Designing a predictive model for evaluating foundry silica sands using data mining and designing experiments (Study of group 50 sands)
Volume 13, Issue 3, Autumn 2023, Pages 281-299
https://doi.org/10.48313/jqem.2023.199699
gholamhossein baghban, abbas rad, Davood Talebi, Hasan Farsijani
Abstract : Mineral industries are one of the important sectors of industry in Iran, therefore, it is necessary to improve the quality of mineral products. One of these products is foundry silica sand. The aim of this study was to create a complete model using this type of silica sand. A comprehensive analysis was done on ten mines and seven mines were selected to perform the quality improvement stage. A total of 1400 tests were conducted to achieve the main goal of the research, which was to increase the quality of silica sand parameters. It was also found that the seven basic characteristics of silica sand have a significant effect on the quality of the final products. The quality of silica sands is influenced by elements such as calcium, sodium, potassium and magnesium, which are alkaline elements of the soil. A higher percentage of silica in a mineral is usually associated with increased quality, as it ensures the achievement of ideal properties and performance in silica sands. Factors affecting the quality of silica sand were prioritized by experts using the fuzzy Delphi technique and hierarchical analysis. These factors have an effect on the chemical composition, purity, reactivity and performance of silica sands. Also, a data mining model was designed to predict the quality of these sands. The findings of this study show that the presence of calcium, sodium, potassium, magnesium, silica content, ADV (sand alkalinity or acidity) and pH affect the quality of silica sands. It is concluded that this model provides an efficient attitude and prediction to increase product quality.