LAUSR

Microalgae, a group of photosynthetic microorganisms, are a promising feedstock for biodiesel production, but their biomass retrieval is challenging. Flocculation is the feasible method for dewatering and harvesting microalgae biomass. In the current study, the effect of alum flocculation for Chlorella vulgaris biomass retrieval have been studied. Alum structural changes with pH was led to a full factorial design to address the effect of this chemical structure changes in different pH values. It is observed the best flocculation efficiency could be achieved in natural pH value of Chlorella vulgaris growth medium (8.2) with less than 0.5 g/L flocculant addition, which would lead to flocculation efficiency of more than 90%. An ensemble architecture of neural networks successfully employed for flocculation modeling. Chlorella vulgaris has successfully being harvested with flocculation method with alum as flocculant. Due to alum chemical structure being dependent on pH medium, a full-factorial design was used to study the effective parameters on alum flocculation of Chlorella vulgaris. The best flocculation conditions were occurred at natural pH of Chlorella vulgaris culture medium (8.2) with less than 0.5 g/L flocculant addition, which would lead to flocculation efficiency of more than 90%. An ensemble neural network architecture was successfully applied for modeling of flocculation behavior. This would be helpful for prediction of flocculation efficiency in other conditions. This article is protected by copyright. All rights reserved.