Machine learning models for modeling the biosorption of Fe(III) ions by activated carbon from olive stone

Using experimental results related to the biosorption of Fe(III) by activated carbon derived from olive pit waste, we
developed and evaluated four artificial neural network (ANN) models in this study, namely MLP-ANN, RBF-ANN, GRANN,
and PSO-ANN, to predict the removal efficiency of Fe(III) during the adsorption process. The purpose of these
models was to forecast the effect of the following five important operational variables: the initial concentration, time,
stirring speed, temperature, and biosorbent dose. We conducted a thorough assessment of the performance of these models
and compared their ability to predict the removal capacity. Several statistical metrics have been used to quantify the quality
of the different models. The results calculated by the machine learning models were analyzed and compared with the
experimental results. The obtained values of the coefficient of determination were 0.9997 for the PSO-ANN model, 0.991
for the GR-ANN model, 0.983 for the RBF-ANN model, and 0.837 for the MLP-ANN model concerning the removal
efficiency. All the studied models are able to accurately predict the adsorbed quantity. ANOVA analysis was used to
evaluate the effect of the parameters inherent to the PSO-ANN model. The PSO-ANN model proves to be a powerful tool
for estimating the efficiency of Fe(III) ion removal.