Journal
BIOTECHNOLOGY AND APPLIED BIOCHEMISTRY
Volume 69, Issue 3, Pages 1112-1120Publisher
WILEY
DOI: 10.1002/bab.2182
Keywords
alum; biodiesel; biomass retrieval; Chlorella vulgaris; flocculation; harvesting; neural network modeling; pH adjustment
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The study investigated the effect of alum flocculation on Chlorella vulgaris biomass retrieval, finding that the best flocculation efficiency could be achieved at the natural pH value of the growth medium with minimal flocculant addition. An ensemble architecture of neural networks was successfully employed for flocculation modeling in this research.
Microalgae, a group of photosynthetic microorganisms, are a promising feedstock for biodiesel production, but their biomass retrieval is a challenging task. Flocculation is a feasible method for dewatering and harvesting microalgae biomass. In the current study, the effect of alum flocculation on Chlorella vulgaris biomass retrieval has been studied. Alum structural changes with pH were led to a full factorial design to address the effect of this chemical structure changes at different pH values. It is observed that the best flocculation efficiency could be achieved in the natural pH value of C. vulgaris growth medium (8.2) with less than 0.5 g/L flocculant addition, which would lead to the flocculation efficiency of more than 90%. An ensemble architecture of neural networks successfully employed for flocculation modeling.
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