Simulation and optimization of nanomaterials application for heavy metal removal from aqueous solutions

The artificial neural network (ANN) and symbiotic organisms search (SOS) optimization algorithm were used for simulation and optimization of heavy metals removal from aqueous solutions by two adsorbents, that is, Chitosan and Chitosan-Montmorillonite Nanocomposite (C. M. Nanocomposite). These adsorbents are utilized for removal of Cd, Al, Co, Cu, Fe, and Pb from aqueous solutions in batch mode under different adsorbent dosages, initial pH values, and contact times. The multi-layer perceptron ANN (MLPANN) and radial basis function ANN (ANNRBF) models hybridized with SOS algorithm (ANN-SOS) were used for modeling the removal efficiency of heavy metals and optimizing the three removal process variables. The predictive ability of the model in terms of heavy metals removal was verified by evaluating the performance of the ANNs. Then, the SOS algorithm was utilized to optimize the removal.