Predictive modelling and optimization of an airlift bioreactor for selenite removal from wastewater using artificial neural networks and particle swarm optimization.

Selenite (Se) is the most toxic of all the oxyanion forms of selenium. In this study, a feed forward back propagation (BP) based artificial neural network (ANN) model was developed for a fungal pelleted airlift bioreactor (ALR) system treating selenite-laden wastewater. The performance of the bioreactor, i.

Selenite removal from wastewater using fungal pelleted airlift bioreactor.

This study investigated the removal of selenite from wastewater using the fungus Asergillus niger KP isolated from a laboratory scale inverse fluidized bed bioreactor. The effect of different carbon sources and initial selenite concentration on fungal growth, pellet formation and selenite removal was first examined in a batch system. The fungal strain showed a maximum selenite removal efficiency of 86% in the batch system.