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.
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 . .
