Phytoextraction capability of Azolla pinnata in the removal of rhodamine B from aqueous solution: artificial neural network and random forests approaches

Abstract

Abstract This study used live Azolla pinnata (AP) to remediate rhodamine B (RB) from aqueous solutions via the phytoextraction method, and machine learning algorithms such as artificial neural networks and random forests were used as predictive models. The pH was found to have a major influence on the phytoextraction process, and the AP dosage can change the pH of the aqueous solution. The optimum condition for the phytoextraction of RB (initial dye concentration at 10 ppm) is at pH 3.0 with a plant dosage of 0.4 g, resulting in removal efficiency as high as 76%. The growth estimation (relative frond number) indicates that AP can tolerate RB concentration of as high as 20 mg L−1, and the estimations from the pigment studies showed that the exposure of AP to RB causes AP to produce higher concentrations of plant pigments than the control, which hinted the possibility of AP using RB and its intermediates for growth.

Keywords

• Phytoextraction
• Rhodamine B
• Artificial neural network
• Random forests
• Machine learning algorithms
• Azolla pinnata