dc.description.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. | sr |