Biotreatment of a triphenylmethane dye solution using a Xanthophyta alga: modeling of key factors by neural network

TLDR: In this article, the effects of operational parameters such as initial dye concentration, temperature, pH and algal amount on biological decolorization efficiency were examined, and the results indicated that artificial neural network provided reasonable predictive performance (R 2 ǫ = 0.979).

Abstract: In this paper biotreatment of triphenylmethane dye, Malachite Green (MG), by a Xanthophyta alga, Vaucheria species, was investigated. The results obtained from batch experiments revealed the ability of Vaucheria sp. to remove MG. The effects of operational parameters such as initial dye concentration, temperature, pH and algal amount on biological decolorization efficiency were examined. The results showed that the biological decolorization efficiency decreased with increasing initial MG concentration. The decolorization rate also enhanced with increasing the temperature, initial pH of the dye solution and the amount of biomass rose. Biological treatment of MG solution by live and dead alga was compared. The reusability and efficiency of the live alga in long-term repetitive operations were also examined. The batch experiments results revealed the ability of algal species in biological degradation of the dye. An artificial neural network (ANN) model was developed to predict the biological decolorization of MG solution. The findings indicated that artificial neural network provided reasonable predictive performance ( R 2  = 0.979). The influence of each parameter on the variable studied was assessed, and reaction time and initial dye concentration were found to be the most significant factors, followed by initial pH, amount of alga and temperature.