modeling and verified via Artificial Neural Network in order to be employed in the production of cement-based binders. Cluster Analysis and Principal Component Analysis were utilized in assessment of the effect of activation process parameters on the activated pyrophyllite quality. Artificial Neural Network which performed with high prediction accuracy, i.e. 0.914 during the training period, was sufficient for precise prediction of activated pyrophyllite quality in a wide range of processing parameters. The probability of utilization of observed activation products was estimated through interrelation of technological parameters (mesh size sieve, activation period, specific energy consumption) and acquired characteristics of pyrophyllite (grain diameter, specific surface area). The optimal products singled out from each activation sequence were used as mineral additives in the mix-designs of four cement binders (cement replacement portion was 30%). Influence of activated pyrophyllite additions on the cement chemistry, mineral phase compositions and microstructures of the cement binders were monitored by instrumental techniques (DTA/TGA, XRD, SEM). Activated pyrophyllite showed characteristics of pozzolana as it slightly accelerated early stages of hydration, decreased cement hydration energy and increased the quantity of cement mineral alite at later hydration stages. Micron-sized crystalline foila characteristic for mechanically activated pyrophyllite formed micro-reinforcement within cement binder microstructure.
T2  - Construction and Building Materials
T1  - Prediction model based on artificial neural network for pyrophyllite mechano-chemical activation as an integral step in production of cement binders
VL  - 258
SP  - 119721
DO  - 10.1016/j.conbuildmat.2020.119721
ER  - 

@article{
author = "Terzić, Anja and Radulović, Dragan and Pezo, Milada L. and Stojanović, Jovica and Pezo, Lato and Radojević, Zagorka and Andrić, Ljubiša",
year = "2020",
abstract = "The optimal outputs of pyrophyllite mechano-chemical activation in an ultra-centrifugal mill performing under different technological conditions were determined by analytical modeling and verified via Artificial Neural Network in order to be employed in the production of cement-based binders. Cluster Analysis and Principal Component Analysis were utilized in assessment of the effect of activation process parameters on the activated pyrophyllite quality. Artificial Neural Network which performed with high prediction accuracy, i.e. 0.914 during the training period, was sufficient for precise prediction of activated pyrophyllite quality in a wide range of processing parameters. The probability of utilization of observed activation products was estimated through interrelation of technological parameters (mesh size sieve, activation period, specific energy consumption) and acquired characteristics of pyrophyllite (grain diameter, specific surface area). The optimal products singled out from each activation sequence were used as mineral additives in the mix-designs of four cement binders (cement replacement portion was 30%). Influence of activated pyrophyllite additions on the cement chemistry, mineral phase compositions and microstructures of the cement binders were monitored by instrumental techniques (DTA/TGA, XRD, SEM). Activated pyrophyllite showed characteristics of pozzolana as it slightly accelerated early stages of hydration, decreased cement hydration energy and increased the quantity of cement mineral alite at later hydration stages. Micron-sized crystalline foila characteristic for mechanically activated pyrophyllite formed micro-reinforcement within cement binder microstructure.",
journal = "Construction and Building Materials",
title = "Prediction model based on artificial neural network for pyrophyllite mechano-chemical activation as an integral step in production of cement binders",
volume = "258",
pages = "119721",
doi = "10.1016/j.conbuildmat.2020.119721"
}

Terzić, A., Radulović, D., Pezo, M. L., Stojanović, J., Pezo, L., Radojević, Z.,& Andrić, L.. (2020). Prediction model based on artificial neural network for pyrophyllite mechano-chemical activation as an integral step in production of cement binders. in Construction and Building Materials, 258, 119721.
https://doi.org/10.1016/j.conbuildmat.2020.119721

Terzić A, Radulović D, Pezo ML, Stojanović J, Pezo L, Radojević Z, Andrić L. Prediction model based on artificial neural network for pyrophyllite mechano-chemical activation as an integral step in production of cement binders. in Construction and Building Materials. 2020;258:119721.
doi:10.1016/j.conbuildmat.2020.119721 .

Terzić, Anja, Radulović, Dragan, Pezo, Milada L., Stojanović, Jovica, Pezo, Lato, Radojević, Zagorka, Andrić, Ljubiša, "Prediction model based on artificial neural network for pyrophyllite mechano-chemical activation as an integral step in production of cement binders" in Construction and Building Materials, 258 (2020):119721,
https://doi.org/10.1016/j.conbuildmat.2020.119721 . .