TY  - JOUR
AU  - Vasić, Milica
AU  - Pezo, Lato
AU  - Gupta, Vivek
AU  - Chaudhary, Sandeep
AU  - Radojević, Zagorka
PY  - 2021
UR  - http://rims.institutims.rs/handle/123456789/404
AB  - This study analyzed the last 20 years' data available on power plant coal ashes used in clay brick production. The statistical analysis has been carried out for a total of 302 cases based on the relevant parameters reported in the literature. The chemical composition of the clays and coal ashes, percentage incorporation and maximum particle size of ash, size of fired samples, peak firing temperature, and the corresponding soaking time were selected as inputs for modeling. The product characteristics i.e. open porosity, water absorption, and compressive strength was taken as output parameters. An artificial neural network model has been developed and showed a satisfactory fit to experimental data and predicted the observed output variables with the overall coefficient of determination (r(2)) of 0.972 during the training period. Besides, the reduced chi-square, mean bias error, root mean square error, and mean percentage error were utilized to check the correctness of the obtained model, which proved the network generalization capability. The sensitivity analysis of the model suggested that the quantity of Na2O coming from brick clays, the percentages of SiO2 and K2O coming from ashes, and MgO coming from clays were the most influential parameters in descending order for the ash-clay composite bricks' quality, mostly owing to the influence of fluxes during firing.
PB  - Međunarodni Institut za nauku o sinterovanju, Beograd
T2  - Science of Sintering
T1  - An Artificial Neural Network-based Prediction Model for Utilization of Coal Ash in Production of Fired Clay Bricks: A review
EP  - 53
IS  - 1
SP  - 37
VL  - 53
DO  - 10.2298/SOS2101037V
ER  - 

@article{
author = "Vasić, Milica and Pezo, Lato and Gupta, Vivek and Chaudhary, Sandeep and Radojević, Zagorka",
year = "2021",
abstract = "This study analyzed the last 20 years' data available on power plant coal ashes used in clay brick production. The statistical analysis has been carried out for a total of 302 cases based on the relevant parameters reported in the literature. The chemical composition of the clays and coal ashes, percentage incorporation and maximum particle size of ash, size of fired samples, peak firing temperature, and the corresponding soaking time were selected as inputs for modeling. The product characteristics i.e. open porosity, water absorption, and compressive strength was taken as output parameters. An artificial neural network model has been developed and showed a satisfactory fit to experimental data and predicted the observed output variables with the overall coefficient of determination (r(2)) of 0.972 during the training period. Besides, the reduced chi-square, mean bias error, root mean square error, and mean percentage error were utilized to check the correctness of the obtained model, which proved the network generalization capability. The sensitivity analysis of the model suggested that the quantity of Na2O coming from brick clays, the percentages of SiO2 and K2O coming from ashes, and MgO coming from clays were the most influential parameters in descending order for the ash-clay composite bricks' quality, mostly owing to the influence of fluxes during firing.",
publisher = "Međunarodni Institut za nauku o sinterovanju, Beograd",
journal = "Science of Sintering",
title = "An Artificial Neural Network-based Prediction Model for Utilization of Coal Ash in Production of Fired Clay Bricks: A review",
pages = "53-37",
number = "1",
volume = "53",
doi = "10.2298/SOS2101037V"
}

Vasić, M., Pezo, L., Gupta, V., Chaudhary, S.,& Radojević, Z.. (2021). An Artificial Neural Network-based Prediction Model for Utilization of Coal Ash in Production of Fired Clay Bricks: A review. in Science of Sintering
Međunarodni Institut za nauku o sinterovanju, Beograd., 53(1), 37-53.
https://doi.org/10.2298/SOS2101037V

Vasić M, Pezo L, Gupta V, Chaudhary S, Radojević Z. An Artificial Neural Network-based Prediction Model for Utilization of Coal Ash in Production of Fired Clay Bricks: A review. in Science of Sintering. 2021;53(1):37-53.
doi:10.2298/SOS2101037V .

