Modeling of solid-phase tea waste extraction for the removal of manganese and cobalt from water samples by using PSO-artificial neural network and response surface methodology

Abstract

The aim of this research was to develop a low price adsorbent with the abundant of source to remove manganese and cobalt from water samples. Tea waste solid-phase extraction coupled with flame atomic absorption spectrometry (FAAS) was used for the extraction and determination of manganese and cobalt ions. Response surface methodology (RSM) and hybrid of artificial neural network-particle swarm optimization (ANN-PSO) have been used to develop predictive models for simulation and optimization of tea waste extraction process. The pH, amount of tea waste, concentration of PAN (complexing agent), eluent volume, concentration of eluent, and sample and eluent flow rates were the input variables, while the extraction percent of Mn and Co were the output. Two approaches for their modeling and optimization capabilities were compared. The generalization and predictive capabilities of both RSM and ANN were compared by unseen data. The results have shown the superiority of ANN compared to RSM. Under the optimum conditions, the detection limits of Mn and Co were 0.5 and 0.67μgL−1, respectively. This method was applied to the preconcentration and determination of manganese and cobalt from water samples.