Combination of ACO-artificial neural network method for modeling of manganese and cobalt extraction onto nanometer SiO2 from water samples

• Cobalt is one of common poisonous elements, is present in waste water.
• The manganese can develop emotional and mental disturbances.
• A SiO2 nanoparticle was successfully applied to selective enrichment of analytes.
• Method was validated and applied to the analysis of water samples.
• Experimental factors were optimized to novel optimization method in chemistry (ACO).

In this study, modeling based on ant-colony optimization – artificial neural network have been employed to develop the model for simulation and optimization of nanometer SiO2 for the extraction of manganese and cobalt from water samples. The pH, time, amount of SiO2 nanoparticles and concentration of 1-(2-pyridylazo)-2-naphthol (PAN) were the input variables, while the extraction% of analytes was the output. Under the optimum conditions, the detection limits were 0.52 and 0.7 μg L−1, for manganese and cobalt, respectively. The method was applied to the extraction of manganese and cobalt from water samples and one certified reference material.

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