ANN model of brick properties using LPNORM calculation of minerals content
Само за регистроване кориснике
2014
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Mineralogical composition of heavy clays is one of the most important properties when stadying raw materials in brick industry. Within this study, quantitative determination of minerals using LPNORM calculation was performed, using the first algorithm among the so-called norms that allows the introduction of a list of minerals and their configuration. This algorithm is implemented for the first time in practice, in order to calculate the minerals content in brick raw materials. The influence of minerals quantity, along with the firing temperature (800-1100 degrees C), and several shape formats of laboratory brick samples were investigated, and the acquired data were used to build Artificial Neural Network (ANN) model. ANN model was developed in order to predict the final products parameters, and its results have been afterwards compared to experimental data. ANN model, coupled with sensitivity analysis, was obtained with high prediction accuracy, according to coefficient of determinati...on, r(2): 0.880-0.884 in compressive strength calculation, 0.954-0.960 for water absorption, 0.869 for firing shrinkage, 0.979-0.984 for water loss during firing and 0.907 for volume mass of cubes model.
Кључне речи:
Sensitivity analysis / Mineral content / Heavy Clay / Brick quality / Artificial neural networksИзвор:
Ceramics International, 2014, 40, 7, 9637-9645Издавач:
- Elsevier Sci Ltd, Oxford
Финансирање / пројекти:
- Развој и примена мултифункционалних материјала на бази домаћих сировина модернизацијом традиционалних технологија (RS-45008)
- Осмотска дехидратација хране - енергетски и еколошки аспекти одрживе производње (RS-31055)
DOI: 10.1016/j.ceramint.2014.02.044
ISSN: 0272-8842
WoS: 000337015200087
Scopus: 2-s2.0-84900501493
Институција/група
Institut za ispitivanje materijalaTY - JOUR AU - Pezo, Lato AU - Arsenović, Milica AU - Radojević, Zagorka PY - 2014 UR - http://rims.institutims.rs/handle/123456789/241 AB - Mineralogical composition of heavy clays is one of the most important properties when stadying raw materials in brick industry. Within this study, quantitative determination of minerals using LPNORM calculation was performed, using the first algorithm among the so-called norms that allows the introduction of a list of minerals and their configuration. This algorithm is implemented for the first time in practice, in order to calculate the minerals content in brick raw materials. The influence of minerals quantity, along with the firing temperature (800-1100 degrees C), and several shape formats of laboratory brick samples were investigated, and the acquired data were used to build Artificial Neural Network (ANN) model. ANN model was developed in order to predict the final products parameters, and its results have been afterwards compared to experimental data. ANN model, coupled with sensitivity analysis, was obtained with high prediction accuracy, according to coefficient of determination, r(2): 0.880-0.884 in compressive strength calculation, 0.954-0.960 for water absorption, 0.869 for firing shrinkage, 0.979-0.984 for water loss during firing and 0.907 for volume mass of cubes model. PB - Elsevier Sci Ltd, Oxford T2 - Ceramics International T1 - ANN model of brick properties using LPNORM calculation of minerals content EP - 9645 IS - 7 SP - 9637 VL - 40 DO - 10.1016/j.ceramint.2014.02.044 ER -
@article{ author = "Pezo, Lato and Arsenović, Milica and Radojević, Zagorka", year = "2014", abstract = "Mineralogical composition of heavy clays is one of the most important properties when stadying raw materials in brick industry. Within this study, quantitative determination of minerals using LPNORM calculation was performed, using the first algorithm among the so-called norms that allows the introduction of a list of minerals and their configuration. This algorithm is implemented for the first time in practice, in order to calculate the minerals content in brick raw materials. The influence of minerals quantity, along with the firing temperature (800-1100 degrees C), and several shape formats of laboratory brick samples were investigated, and the acquired data were used to build Artificial Neural Network (ANN) model. ANN model was developed in order to predict the final products parameters, and its results have been afterwards compared to experimental data. ANN model, coupled with sensitivity analysis, was obtained with high prediction accuracy, according to coefficient of determination, r(2): 0.880-0.884 in compressive strength calculation, 0.954-0.960 for water absorption, 0.869 for firing shrinkage, 0.979-0.984 for water loss during firing and 0.907 for volume mass of cubes model.", publisher = "Elsevier Sci Ltd, Oxford", journal = "Ceramics International", title = "ANN model of brick properties using LPNORM calculation of minerals content", pages = "9645-9637", number = "7", volume = "40", doi = "10.1016/j.ceramint.2014.02.044" }
Pezo, L., Arsenović, M.,& Radojević, Z.. (2014). ANN model of brick properties using LPNORM calculation of minerals content. in Ceramics International Elsevier Sci Ltd, Oxford., 40(7), 9637-9645. https://doi.org/10.1016/j.ceramint.2014.02.044
Pezo L, Arsenović M, Radojević Z. ANN model of brick properties using LPNORM calculation of minerals content. in Ceramics International. 2014;40(7):9637-9645. doi:10.1016/j.ceramint.2014.02.044 .
Pezo, Lato, Arsenović, Milica, Radojević, Zagorka, "ANN model of brick properties using LPNORM calculation of minerals content" in Ceramics International, 40, no. 7 (2014):9637-9645, https://doi.org/10.1016/j.ceramint.2014.02.044 . .