Vasić, Milica, Pezo, Lato, Gupta, Vivek, Chaudhary, Sandeep, Radojević, Zagorka, "An Artificial Neural Network-based Prediction Model for Utilization of Coal Ash in Production of Fired Clay Bricks: A review" in Science of Sintering, 53, no. 1 (2021):37-53,
https://doi.org/10.2298/SOS2101037V . .

TY  - CONF
AU  - Petojević, Zorana
AU  - Mitković, Predrag
AU  - Mirković, Nikola
AU  - Milovanović, Jovana
AU  - Ninić, Bojana
AU  - Mirković, Milica
AU  - Šumarac, Dragoslav
AU  - Gospavić, Radovan
AU  - Todorović, Goran
PY  - 2017
UR  - http://rims.institutims.rs/handle/123456789/433
AB  - This paper is presenting a method for temperature transfer function (TTF)
estimation by filtering an experimentally collected data. The experimental data were
obtained in simultaneous measurements of inside and outside temperatures variations
during the period of 3 months of a building located in Belgrade, Serbia. The TTF
estimation is based on Wiener filtering technique for the dynamic systems with finite
impulse response (FIR). TTF is derivate in time and complex domain and the correctness
of the acquired transfer function is tested on the new input data set. The estimated TTF in
complex domain is used to get decrement factor (DF) and time lag (TL) between the
temperatures.
PB  - Građevinski fakultet Subotica
C3  - Proceedings of the 5th International Conference, Contemporary achievements in civil engineering, Subotica 2017
T1  - Estimation of temperature transfer function in facade wall heat transport
EP  - 748
SP  - 739
DO  - 10.14415/konferencijaGFS2017.07
ER  - 

@conference{
author = "Petojević, Zorana and Mitković, Predrag and Mirković, Nikola and Milovanović, Jovana and Ninić, Bojana and Mirković, Milica and Šumarac, Dragoslav and Gospavić, Radovan and Todorović, Goran",
year = "2017",
abstract = "This paper is presenting a method for temperature transfer function (TTF)
estimation by filtering an experimentally collected data. The experimental data were
obtained in simultaneous measurements of inside and outside temperatures variations
during the period of 3 months of a building located in Belgrade, Serbia. The TTF
estimation is based on Wiener filtering technique for the dynamic systems with finite
impulse response (FIR). TTF is derivate in time and complex domain and the correctness
of the acquired transfer function is tested on the new input data set. The estimated TTF in
complex domain is used to get decrement factor (DF) and time lag (TL) between the
temperatures.",
publisher = "Građevinski fakultet Subotica",
journal = "Proceedings of the 5th International Conference, Contemporary achievements in civil engineering, Subotica 2017",
title = "Estimation of temperature transfer function in facade wall heat transport",
pages = "748-739",
doi = "10.14415/konferencijaGFS2017.07"
}

Petojević, Z., Mitković, P., Mirković, N., Milovanović, J., Ninić, B., Mirković, M., Šumarac, D., Gospavić, R.,& Todorović, G.. (2017). Estimation of temperature transfer function in facade wall heat transport. in Proceedings of the 5th International Conference, Contemporary achievements in civil engineering, Subotica 2017
Građevinski fakultet Subotica., 739-748.
https://doi.org/10.14415/konferencijaGFS2017.07

Petojević Z, Mitković P, Mirković N, Milovanović J, Ninić B, Mirković M, Šumarac D, Gospavić R, Todorović G. Estimation of temperature transfer function in facade wall heat transport. in Proceedings of the 5th International Conference, Contemporary achievements in civil engineering, Subotica 2017. 2017;:739-748.
doi:10.14415/konferencijaGFS2017.07 .

Petojević, Zorana, Mitković, Predrag, Mirković, Nikola, Milovanović, Jovana, Ninić, Bojana, Mirković, Milica, Šumarac, Dragoslav, Gospavić, Radovan, Todorović, Goran, "Estimation of temperature transfer function in facade wall heat transport" in Proceedings of the 5th International Conference, Contemporary achievements in civil engineering, Subotica 2017 (2017):739-748,
https://doi.org/10.14415/konferencijaGFS2017.07 . .