Phytoextraction potential of water fern (Azolla pinnata) in the removal of a hazardous dye, methyl violet 2B: Artificial neural network modelling

ABSTRACT

This study investigated the potential of Azolla pinnata (AP) in the removal of toxic methyl violet 2B (MV) dye wastewater using the phytoextraction approach with the inclusion of an Artificial Neural Network (ANN) modelling. Parameters examined included the effects of dye concentration, pH and plant dosage. The highest removal efficiency was 93% which was achieved at a plant dosage of 0.8 g (dye volume = 200 mL, initial pH = 6.0, initial dye concentration = 10 mg L⁻¹). A significant decrease in relative frond number (RFN), a growth rate estimator, observed at a dye concentration of 20 mg L⁻¹ MV indicated some toxicity, which coincided with the plant pigments studies where the chlorophyll a content was lower than the control. There were little differences in the plant pigment contents between the control and those in the presence of dye (5 to 15 mg L⁻¹) indicating the tolerance of AP to MV at lower concentrations. A three-layer ANN model was optimized (6 neurons in the hidden layer) and successfully predicted the phytoextraction of MV (R = 0.9989, RMSE = 0.0098). In conclusion, AP proved to be a suitable plant that could be used for the phytoextraction of MV while the ANN modelling has shown to be a reliable method for the modelling of phytoextraction of MV using AP.

KEYWORDS:

• Methyl violet 2b
• phytoextraction
• water fern (Azolla pinnata)

Acknowledgement

Appreciation is given to the Government of Brunei Darussalam and Universiti Brunei Darussalam (UBD) for their offer of Graduate Research Studies scholarship. Appreciation also goes to the Applied Physics and the Environment and Life Sciences Sections of UBD for the usage of the SEM instrument. A special thanks is also given to Dr H.M Thippeswamy of the Department of Agriculture (Soil and Plant Nutrition unit), Ministry of Industrial and Primary Resource, Brunei Darussalam for the provision of the Azolla pinnata sample, and also to Associate Professor S.M.N. Arosha Senanayake for his advices on the ANN concept and software.

Conflict of interest

No potential conflict of interest was reported by the authors