Using Artificial Neural Network to modeling of valuable metals bioleaching from fuel oil fly ash using Acidithiobacillus ferrooxidans

In this study, the modeling of vanadium, nickel and copper bioleaching from fuel oil ash ash using artificial neural networks was investigated. In the obtained models, the extraction percentage of metals was investigated as a function of factors such as initial pH (from 1-2.5), initial Fe2+ concentration (from 0 – 9 g/l), initial bacterial inoculation (from 1 – 10%) and process time (from 0-15 day). Three neural network models were presented to estimate the extraction percentage of metals. The propagation error method and Levenberg–Marquardt algorithm were used for training. Furthermore, trial and error method was used to determine the optimal number of neurons. One quarter of the data were used to evaluate the model and were not used for training process. The Mean Relative Errors (MRE) were obtained 5.35%, 3.07% and 2.82% for V, Ni and Cu, respectively. Also the higher 0.99 of R2 indicates the validity of the obtained models.