Prediction of mechanical and physical properties of spent bleaching earth based fired bricks: an experimental study using RSM and ANN

Abstract

In this study, Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) are used to develop models to predict compressive strength, thermal conductivity, porosity and water absorption of eco-friendly fired clay bricks containing different amounts of water, percentages of spent bleaching earth (SBE) and, firing temperatures. Water content was varied between 5 and 8 wt.%, SBE was varied in the range of 0 to 50 wt.% and, firing temperature ranges from 800 to 950 °C. The fired bricks properties were strongly influenced by the SBE content and fairing temperature as confirmed by the SEM images. The percentages of water strongly influenced the compressive strength but had less influence on the porosity and water absorption and no influence on the thermal conductivity. The statistical values for both RSM and ANN models: (coefficient of determination (R²), adjusted coefficient of determination (R² _adj), mean square error (MSE), root mean square error (RMSE) and relative percent deviation (RDP)), were used to compare the two models. The results reveal high correlation coefficients, adjusted coefficients and low root mean square errors. The models were found robust and accurate in their predictions. Based on these results, the RSM and ANN models can be applied as an effective tool to predict compressive strength, thermal conductivity, porosity, and water absorption of fired bricks. Nevertheless, the artificial neural network model showed better accuracy.

Graphical Abstract