Advanced Optimization of Heavy Clay Products Quality by Using Artificial Neural Network Mode
Конференцијски прилог (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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, couple...d 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.
Кључне речи:
Heavy clay products / Prediction / OptimizationИзвор:
Serbian Ceramic Society Conference - ADVANCED CERAMICS AND APPLICATION III: Program and the Book of Abstracts, 2014, 82-Издавач:
- Belgrade : Serbian Ceramic Society
Институција/група
Institut za ispitivanje materijalaTY - CONF AU - Arsenović, Milica AU - Pezo, Lato AU - Mančić, Lidija AU - Radojević, Zagorka PY - 2014 UR - http://rims.institutims.rs/handle/123456789/690 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 - Serbian Ceramic Society Conference - ADVANCED CERAMICS AND APPLICATION III: Program and the Book of Abstracts T1 - Advanced Optimization of Heavy Clay Products Quality by Using Artificial Neural Network Mode SP - 82 UR - https://hdl.handle.net/21.15107/rcub_rims_690 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 = "Serbian Ceramic Society Conference - ADVANCED CERAMICS AND APPLICATION III: Program and the Book of Abstracts", title = "Advanced Optimization of Heavy Clay Products Quality by Using Artificial Neural Network Mode", pages = "82", url = "https://hdl.handle.net/21.15107/rcub_rims_690" }
Arsenović, M., Pezo, L., Mančić, L.,& Radojević, Z.. (2014). Advanced Optimization of Heavy Clay Products Quality by Using Artificial Neural Network Mode. in Serbian Ceramic Society Conference - ADVANCED CERAMICS AND APPLICATION III: Program and the Book of Abstracts Belgrade : Serbian Ceramic Society., 82. https://hdl.handle.net/21.15107/rcub_rims_690
Arsenović M, Pezo L, Mančić L, Radojević Z. Advanced Optimization of Heavy Clay Products Quality by Using Artificial Neural Network Mode. in Serbian Ceramic Society Conference - ADVANCED CERAMICS AND APPLICATION III: Program and the Book of Abstracts. 2014;:82. https://hdl.handle.net/21.15107/rcub_rims_690 .
Arsenović, Milica, Pezo, Lato, Mančić, Lidija, Radojević, Zagorka, "Advanced Optimization of Heavy Clay Products Quality by Using Artificial Neural Network Mode" in Serbian Ceramic Society Conference - ADVANCED CERAMICS AND APPLICATION III: Program and the Book of Abstracts (2014):82, https://hdl.handle.net/21.15107/rcub_rims_690 .