IJSEA Volume 11 Issue 2

Design of an Artificial Neural Network (BPNN) to Predict the Content of Silicon Oxide (SiO2) based on the Values of the Rock Main Oxides: Glass Factory Feed Case Study

Hamed Nazerian, Adel Shirazy, Aref Shirazi, Ardeshir Hezarkhani
10.7753/IJSEA1102.1001
keywords : artificial neural network (ANN), back propagation neural network (BPNN), silica (SiO2), mineral processing, prediction, Glass and Crystal Factory.

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Artificial neural network (ANN) is one of the practical methods for prediction in various sciences. In this study, which was carried out on Glass and Crystal Factory in Isfahan, the amount of silica purification used in industry has been investigated according to its analyses. In this discussion, according to the artificial neural network algorithm back propagation neural network (BPNN), the amount of silica (SiO2) was predicted according to rock main oxides in chemical analysis. These studies can be used as a criterion for estimating the purity for use in the factory due to the high accuracy obtained.
@artical{h1122022ijsea11021001,
Title = "Design of an Artificial Neural Network (BPNN) to Predict the Content of Silicon Oxide (SiO2) based on the Values of the Rock Main Oxides: Glass Factory Feed Case Study",
Journal ="International Journal of Science and Engineering Applications (IJSEA)",
Volume = "11",
Issue ="2",
Pages ="41 - 44",
Year = "2022",
Authors ="Hamed Nazerian, Adel Shirazy, Aref Shirazi, Ardeshir Hezarkhani"}