In the present work, spent tea leaves were modified with Ca(OH) 2 and used as a new, non-conventional and low-cost biosorbent for the removal of Cu(II) from aqueous solution. Response surface methodology (RSM) and artificial neural network (ANN) were used to develop predictive models for simulation and optimization of the biosorption process. The influence of process parameters (pH, biosorbent dose and reaction time) on the biosorption efficiency was investigated through a twolevel three-factor (2 3 ) full factorial central composite design with the help of Design Expert. The same design was also used to obtain a training set for ANN. Finally, both modeling methodologies were statistically compared by the root mean square error and absolute average deviation based on the validation data set. Results suggest that RSM has better prediction performance as compared to ANN. The biosorption followed Langmuir adsorption isotherm and it followed pseudo-second-order kinetic. The optimum removal efficiency of the adsorbent was found as 96.12 %.

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Bioremediation of a chromium complex dye was conducted by growing Aspergillus flavus and Aspergillus tamarii in a batch bioreactor to evaluate color and chromium removal. A. tamarii was found to be more efficient for bioremediation of this dye in the initial concentration range. The optimum conditions of parameters for color removal were determined using response surface methodology. The theoretical and experimental responses of color removal were in close agreement as suggested by the confirmatory experiments performed in batch and continuous modes under the optimum conditions. The bioremediation mechanism of the chromium complex dye was examined by gas chromatography‐mass spectrometry, scanning electron microscopy, transmission electron microscopy, and energy‐dispersive X‐ray spectroscopic analyses.

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