Metal recovery prediction of elements from anode slime

Abstract

Metal recovery prediction of elements remains as one of the significant problems in the metal industry due to limited application data. In this research, quasi-Newton training algorithm-based artificial neural network (ANN) was applied for estimating the recovery amount of the elements during the anode slime emerging processes. ANN models were designed with the approximation of the inverse Hessian at each iteration by using gradient information. Temperature, leaching time, solid-to-liquid ratio and ionic liquid concentration were taken as input parameters for the recovery amount estimation. The results showed that the proposed algorithm is highly efficient in predicting metal recovery amount from anode slime. As a precious one, maximum Au recovery amount is predicted by ANN as 82.11% when the solid-to-liquid ratio is 1/25, the temperature is 45 °C, ionic liquid concentration is 40% and the leaching time is 0.5 h.