Prediction of sulphur removal with Acidithiobacillus sp. using artificial neural networks

Artificial neural network (ANN) model was used to predict the extent of sulphur removal from three types of coal using native cultures of Acidithiobacillus ferrooxidans. The type of coal, initial pH, pulp density, particle size, residence time, media composition and initial sulphur content of coal were fed as input to the network. The output of the model was sulphur removal. The resulting ANN showed satisfactory prediction of sulphur removal percentages with mean absolute deviations varying from 0.003 to 0.5. A three layer feed forward neural network model consisting of an input layer, one hidden layer and an output layer was found to give satisfactory results. Although the number of data sets were limited, the parity plot shows that the model estimations for the test set was good.