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Advanced Optimization of Heavy Clay Products Quality by Using Artificial Neural Network Model

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2014
599.pdf (162.0Kb)
Authors
Arsenović, Milica
Pezo, Lato
Mančić, Lidija
Radojević, Zagorka
Conference object (Published version)
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Abstract
The effects of firing temperature (800–1100°C), chemical composition (expressed in terms of the content of major oxides - SiO2, Al2O3, Fe2O3, CaO, MgO, Na2O, K2O, MnO and TiO2), as well as several shape formats of laboratory brick samples on the final product quality were investigated. Prediction of the final laboratory products parameters was evaluated by second order polynomial regression models (SOPs) and artificial neural networks (ANNs), and afterwards both models were compared to one another and to experimental results. . Observed parameters of fired products that were determined in this study were: compressive strength (CS), water absorption (WA), firing shrinkage (FS), weight loss during firing (WLF) and volume mass of cubes (VMC). SOPs showed high r2 values (0.897 - 0.913 for compressive strength models, 0.942-0.962 for water absorption, 0.928 for firing shrinkage, 0.988-0.991 for water loss during firing and 0.941 for volume mass of cubes models). ANN model, coupled with sens...itivity analysis, was obtained with high prediction accuracy: 0.866–0.939 for compressive strength models, 0.954–0.974 for water absorption, 0.882 for firing shrinkage, 0.982-0.988 for water loss during firing and 0.920 for volume mass of cubes models. The optimal samples chemical composition and firing temperature were chosen depending on a final usage of the raw material in heavy clay brick industry.

Keywords:
heavy clay products / prediction / optimization
Source:
Advanced Ceramics and Application : new frontiers in multifunctional material science and processing : program and the book of abstracts / III Serbian Ceramic Society Conference, 29th September - 1st October, Belgrade, 2014, 2014, 82-82
Publisher:
  • Belgrade : Serbian Ceramic Society

ISBN: 9788691562724

[ Google Scholar ]
Handle
https://hdl.handle.net/21.15107/rcub_dais_602
URI
https://dais.sanu.ac.rs/123456789/602
Collections
  • ИТН САНУ - Општа колекција / ITS SASA - General collection
Institution/Community
Институт техничких наука САНУ / Institute of Technical Sciences of SASA
TY  - CONF
AU  - Arsenović, Milica
AU  - Pezo, Lato
AU  - Mančić, Lidija
AU  - Radojević, Zagorka
PY  - 2014
UR  - https://dais.sanu.ac.rs/123456789/602
AB  - The effects of firing temperature (800–1100°C), chemical composition (expressed in terms of the content of major oxides - SiO2, Al2O3, Fe2O3, CaO, MgO, Na2O, K2O, MnO and TiO2), as well as several shape formats of laboratory brick samples on the final product quality were investigated. Prediction of the final laboratory products parameters was evaluated by second order polynomial regression models (SOPs) and artificial neural networks (ANNs), and afterwards both models were compared to one another and to experimental results. . Observed parameters of fired products that were determined in this study were: compressive strength (CS), water absorption (WA), firing shrinkage (FS), weight loss during firing (WLF) and volume mass of cubes (VMC). SOPs showed high r2 values (0.897 - 0.913 for compressive strength models, 0.942-0.962 for water absorption, 0.928 for firing shrinkage, 0.988-0.991 for water loss during firing and 0.941 for volume mass of cubes models). ANN model, coupled with sensitivity analysis, was obtained with high prediction accuracy: 0.866–0.939 for compressive strength models, 0.954–0.974 for water absorption, 0.882 for firing shrinkage, 0.982-0.988 for water loss during firing and 0.920 for volume mass of cubes models. The optimal samples chemical composition and firing temperature were chosen depending on a final usage of the raw material in heavy clay brick industry.
PB  - Belgrade : Serbian Ceramic Society
C3  - Advanced Ceramics and Application : new frontiers in multifunctional material science and processing : program and the book of abstracts / III Serbian Ceramic Society Conference, 29th September - 1st October, Belgrade, 2014
T1  - Advanced Optimization of Heavy Clay Products Quality by Using Artificial Neural Network Model
SP  - 82
EP  - 82
UR  - https://hdl.handle.net/21.15107/rcub_dais_602
ER  - 
@conference{
author = "Arsenović, Milica and Pezo, Lato and Mančić, Lidija and Radojević, Zagorka",
year = "2014",
abstract = "The effects of firing temperature (800–1100°C), chemical composition (expressed in terms of the content of major oxides - SiO2, Al2O3, Fe2O3, CaO, MgO, Na2O, K2O, MnO and TiO2), as well as several shape formats of laboratory brick samples on the final product quality were investigated. Prediction of the final laboratory products parameters was evaluated by second order polynomial regression models (SOPs) and artificial neural networks (ANNs), and afterwards both models were compared to one another and to experimental results. . Observed parameters of fired products that were determined in this study were: compressive strength (CS), water absorption (WA), firing shrinkage (FS), weight loss during firing (WLF) and volume mass of cubes (VMC). SOPs showed high r2 values (0.897 - 0.913 for compressive strength models, 0.942-0.962 for water absorption, 0.928 for firing shrinkage, 0.988-0.991 for water loss during firing and 0.941 for volume mass of cubes models). ANN model, coupled with sensitivity analysis, was obtained with high prediction accuracy: 0.866–0.939 for compressive strength models, 0.954–0.974 for water absorption, 0.882 for firing shrinkage, 0.982-0.988 for water loss during firing and 0.920 for volume mass of cubes models. The optimal samples chemical composition and firing temperature were chosen depending on a final usage of the raw material in heavy clay brick industry.",
publisher = "Belgrade : Serbian Ceramic Society",
journal = "Advanced Ceramics and Application : new frontiers in multifunctional material science and processing : program and the book of abstracts / III Serbian Ceramic Society Conference, 29th September - 1st October, Belgrade, 2014",
title = "Advanced Optimization of Heavy Clay Products Quality by Using Artificial Neural Network Model",
pages = "82-82",
url = "https://hdl.handle.net/21.15107/rcub_dais_602"
}
Arsenović, M., Pezo, L., Mančić, L.,& Radojević, Z.. (2014). Advanced Optimization of Heavy Clay Products Quality by Using Artificial Neural Network Model. in Advanced Ceramics and Application : new frontiers in multifunctional material science and processing : program and the book of abstracts / III Serbian Ceramic Society Conference, 29th September - 1st October, Belgrade, 2014
Belgrade : Serbian Ceramic Society., 82-82.
https://hdl.handle.net/21.15107/rcub_dais_602
Arsenović M, Pezo L, Mančić L, Radojević Z. Advanced Optimization of Heavy Clay Products Quality by Using Artificial Neural Network Model. in Advanced Ceramics and Application : new frontiers in multifunctional material science and processing : program and the book of abstracts / III Serbian Ceramic Society Conference, 29th September - 1st October, Belgrade, 2014. 2014;:82-82.
https://hdl.handle.net/21.15107/rcub_dais_602 .
Arsenović, Milica, Pezo, Lato, Mančić, Lidija, Radojević, Zagorka, "Advanced Optimization of Heavy Clay Products Quality by Using Artificial Neural Network Model" in Advanced Ceramics and Application : new frontiers in multifunctional material science and processing : program and the book of abstracts / III Serbian Ceramic Society Conference, 29th September - 1st October, Belgrade, 2014 (2014):82-82,
https://hdl.handle.net/21.15107/rcub_dais_602 .

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