Predictive modeling and response analysis of spent catalyst bioleaching using artificial neural network

Abstract

Bioleaching of spent catalyst using acidophiles usually leads to lower Mo extraction than other metals. To address this problem, Mo leaching has been examined using alkaline chemicals or other microorganisms. In this study, experimental data of Mo bioleaching from spent catalyst using Escherichia coli was collected and modeled with Artificial Neural Network (ANN). Variation in metal extraction with change in particle size, pulp density, temperature, and residence time, was examined using response modeling. ANN was trained and optimized using Limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm for varying hidden layers and different activation functions. The training was done with tenfold cross-validation to avoid overfitting on the training data. The best network configuration was identified using Coefficient of Determination (R2 score) and evaluated on a test dataset. A network with logistic activation function and hidden layer configuration with two layers was found most effective with an R2 score of 0.988.