مقایسه مدلسازی روش‌های شبکه عصبی و RSM فرایند استخراج از صفحات مدارچاپی تلفن همراه توسط عصاره لیمو

In this study, the application of bio-acid leaching method based on the use of lemon juice to extract copper and zinc metals from mobile printed circuit boards has been investigated. Three important factors were investigated include lemon juice concentration, Solid/Liquid (S/L) ratio, and hydrogen peroxide (H₂O₂) concentration. Response surface methodology (RSM) was used to optimize the effective factors. The results showed that for particles with a size of 150 to 180 μm at a constant temperature of 20 ° C and time 4 h under optimal conditions including 1.41% (w/v) S/L ratio, 12.2% (v/v) H₂O₂ and 74% (v/v) lemon juice, copper and zinc recovery efficiencies are 89% and 73%, respectively. Moreover, the artificial neural network was used to predict the extraction of copper and zinc metals as a function of the studied factors. To validate the model, laboratory results were considered as evaluation data. The results of neural network modeling showed high accuracy to predict the target variable. The values of MRE, MSE, and R² were 9.485, 15.254, and 0.9356% for the copper extraction model and 1.854%, 1.094, and 0.9963% for the zinc extraction model, respectively.