Prediction and Optimization of Heavy Clay Products Quality
Само за регистроване кориснике
2014
Поглавље у монографији (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
The effects of chemical composition, firing temperature (800-1100 °C), and 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 aft erwards compared to experimental results. 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). An 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.
Кључне речи:
Prediction / Optimization / Heavy clay productsИзвор:
Advanced Materials for Agriculture, Food and Environmental Safety, 2014, 9781118773437, 87-120Издавач:
- Wiley Blackwell
DOI: 10.1002/9781118773857.ch4
ISBN: 978-86-915627-2-4
Scopus: 2-s2.0-84927678659
Институција/група
Institut za ispitivanje materijalaTY - CHAP AU - Arsenović, Milica AU - Pezo, Lato AU - Mančić, Lidija AU - Radojević, Zagorka PY - 2014 UR - http://rims.institutims.rs/handle/123456789/256 AB - The effects of chemical composition, firing temperature (800-1100 °C), and 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 aft erwards compared to experimental results. 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). An 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 - Wiley Blackwell T2 - Advanced Materials for Agriculture, Food and Environmental Safety T1 - Prediction and Optimization of Heavy Clay Products Quality EP - 120 SP - 87 VL - 9781118773437 DO - 10.1002/9781118773857.ch4 ER -
@inbook{ author = "Arsenović, Milica and Pezo, Lato and Mančić, Lidija and Radojević, Zagorka", year = "2014", abstract = "The effects of chemical composition, firing temperature (800-1100 °C), and 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 aft erwards compared to experimental results. 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). An 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 = "Wiley Blackwell", journal = "Advanced Materials for Agriculture, Food and Environmental Safety", booktitle = "Prediction and Optimization of Heavy Clay Products Quality", pages = "120-87", volume = "9781118773437", doi = "10.1002/9781118773857.ch4" }
Arsenović, M., Pezo, L., Mančić, L.,& Radojević, Z.. (2014). Prediction and Optimization of Heavy Clay Products Quality. in Advanced Materials for Agriculture, Food and Environmental Safety Wiley Blackwell., 9781118773437, 87-120. https://doi.org/10.1002/9781118773857.ch4
Arsenović M, Pezo L, Mančić L, Radojević Z. Prediction and Optimization of Heavy Clay Products Quality. in Advanced Materials for Agriculture, Food and Environmental Safety. 2014;9781118773437:87-120. doi:10.1002/9781118773857.ch4 .
Arsenović, Milica, Pezo, Lato, Mančić, Lidija, Radojević, Zagorka, "Prediction and Optimization of Heavy Clay Products Quality" in Advanced Materials for Agriculture, Food and Environmental Safety, 9781118773437 (2014):87-120, https://doi.org/10.1002/9781118773857.ch4 . .