Neural extension of experimental data to investigate using phosphogypsume in light brick production

In this study, usability of wastes produced in phosphoric acid plants in structural brick manufacture has been investigated. There are several parameters involved in using these wastes in brick production namely the rate of added waste, firing speed and firing temperature. The performance of these parameters can be measured by several criteria such as natural drying shortening, water absorption and weight loss. Therefore, so many experiments are needed to investigate the effects of these parameters on the bricks produced with these wastes. The result of a series of experiments were utilized to achieve this end. The results have shown that the industrial wastes considered improve the performance of the bricks in terms of the criteria mentioned above. However, the results have also shown that further investigations are needed to explore the effects of interim values on the performance of the bricks. To achieve that end, a neural experimental study is adopted. For this purpose, the results of the experiments conducted were used to construct an artificial neural network. The trained and tested network was then used to check the effects of 280 different combinations for each type of material mixtures mentioned. The outcome of these artificial tests have provided the optimal values for the waste addition rate, firing speed and firing temperature based on the four criteria mentioned previously.