Optimizing the processing parameters of producing Al–Si alloys using sodium fluosilicate via artificial neural network

Abstract

This study investigates the optimal conditions for producing Al–Si cast alloys by reacting sodium fluorosilicate (Na₂SiF₆) with molten aluminium, employing artificial neural network via the Levenberg–Marquardt algorithm (LMA-ANN). The goal is to identify the lowest reaction times that yield the highest silicon recovery percentages at minimal stirring speeds and reaction temperatures, optimizing processing parameters and economic outcomes. Characterization techniques such as XRD and LOM confirmed the presence of α Al, and a uniform fine fibrous eutectic silicon. Differential thermal analysis (DTA) showed an exothermic peak at approximately 871 °C, indicating a multi-step reaction involving Al and Na₂SiF₆. The efficiency of silicon recovery was directly proportional to the stirring speed and temperature within the reaction time range of 15 to 30 minutes. The cascade-forward back-propagation ANN was trained to optimize silicon recovery, considering reaction time, temperature, and stirring speed. Analysis revealed that higher temperatures led to increased silicon recoveries, with significant gains at higher temperatures for all stirring speeds. The maximum silicon recovery efficiency of 92.14% was obtained with a reaction time of 25.86 minutes, a temperature of 950 °C, and a stirring speed of 600 rpm. This study highlights the effectiveness of the ANN-LMA approach in deriving optimal processing conditions for high-efficiency silicon recovery in Al–Si alloys.