Comprehensive approach to the influence of frequently used secondary raw materials on clay bricks quality using mathematical modeling (a systematic review). Issue 2 (1st February 2018)

• Record Type: Journal Article
• Authors: Vasić, Milica V.; Pezo, Lato; Zdravković, Jelena; Stanković, Slavka; Radojević, Zagorka
• Is Part Of: Ceramics international. Volume 44:Issue 2(2018)
• Page Start: 1269
• Page End: 1276
• Publication Date: 2018-02-01
• Subjects: Brick clay -- Secondary raw materials -- Mathematical modeling -- Sensitivity analysis; Ceramics -- Periodicals; Céramique industrielle -- Périodiques; Ceramics; Periodicals; Electronic journals; 666
• DOI: 10.1016/j.ceramint.2017.10.191
• Languages: English
• ISSNs: 0272-8842

Abstract

Abstract: The main idea of this research was to evaluate rice and sunflower hulls, sawdust and their ashes, as additives in clay brick production using mathematical analysis. All available papers, containing the parameters of interest, were used in the study, which consisted of total 316 cases, obtained from the literature. The major oxide content of clays and mixtures, then weight percent addition and particle size ranges of secondary raw materials, and process parameters (firing temperature, soaking time and average heating rate) were selected as inputs to mathematical models. Shaping moist was the only parameter characterizing transition state of the products analyzed as an output parameter. The other parameters described the fired product quality: linear shrinkage, bulk density, water absorption, compressive and bending strength. The main goal was to find the relationships and the main influences between raw material properties, process parameters, and the quality of the obtained products and mixtures, by using mathematical tools. Statistical and mathematical analyses were applied for prediction of final product quality. Developed artificial neural network empirical models (ANNs) give a reasonable fit to experimental data and successfully predict the most of the observed output variables, showing the good prediction capabilities (coefficient of determination varied between 0.714 and 0.998). Sensitivity analysis showed that, among all the studied parameters concerning raw materials and process parameters, the dominant influence belonged to loss on ignition.